Model Loading Measurements – Dr. Trippers Stuff

What measurements can we take while loading a model?
When loading a model, there are several measurements you can take to ensure the loading process is successful and to evaluate the performance of the loaded model. Here are some common measurements you can consider:

1. **Loading time**: Measure the time it takes to load the model from storage into memory. This can help you understand the efficiency of the loading process and identify any potential bottlenecks.

2. **Memory usage**: Monitor the memory usage before and after loading the model. This measurement allows you to assess the memory requirements of the model and ensure it fits within the available resources.

3. **Model size**: Determine the size of the model file on disk. This measurement provides insights into the storage requirements for the model and can be useful for managing disk space.

4. **Dependency compatibility**: Check the compatibility of the model’s dependencies with the current environment. Ensure that all the required libraries, frameworks, and versions are available and compatible to avoid any runtime errors or issues.

5. **Model integrity**: Perform integrity checks to ensure the loaded model file is not corrupted or modified. This can involve comparing checksums or using other verification techniques to validate the integrity of the model.

6. **Versioning**: Keep track of the model version being loaded. This helps in managing different versions of the model and enables you to track changes and improvements over time.

7. **Compatibility with the inference system**: Verify that the loaded model is compatible with the specific inference system or runtime environment you are using. Ensure that the required runtime libraries and configurations are in place for the model to work correctly.

8. **Functional testing**: After loading the model, perform some initial functional tests to ensure it is functioning as expected. This can involve feeding sample inputs and comparing the outputs with the expected results.

9. **Performance evaluation**: Measure the performance of the loaded model in terms of inference speed and resource utilization. Benchmark the model on a representative set of inputs to assess its efficiency and identify any performance issues.

By considering these measurements, you can effectively monitor the loading process, validate the integrity of the model, and evaluate its performance, ensuring a smooth and reliable deployment of the model.
I will be loading from transformers and would like to have a progress bar to show to downloading of the model and files Is that a measurement that can be taken?
Yes, displaying a progress bar while downloading the model and associated files is a useful feature that provides visibility into the progress of the loading process. Although it is not strictly a measurement, it enhances the user experience by indicating the status of the download and gives a sense of how much time is remaining.

Implementing a progress bar while loading models from the Hugging Face Transformers library is certainly possible. Here’s an example of how you can achieve this using the `tqdm` library in Python:

“`python
from tqdm import tqdm
from transformers import AutoTokenizer, AutoModel

model_name = “your_model_name” # Specify the name of the model you want to load

# Initialize the progress bar
with tqdm(total=1, unit=”model”) as pbar:
# Download and load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name, cache_dir=”./cache_dir”, local_files_only=True)
pbar.set_description(“Loading tokenizer”)

# Update the progress bar
pbar.update(0.5)

# Download and load the model
model = AutoModel.from_pretrained(model_name, cache_dir=”./cache_dir”, local_files_only=True)
pbar.set_description(“Loading model”)

# Update the progress bar
pbar.update(0.5)
“`

In this example, the `tqdm` progress bar is initialized with a total of 1 unit, representing the completion of the entire loading process. As the tokenizer and model are downloaded and loaded, the progress bar is updated accordingly, indicating the progress at each step.

Make sure to adjust the `model_name` variable to the specific model you want to load, and set the appropriate `cache_dir` if needed. Additionally, you may need to install the `tqdm` library if you haven’t done so already (`pip install tqdm`).

With this implementation, you can have a progress bar that visually represents the downloading and loading progress of the model and files from the Transformers library.
Ok. Then rewrite this code to use the streamlit interface to include a progress meter in the left column. from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch

model = “tiiuae/falcon-7b-instruct”

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
“text-generation”,
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map=”auto”,
)
sequences = pipeline(
“Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.nDaniel: Hello, Girafatron!nGirafatron:”,
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f”Result: {seq[‘generated_text’]}”)

To integrate the code with Streamlit and display a progress meter in the left column, you can use the `st.progress()` function provided by Streamlit. Here’s the modified code that incorporates the Streamlit interface and progress meter:

“`python
import streamlit as st
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = “tiiuae/falcon-7b-instruct”

# Set up Streamlit layout
st.title(“Text Generation with Falcon-7B-Instruct”)
progress_bar = st.progress(0)

# Load tokenizer and model
with st.spinner(“Loading tokenizer and model…”):
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model)

# Update progress bar
progress_bar.progress(0.5)

# Configure text generation pipeline
pipeline = transformers.pipeline(
“text-generation”,
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map=”auto”,
)

# Generate text
with st.spinner(“Generating text…”):
sequences = pipeline(
“Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.nDaniel: Hello, Girafatron!nGirafatron:”,
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)

# Update progress bar
progress_bar.progress(1.0)

# Display generated text
for seq in sequences:
st.subheader(“Generated Text:”)
st.write(seq[“generated_text”])
“`

In this code, I’ve added Streamlit’s `st.title()` function to set the page’s title. The `st.progress()` function creates a progress meter that will be updated at different stages of the code execution.

The loading of the tokenizer and model is wrapped in a `with st.spinner()` block to display a loading message while the process is ongoing.

Similarly, the text generation process is also wrapped in a `with st.spinner()` block to show a loading message.

Finally, the generated text is displayed using Streamlit’s `st.subheader()` and `st.write()` functions.

To run this code, make sure you have Streamlit installed (`pip install streamlit`) and save the code in a Python file (e.g., `app.py`). Then run `streamlit run app.py` from the command line, and it will start a local web server where you can view the Streamlit app with the progress meter and the generated text.
what is the command to create a requirements file from pip
To create a requirements file from the currently installed packages using pip, you can use the following command:

“`bash
pip freeze > requirements.txt
“`

This command saves the names and versions of all installed packages into a file named `requirements.txt`. Each line in the file represents a package in the format `package_name==version`. This requirements file can be used to recreate the same environment or share the list of dependencies with others.

Make sure to run this command in the same environment or virtual environment where your desired packages are installed. If you’re using a virtual environment, activate it before running the command to capture the packages specific to that environment.
streamlit run app.py

You can now view your Streamlit app in your browser.

Local URL: http://localhost:8501
Network URL: http://192.168.254.74:8501

Downloading (…)okenizer_config.json: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 220/220 [00:00<?, ?B/s]
Downloading (…)/main/tokenizer.json: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2.73M/2.73M [00:01<00:00, 2.54MB/s]
Downloading (…)cial_tokens_map.json: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 281/281 [00:00<?, ?B/s]
Downloading (…)lve/main/config.json: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 667/667 [00:00<00:00, 667kB/s]
2023-06-10 20:36:19.049 Uncaught app exception
Traceback (most recent call last):
File "G:Falcon.envlibsite-packagesstreamlitruntimescriptrunnerscript_runner.py", line 552, in _run_script
exec(code, module.__dict__)
File "G:Falconapp.py", line 15, in
model = AutoModelForCausalLM.from_pretrained(model)
File “G:Falcon.envlibsite-packagestransformersmodelsautoauto_factory.py”, line 456, in from_pretrained
config, kwargs = AutoConfig.from_pretrained(
File “G:Falcon.envlibsite-packagestransformersmodelsautoconfiguration_auto.py”, line 947, in from_pretrained
trust_remote_code = resolve_trust_remote_code(
File “G:Falcon.envlibsite-packagestransformersdynamic_module_utils.py”, line 535, in resolve_trust_remote_code
signal.signal(signal.SIGALRM, _raise_timeout_error)
AttributeError: module ‘signal’ has no attribute ‘SIGALRM’
The error message you encountered suggests that the `signal` module does not have an attribute named `SIGALRM`. This error can occur if you’re using a platform or environment where `SIGALRM` is not available or not supported.

One possible solution is to modify your code to remove the dependency on `SIGALRM`. To do this, you can explicitly set the `trust_remote_code` parameter to `False` when initializing the pipeline. Here’s the modified code:

“`python
import streamlit as st
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = “tiiuae/falcon-7b-instruct”

# Set up Streamlit layout
st.title(“Text Generation with Falcon-7B-Instruct”)
progress_bar = st.progress(0)

# Load tokenizer and model
with st.spinner(“Loading tokenizer and model…”):
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, trust_remote_code=False) # Set trust_remote_code to False

# Update progress bar
progress_bar.progress(0.5)

# Update progress bar
progress_bar.progress(1.0)

# Display generated text
for seq in sequences:
st.subheader(“Generated Text:”)
st.write(seq[“generated_text”])
“`

By setting `trust_remote_code` to `False`, you bypass the need for `SIGALRM` and resolve the attribute error. However, please note that setting `trust_remote_code` to `False` may introduce potential security risks if you’re using models from untrusted sources.

If the error persists or you’re still facing issues, please ensure that you have the latest versions of the `transformers` and `torch` libraries installed (`pip install transformers torch –upgrade`).

Additionally, make sure your Python environment is correctly set up and compatible with the `transformers` library and the model you are trying to load.
Lets change to loading icon with timer that show milliseconds
To display a loading icon with a timer that shows milliseconds in Streamlit, you can use the `st.spinner()` function along with the `time` module in Python. Here’s an example code that incorporates a loading icon and a timer:

“`python
import streamlit as st
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import time

model = “tiiuae/falcon-7b-instruct”

# Set up Streamlit layout
st.title(“Text Generation with Falcon-7B-Instruct”)

# Load tokenizer and model
with st.spinner(“Loading tokenizer and model…”):
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model)
st.success(“Tokenizer and model loaded successfully!”)

# Display loading icon with timer
with st.spinner(“Generating text…”):
start_time = time.time()

# Generate text
sequences = transformers.pipeline(
“text-generation”,
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=False,
device_map=”auto”,
)(
“Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.nDaniel: Hello, Girafatron!nGirafatron:”,
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)

elapsed_time = time.time() – start_time

# Display generated text
st.subheader(“Generated Text:”)
for seq in sequences:
st.write(seq[“generated_text”])

# Display elapsed time in milliseconds
st.subheader(“Elapsed Time:”)
st.write(f”{elapsed_time * 1000:.2f} milliseconds”)
“`

In this code, the `st.spinner()` function is used to display a loading icon while the tokenizer and model are being loaded, as well as during text generation. The `start_time` variable captures the timestamp before text generation, and the `elapsed_time` variable calculates the time difference after text generation.

The generated text is then displayed using `st.subheader()` and `st.write()` functions.

Lastly, the elapsed time is displayed in milliseconds using the `st.subheader()` and `st.write()` functions with formatting to two decimal places.

You can run this code using `streamlit run app.py` command, assuming you have Streamlit and the necessary dependencies installed. The Streamlit app will show the loading icon during the loading and generation process, and then display the generated text and elapsed time in milliseconds.
AttributeError: module ‘signal’ has no attribute ‘SIGALRM’
Traceback:
File “G:Falcon.envlibsite-packagesstreamlitruntimescriptrunnerscript_runner.py”, line 552, in _run_script
exec(code, module.__dict__)
File “G:Falconapp1.py”, line 15, in
model = AutoModelForCausalLM.from_pretrained(model)
File “G:Falcon.envlibsite-packagestransformersmodelsautoauto_factory.py”, line 456, in from_pretrained
config, kwargs = AutoConfig.from_pretrained(
File “G:Falcon.envlibsite-packagestransformersmodelsautoconfiguration_auto.py”, line 947, in from_pretrained
trust_remote_code = resolve_trust_remote_code(
File “G:Falcon.envlibsite-packagestransformersdynamic_module_utils.py”, line 535, in resolve_trust_remote_code
signal.signal(signal.SIGALRM, _raise_timeout_error)
I apologize for the inconvenience. It seems that the `SIGALRM` attribute is not available in the `signal` module in your current environment or platform.

As an alternative approach, you can try setting the `timeout` parameter of the `from_pretrained()` method to a small value (e.g., `timeout=1`) to avoid potential hangs during model loading. This will prevent the code from waiting indefinitely if there is an issue with the download process.

Here’s the updated code:

“`python
import streamlit as st
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = “tiiuae/falcon-7b-instruct”

# Set up Streamlit layout
st.title(“Text Generation with Falcon-7B-Instruct”)
progress_bar = st.progress(0)

# Update progress bar
progress_bar.progress(0.5)

# Update progress bar
progress_bar.progress(1.0)

# Display generated text
for seq in sequences:
st.subheader(“Generated Text:”)
st.write(seq[“generated_text”])
“`

Please give it a try and let me know if you still encounter any issues.
So this has produced a paragraph (200 tokens) but has no place to ask questions. I want the seed prompt removed.
So this has produced a paragraph (200 tokens) but has no place to ask questions. I want the seed prompt removed. And I want to send it a prompt.
If you want to remove the seed prompt and generate text directly without any specific initial context, you can modify the code as follows:

“`python
import streamlit as st
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = “tiiuae/falcon-7b-instruct”

# Set up Streamlit layout
st.title(“Text Generation with Falcon-7B-Instruct”)
progress_bar = st.progress(0)

# Update progress bar
progress_bar.progress(0.5)

# Generate text
with st.spinner(“Generating text…”):
sequences = pipeline(
“”, # Empty prompt to generate text without initial context
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)

# Update progress bar
progress_bar.progress(1.0)

# Display generated text
for seq in sequences:
st.subheader(“Generated Text:”)
st.write(seq[“generated_text”])
“`

In this updated code, the `pipeline()` method is called with an empty prompt (`””`) to generate text without any specific initial context. This allows the model to generate text freely based on its training.

Now, when you run the code, it will directly generate a paragraph of text without displaying any seed prompt.
Apologies for the confusion. If you want to remove the seed prompt and generate text without any specific input, you can modify the code to generate text without providing the initial prompt.

Here’s the updated code that removes the seed prompt:

“`python
import streamlit as st
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = “tiiuae/falcon-7b-instruct”

# Set up Streamlit layout
st.title(“Text Generation with Falcon-7B-Instruct”)
progress_bar = st.progress(0)

# Update progress bar
progress_bar.progress(0.5)

# Generate text
with st.spinner(“Generating text…”):
sequences = pipeline(
“”, # Empty string as input, no prompt
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)

# Update progress bar
progress_bar.progress(1.0)

# Display generated text
for seq in sequences:
st.subheader(“Generated Text:”)
st.write(seq[“generated_text”])
“`

In this updated code, the initial prompt provided to the pipeline method is an empty string (`””`), which means there is no specific input or seed prompt provided. The code will generate text without any pre-defined context.

Please note that generating text without a specific prompt may result in more random and unpredictable outputs. You can adjust the `max_length`, `top_k`, and other parameters in the `pipeline()` method to fine-tune the generated text according to your preferences.
Apologies for the confusion. To remove the seed prompt and allow users to input their own prompt, you can modify the code to use the `st.text_input()` function to get user input. Here’s the updated code:

“`python
import streamlit as st
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = “tiiuae/falcon-7b-instruct”

# Set up Streamlit layout
st.title(“Text Generation with Falcon-7B-Instruct”)
prompt = st.text_input(“Enter your prompt:”, value=””, max_chars=1000)
button = st.button(“Generate Text”)
generated_text = st.empty()

if button:
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model)

# Generate text
sequences = pipeline(
prompt,
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)

# Display generated text
generated_text.subheader(“Generated Text:”)
for seq in sequences:
generated_text.write(seq[“generated_text”])
“`

In this code, the `st.text_input()` function is used to create an input field where users can enter their prompt. The prompt is then passed as the input to the text generation pipeline.

The `st.button()` function creates a button that triggers the generation process when clicked. Once the button is clicked, the generated text is displayed using `st.subheader()` and `st.write()` functions.

Please note that the maximum number of characters for the prompt input field is set to 1000 in this example. You can adjust this value according to your requirements.

You can run the code using `streamlit run app.py` command, assuming you have Streamlit and the necessary dependencies installed. The Streamlit app will display an input field where you can enter your prompt, and a button to generate the text based on the given prompt. The generated text will be displayed below the button.
streamlit run app1.py

You can now view your Streamlit app in your browser.

Local URL: http://localhost:8501
Network URL: http://192.168.254.74:8501

Loading checkpoint shards: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:08<00:00, 4.27s/it]
Loading checkpoint shards: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:07<00:00, 3.82s/it]
WARNING[XFORMERS]: xFormers can't load C++/CUDA extensions. xFormers was built for:
PyTorch 2.0.1+cu118 with CUDA 1108 (you have 2.0.1+cpu)
Python 3.10.11 (you have 3.10.6)
Please reinstall xformers (see https://github.com/facebookresearch/xformers#installing-xformers)
Memory-efficient attention, SwiGLU, sparse and more won't be available.
Set XFORMERS_MORE_DETAILS=1 for more details
The model 'RWForCausalLM' is not supported for text-generation. Supported models are ['BartForCausalLM', 'BertLMHeadModel', 'BertGenerationDecoder', 'BigBirdForCausalLM', 'BigBirdPegasusForCausalLM', 'BioGptForCausalLM', 'BlenderbotForCausalLM', 'BlenderbotSmallForCausalLM', 'BloomForCausalLM', 'CamembertForCausalLM', 'CodeGenForCausalLM', 'CpmAntForCausalLM', 'CTRLLMHeadModel', 'Data2VecTextForCausalLM', 'ElectraForCausalLM', 'ErnieForCausalLM', 'GitForCausalLM', 'GPT2LMHeadModel', 'GPT2LMHeadModel', 'GPTBigCodeForCausalLM', 'GPTNeoForCausalLM', 'GPTNeoXForCausalLM', 'GPTNeoXJapaneseForCausalLM', 'GPTJForCausalLM', 'LlamaForCausalLM', 'MarianForCausalLM', 'MBartForCausalLM', 'MegaForCausalLM', 'MegatronBertForCausalLM', 'MvpForCausalLM', 'OpenLlamaForCausalLM', 'OpenAIGPTLMHeadModel', 'OPTForCausalLM', 'PegasusForCausalLM', 'PLBartForCausalLM', 'ProphetNetForCausalLM', 'QDQBertLMHeadModel', 'ReformerModelWithLMHead', 'RemBertForCausalLM', 'RobertaForCausalLM', 'RobertaPreLayerNormForCausalLM', 'RoCBertForCausalLM', 'RoFormerForCausalLM', 'RwkvForCausalLM', 'Speech2Text2ForCausalLM', 'TransfoXLLMHeadModel', 'TrOCRForCausalLM', 'XGLMForCausalLM', 'XLMWithLMHeadModel', 'XLMProphetNetForCausalLM', 'XLMRobertaForCausalLM', 'XLMRobertaXLForCausalLM', 'XLNetLMHeadModel', 'XmodForCausalLM'].
G:Falcon.envlibsite-packagestransformersgenerationutils.py:1259: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation)
warnings.warn(
Setting `pad_token_id` to `eos_token_id`:11 for open-end generation.
Loading checkpoint shards: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:10<00:00, 5.36s/it]
The model 'RWForCausalLM' is not supported for text-generation. Supported models are ['BartForCausalLM', 'BertLMHeadModel', 'BertGenerationDecoder', 'BigBirdForCausalLM', 'BigBirdPegasusForCausalLM', 'BioGptForCausalLM', 'BlenderbotForCausalLM', 'BlenderbotSmallForCausalLM', 'BloomForCausalLM', 'CamembertForCausalLM', 'CodeGenForCausalLM', 'CpmAntForCausalLM', 'CTRLLMHeadModel', 'Data2VecTextForCausalLM', 'ElectraForCausalLM', 'ErnieForCausalLM', 'GitForCausalLM', 'GPT2LMHeadModel', 'GPT2LMHeadModel', 'GPTBigCodeForCausalLM', 'GPTNeoForCausalLM', 'GPTNeoXForCausalLM', 'GPTNeoXJapaneseForCausalLM', 'GPTJForCausalLM', 'LlamaForCausalLM', 'MarianForCausalLM', 'MBartForCausalLM', 'MegaForCausalLM', 'MegatronBertForCausalLM', 'MvpForCausalLM', 'OpenLlamaForCausalLM', 'OpenAIGPTLMHeadModel', 'OPTForCausalLM', 'PegasusForCausalLM', 'PLBartForCausalLM', 'ProphetNetForCausalLM', 'QDQBertLMHeadModel', 'ReformerModelWithLMHead', 'RemBertForCausalLM', 'RobertaForCausalLM', 'RobertaPreLayerNormForCausalLM', 'RoCBertForCausalLM', 'RoFormerForCausalLM', 'RwkvForCausalLM', 'Speech2Text2ForCausalLM', 'TransfoXLLMHeadModel', 'TrOCRForCausalLM', 'XGLMForCausalLM', 'XLMWithLMHeadModel', 'XLMProphetNetForCausalLM', 'XLMRobertaForCausalLM', 'XLMRobertaXLForCausalLM', 'XLNetLMHeadModel', 'XmodForCausalLM'].
Setting `pad_token_id` to `eos_token_id`:11 for open-end generation.
streamlit run app1.py

You can now view your Streamlit app in your browser.

Local URL: http://localhost:8501
Network URL: http://192.168.254.74:8501

2023-06-11 08:42:01.868 Uncaught app exception
Traceback (most recent call last):
File "G:Falcon.envlibsite-packagesstreamlitruntimescriptrunnerscript_runner.py", line 552, in _run_script
exec(code, module.__dict__)
File "G:Falconapp1.py", line 17, in
model = AutoModelForCausalLM.from_pretrained(model)
File “G:Falcon.envlibsite-packagestransformersmodelsautoauto_factory.py”, line 456, in from_pretrained
config, kwargs = AutoConfig.from_pretrained(
File “G:Falcon.envlibsite-packagestransformersmodelsautoconfiguration_auto.py”, line 947, in from_pretrained
trust_remote_code = resolve_trust_remote_code(
File “G:Falcon.envlibsite-packagestransformersdynamic_module_utils.py”, line 535, in resolve_trust_remote_code
signal.signal(signal.SIGALRM, _raise_timeout_error)
AttributeError: module ‘signal’ has no attribute ‘SIGALRM’

It seems that there are issues with the model you’re trying to load (`RWForCausalLM`) as it is not supported for text generation. The supported models for text generation are listed in the error message.

Please ensure that you are using a valid model name that is supported for text generation. You can refer to the Hugging Face model repository (https://huggingface.co/models) to find the available models and their names.

If you want to use the “falcon-7b-instruct” model, you can update the `model` variable in your code with the correct model name:

“`python
model = “tiiuae/falcon-7b-instruct”
“`

Make sure that the model name is correct and corresponds to an available model for text generation.
Apologies for the inconvenience. It seems that the `SIGALRM` attribute is not available in the `signal` module in your current environment or platform, which is causing the error.

As an alternative solution, you can try using the `transformers` library with the `trust_remote_code` flag set to `True` to bypass the signal handling related to timeouts. Here’s the updated code:

“`python
import streamlit as st
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = “tiiuae/falcon-7b-instruct”

if button:
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(model, trust_remote_code=True)

# Generate text
sequences = pipeline(
prompt,
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)

# Display generated text
generated_text.subheader(“Generated Text:”)
for seq in sequences:
generated_text.write(seq[“generated_text”])
“`

Please give it a try and let me know if you encounter any further issues.
AttributeError: module ‘signal’ has no attribute ‘SIGALRM’
Traceback:
File “G:Falcon.envlibsite-packagesstreamlitruntimescriptrunnerscript_runner.py”, line 552, in _run_script
exec(code, module.__dict__)
File “G:Falconapp1.py”, line 17, in
model = AutoModelForCausalLM.from_pretrained(model)
File “G:Falcon.envlibsite-packagestransformersmodelsautoauto_factory.py”, line 456, in from_pretrained
config, kwargs = AutoConfig.from_pretrained(
File “G:Falcon.envlibsite-packagestransformersmodelsautoconfiguration_auto.py”, line 947, in from_pretrained
trust_remote_code = resolve_trust_remote_code(
File “G:Falcon.envlibsite-packagestransformersdynamic_module_utils.py”, line 535, in resolve_trust_remote_code
signal.signal(signal.SIGALRM, _raise_timeout_error)
I apologize for the ongoing issue. It appears that the `signal` module’s `SIGALRM` attribute is not available in your environment, causing the error.

In this case, you can try a workaround by installing the `timeout-decorator` package and using it to handle the timeout for model loading. Here’s an updated version of the code:

“`python
import streamlit as st
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
from timeout_decorator import timeout

model = “tiiuae/falcon-7b-instruct”

@timeout(30) # Timeout value in seconds (adjust as needed)
def load_model():
return AutoModelForCausalLM.from_pretrained(model, trust_remote_code=True)

if button:
try:
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model)
model = load_model()

# Generate text
sequences = pipeline(
prompt,
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)

# Display generated text
generated_text.subheader(“Generated Text:”)
for seq in sequences:
generated_text.write(seq[“generated_text”])

except TimeoutError:
st.error(“Timeout occurred while loading the model. Please try again.”)
except Exception as e:
st.error(f”An error occurred: {e}”)
“`

In this code, the `timeout-decorator` package is used to set a timeout of 30 seconds (you can adjust this value if needed) for the model loading process. If the timeout is reached, a `TimeoutError` will be raised, and an error message will be displayed in the Streamlit app.

Please make sure to install the `timeout-decorator` package by running `pip install timeout-decorator` before running the updated code.

Give this code a try, and it should handle the timeout issue during model loading. Let me know if you have any further questions or concerns.
ModuleNotFoundError: No module named ‘timeout_decorator’
Traceback:
File “G:Falcon.envlibsite-packagesstreamlitruntimescriptrunnerscript_runner.py”, line 552, in _run_script
exec(code, module.__dict__)
File “G:Falconapp1.py”, line 5, in
from timeout_decorator import timeout
I apologize for the inconvenience. It seems that the `timeout-decorator` package is not available in your environment.

As an alternative solution, you can use the `threading` module to implement a timeout for the model loading process. Here’s an updated version of the code without relying on the `timeout-decorator` package:

“`python
import streamlit as st
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import threading

model = “tiiuae/falcon-7b-instruct”

def load_model():
global model
model = AutoModelForCausalLM.from_pretrained(model, trust_remote_code=True)

def generate_text():
try:
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model)

# Generate text
sequences = pipeline(
prompt,
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)

# Display generated text
generated_text.subheader(“Generated Text:”)
for seq in sequences:
generated_text.write(seq[“generated_text”])

except Exception as e:
st.error(f”An error occurred: {e}”)

if button:
load_thread = threading.Thread(target=load_model)
load_thread.start()
load_thread.join(timeout=30) # Timeout value in seconds (adjust as needed)

if load_thread.is_alive():
st.error(“Timeout occurred while loading the model. Please try again.”)
else:
generate_text()
“`

In this code, we use the `threading` module to run the model loading process in a separate thread. The `load_model()` function loads the model using `AutoModelForCausalLM.from_pretrained()`, and the `generate_text()` function generates the text using the loaded model.

The main part of the code starts the `load_model()` function in a separate thread. We then use the `join()` method to wait for the thread to finish, with a timeout of 30 seconds. If the thread doesn’t finish within the specified timeout, an error message is displayed. Otherwise, the `generate_text()` function is called to generate and display the text.

Please give this updated code a try, and let me know if you encounter any further issues.
g:/forestOfThoughts/langchain_polyculture.py
Traceback (most recent call last):
File “g:forestOfThoughtslangchain_polyculture.py”, line 10, in
os.environ[“HUGGINGFACEHUB_API_TOKEN”] = hf_api_key
File “C:UsersSarahAppDataLocalProgramsPythonPython310libos.py”, line 684, in __setitem__
value = self.encodevalue(value)
File “C:UsersSarahAppDataLocalProgramsPythonPython310libos.py”, line 742, in check_str
raise TypeError(“str expected, not %s” % type(value).__name__)
TypeError: str expected, not NoneType

The error you encountered suggests that the value you provided for the `hf_api_key` variable is `None`. The `os.environ` dictionary expects a string value for setting environment variables.

To resolve this issue, make sure you provide a valid API token for the Hugging Face API. Here’s an example code snippet that sets the environment variable using an API token:

“`python
import os

# Replace `YOUR_HUGGINGFACE_API_TOKEN` with your actual API token
hf_api_key = “YOUR_HUGGINGFACE_API_TOKEN”

# Set the environment variable
os.environ[“HUGGINGFACEHUB_API_TOKEN”] = hf_api_key

# Rest of your code…
“`

Make sure you replace `”YOUR_HUGGINGFACE_API_TOKEN”` with your actual API token. If you don’t have an API token, you can obtain one from the Hugging Face website (https://huggingface.co/). Once you have the API token, replace the placeholder with the actual token in the code.

By setting the `HUGGINGFACEHUB_API_TOKEN` environment variable, you will be able to access models from the Hugging Face Model Hub.

Let me know if you need further assistance!
OPENAI_API_KEY = “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”
HUGGINGFACE_API_TOKEN = “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”
Please note that it is not recommended to share your API keys publicly as they can be misused by others. I would advise you to revoke the API keys you shared in the previous message and generate new ones.

If you have generated new API keys, you can set them as environment variables in your code as follows:

“`python
import os

# Set your OpenAI API key
os.environ[“OPENAI_API_KEY”] = “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”

# Set your Hugging Face API token
os.environ[“HUGGINGFACE_API_TOKEN”] = “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”

# Rest of your code…
“`

Make sure to replace the placeholder strings `”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″` and `”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”` with your actual API keys.

Remember to keep your API keys secure and avoid sharing them publicly. If you have accidentally shared your API keys, it’s a good practice to revoke them and generate new ones to maintain the security of your accounts.

Let me know if you have any further questions!
how should it be inside of .env file
To store your API keys in a `.env` file, you can follow these steps:

1. Create a file named `.env` in the same directory as your Python script.
2. Open the `.env` file in a text editor.
3. Add the following lines to the `.env` file, replacing the placeholder values with your actual API keys:

“`
OPENAI_API_KEY=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
HUGGINGFACE_API_TOKEN=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
“`

4. Save the `.env` file.

In your Python script, you can then use the `python-dotenv` library to load the environment variables from the `.env` file. Here’s an example:

“`python
import os
from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()

# Access the API keys
openai_api_key = os.getenv(“OPENAI_API_KEY”)
huggingface_api_token = os.getenv(“HUGGINGFACE_API_TOKEN”)

# Rest of your code…
“`

Make sure you have the `python-dotenv` library installed in your environment. You can install it using `pip`:

“`
pip install python-dotenv
“`

With this setup, the `os.getenv()` function retrieves the values of the API keys from the environment variables defined in the `.env` file.

Remember to keep your `.env` file secure and avoid sharing it publicly, as it contains sensitive information.
Convert this code to a streamlit application. I want five sections with a header that with model info.
The footer should show RAM, CPU and VRAM usage meters evenly spaced across the footer at 1/5 the screen height. The left column should show the thoughts processes or intermediary outputs of each of the agents. Add a random image from a cdn in the right column. from dotenv import load_dotenv
import os
from langchain import PromptTemplate, LLMChain
from langchain.llms import OpenAI
from langchain import HuggingFaceHub

load_dotenv()
openai_api_key = os.getenv(“OPENAI_API_KEY”)
hf_api_key = os.getenv(“HUGGINGFACE_API_TOKEN”)
#os.environ[“HUGGINGFACEHUB_API_TOKEN”] = hf_api_key

repo_id1 = “gpt2”
repo_id2 = “facebook/opt-350m”

llm_1 = OpenAI(temperature=0.7, max_tokens=3000)
llm_2 = HuggingFaceHub(repo_id=repo_id1, model_kwargs={“temperature”:0.1, “max_length”:500})
llm_3 = HuggingFaceHub(repo_id=repo_id2, model_kwargs={“temperature”:0.1, “max_length”:500})

template_1 = “””Imagine three different experts are answering this question.
They will brainstorm the answer step by step reasoning carefully and taking all facts into consideration
All experts will write down 1 step of their thinking,
then share it with the group.
They will each critique their response, and the all the responses of others
They will check their answer based on science and the laws of physics
Then all experts will go on to the next step and write down this step of their thinking.
They will keep going through steps until they reach their conclusion taking into account the thoughts of the other experts
If at any time they realise that there is a flaw in their logic they will backtrack to where that flaw occurred
If any expert realises they’re wrong at any point then they acknowledges this and start another train of thought
Each expert will assign a likelihood of their current assertion being correct
Continue until the experts agree on the single most likely location
The question is {question}

The experts reasoning is…
“””

template_2 = “””Imagine six different experts are answering this question.
They will brainstorm the answer step by step reasoning carefully and taking all facts into consideration
Each expert will share their first step with all of the other experts
They will each critique their response, and the all the responses of others
They will check their answer being careful to think through any consequences
Each expert will then write down the next step of their thought process
Each expert will assign a likelihood of their current assertion being correct
Continue until the experts agree on the single most likely location
The question is {question}

The experts reasoning is…
“””

template_3 = “””Imagine four different experts are answering this question.
They will first write down all the facts
They will then consider three different alternative answers and communicate these answers to the other experts
they will write down the likelihood of each answer
Based on this they will each come up with a single answer
The question is {question}

The experts reasoning is…
“””

question = “””1.What is the best season for gold pannoing in California? “””

prompt1 = PromptTemplate(template=template_1, input_variables=[“question”])
prompt2 = PromptTemplate(template=template_2, input_variables=[“question”])
prompt3 = PromptTemplate(template=template_3, input_variables=[“question”])

llm_chain_1 = LLMChain(prompt=prompt1, llm=llm_1)
llm_chain_2 = LLMChain(prompt=prompt2, llm=llm_2)
llm_chain_3 = LLMChain(prompt=prompt3, llm=llm_3)

response_1 = llm_chain_1.run(question)
response_2 = llm_chain_2.run(question)
response_3 = llm_chain_3.run(question)

print(response_1)
print(response_2)
print(response_3)

get_together = “””Several experts have been asked this question
What is the best season for gold panning in California?
Their reasoning is…
{response_1}
{response_2}
{response_3}
Where is the single most likely location of the watch? their resulting answers are {answer}
which answer is most likely?
The most likely answer is…”””

answer = response_1+response_2+response_3
fusion = PromptTemplate(template=get_together, input_variables=[“answer”])

wood_for_trees = OpenAI(temperature=0.1, max_tokens=3000)
final = LLMChain(prompt=fusion, llm=wood_for_trees)
conclusion = final.run(answer)

print(f”Conclusion is {conclusion}”)

Convert this code to a streamlit application. I want five sections with a header that with model info.
The footer should show RAM, CPU and VRAM usage meters evenly spaced across the footer at 1/5 the screen height. The left column should show the thoughts processes or intermediary outputs of each of the agents. Show the prompt in the right column. The center column will have a user imput text area that will prompt the LLM. The code should reflect that the prompt is the supplied by the user. Reconfigure the code to wait for the prompt to be submitted. Add default prompt “Please tell us what task to work on…”. Default button is inactive until user supplies a prompt.
When the prompt is submitted the center column changes to a response column and the right column is then populated by the user input prompt.
A stop processing button will stop the process.

from dotenv import load_dotenv
import os
from langchain import PromptTemplate, LLMChain
from langchain.llms import OpenAI
from langchain import HuggingFaceHub

load_dotenv()
openai_api_key = os.getenv(“OPENAI_API_KEY”)
hf_api_key = os.getenv(“HUGGINGFACE_API_TOKEN”)
#os.environ[“HUGGINGFACEHUB_API_TOKEN”] = hf_api_key

repo_id1 = “gpt2”
repo_id2 = “facebook/opt-350m”

The experts reasoning is…
“””

question = “””1.What is the best season for gold pannoing in California? “””

llm_chain_1 = LLMChain(prompt=prompt1, llm=llm_1)
llm_chain_2 = LLMChain(prompt=prompt2, llm=llm_2)
llm_chain_3 = LLMChain(prompt=prompt3, llm=llm_3)

response_1 = llm_chain_1.run(question)
response_2 = llm_chain_2.run(question)
response_3 = llm_chain_3.run(question)

print(response_1)
print(response_2)
print(response_3)

answer = response_1+response_2+response_3
fusion = PromptTemplate(template=get_together, input_variables=[“answer”])

wood_for_trees = OpenAI(temperature=0.1, max_tokens=3000)
final = LLMChain(prompt=fusion, llm=wood_for_trees)
conclusion = final.run(answer)

print(f”Conclusion is {conclusion}”)

from dotenv import load_dotenv
import os
from langchain import PromptTemplate, LLMChain
from langchain.llms import OpenAI
from langchain import HuggingFaceHub

load_dotenv()
openai_api_key = os.getenv(“OPENAI_API_KEY”)
hf_api_key = os.getenv(“HUGGINGFACE_API_TOKEN”)
#os.environ[“HUGGINGFACEHUB_API_TOKEN”] = hf_api_key

repo_id1 = “gpt2”
repo_id2 = “facebook/opt-350m”

The experts reasoning is…
“””

question = “{question}”

llm_chain_1 = LLMChain(prompt=prompt1, llm=llm_1)
llm_chain_2 = LLMChain(prompt=prompt2, llm=llm_2)
llm_chain_3 = LLMChain(prompt=prompt3, llm=llm_3)

response_1 = llm_chain_1.run(question)
response_2 = llm_chain_2.run(question)
response_3 = llm_chain_3.run(question)

print(response_1)
print(response_2)
print(response_3)

get_together = “””Several experts have been asked this””” “{question}”

“””And there are three different answers
{response_1}
{response_2}
{response_3}
Where is the single most likely location of the watch? their resulting answers are {answer}
which answer is most likely?
The most likely answer is…”””

answer = response_1+response_2+response_3
fusion = PromptTemplate(template=get_together, input_variables=[“answer”])

wood_for_trees = OpenAI(temperature=0.1, max_tokens=3000)
final = LLMChain(prompt=fusion, llm=wood_for_trees)
conclusion = final.run(answer)

print(f”Conclusion is {conclusion}”)

To convert the provided code into a Streamlit application with the requested layout, you can use the following code:

“`python
import streamlit as st
from dotenv import load_dotenv
import os
from langchain import PromptTemplate, LLMChain
from langchain.llms import OpenAI
from langchain import HuggingFaceHub

# Load environment variables from .env file
load_dotenv()

# Access the API keys
openai_api_key = os.getenv(“OPENAI_API_KEY”)
hf_api_key = os.getenv(“HUGGINGFACE_API_TOKEN”)

repo_id1 = “gpt2”
repo_id2 = “facebook/opt-350m”

llm_1 = OpenAI(temperature=0.7, max_tokens=3000)
llm_2 = HuggingFaceHub(repo_id=repo_id1, model_kwargs={“temperature”: 0.1, “max_length”: 500})
llm_3 = HuggingFaceHub(repo_id=repo_id2, model_kwargs={“temperature”: 0.1, “max_length”: 500})

template_1 = “””Imagine three different experts are answering this question.
They will brainstorm the answer step by step
To convert the code into a Streamlit application, follow these steps:

1. Install the necessary libraries by running the following command in your terminal:
“`
pip install streamlit langchain python-dotenv
“`

2. Create a file named `app.py` and open it in a text editor.

3. Copy and paste the code below into `app.py`:

“`python
from dotenv import load_dotenv
import os
import streamlit as st
from langchain import PromptTemplate, LLMChain
from langchain.llms import OpenAI
from langchain import HuggingFaceHub

load_dotenv()
openai_api_key = os.getenv(“OPENAI_API_KEY”)
hf_api_key = os.getenv(“HUGGINGFACE_API_TOKEN”)

repo_id1 = “gpt2”
repo_id2 = “facebook/opt-350m”

llm_1 = OpenAI(temperature=0.7, max_tokens=3000)
llm_2 = HuggingFaceHub(repo_id=repo_id1, model_kwargs={“temperature”: 0.1, “max_length”: 500})
llm_3 = HuggingFaceHub(repo_id=repo_id2, model_kwargs={“temperature”: 0.1, “max_length”: 500})

The experts reasoning is…
“””

question = “””1.What is the best season for gold pannoing in California? “””

llm_chain_1 = LLMChain(prompt=prompt1, llm=llm_1)
llm_chain_2 = LLMChain(prompt=prompt2, llm=
To convert the provided code into a Streamlit application with the desired layout and functionality, you can use the following code:

“`python
import streamlit as st
from dotenv import load_dotenv
import os
from langchain import PromptTemplate, LLMChain
from langchain.llms import OpenAI
from langchain import HuggingFaceHub

# Load environment variables from .env file
load_dotenv()

# Access the API keys
openai_api_key = os.getenv(“OPENAI_API_KEY”)
hf_api_key = os.getenv(“HUGGINGFACE_API_TOKEN”)

# Model initialization
repo_id1 = “gpt2”
repo_id2 = “facebook/opt-350m”

llm_1 = OpenAI(temperature=0.7, max_tokens=3000)
llm_2 = HuggingFaceHub(repo_id=repo_id1, model_kwargs={“temperature”: 0.1, “max_length”: 500})
llm_3 = HuggingFaceHub(repo_id=repo_id2, model_kwargs={“temperature”: 0.1, “max_length”: 500})

# Templates and chains
template_1 = “””Imagine three different experts are answering this question.
They will brainstorm the answer step by step reasoning carefully and taking all facts into consideration
All experts will write down 1 step of their thinking,
then share it with the group.
They will each critique their response, and the all the responses of others
They will check their answer based on science and the laws of physics
Then all experts will go on to the next step and write down this step of their thinking.
They will keep going through steps until they reach their conclusion taking into account the thoughts of the other experts
If at any time they realise that there is a flaw in their logic they will backtrack to where that flaw occurred
If any expert realises they’re wrong at any point then they acknowledges this and start another train of thought
Each expert will assign a likelihood of their current assertion being correct
Continue until the experts agree on the single most likely location
The question is {question}

The experts reasoning is…
“””

question = “Please tell us what task to work on…”

llm_chain_1 = LLMChain(prompt=prompt1, llm=llm_1)
llm_chain_2 = LLMChain(prompt=prompt2, llm=llm_2)
llm_chain_3 = LLMChain(prompt=prompt3, llm=llm_3)

response_1 = llm_chain_
llm_2)
llm_chain_3 = LLMChain(prompt=prompt3, llm=llm_3)

response_1 = llm_chain_1.run(question)
response_2 = llm_chain_2.run(question)
response_3 = llm_chain_3.run(question)

answer = response_1 + response_2 + response_3
fusion = PromptTemplate(template=get_together, input_variables=[“answer”])

wood_for_trees = OpenAI(temperature=0.1, max_tokens=3000)
final = LLMChain(prompt=fusion, llm=wood_for_trees)
conclusion = final.run(answer)

# Streamlit app code
st.set_page_config(layout=”wide”)

# Header
st.header(“Model Info”)
st.write(“Add model information here.”)

# Sidebar
st.sidebar.title(“Thought Processes”)

# Center column
st.title(“Prompt Input”)
prompt_input = st.text_area(“Enter your prompt here”, “Please tell us what task to work on…”)

# Right column
st.title(“User Prompt”)
st.write(prompt_input)

# Footer
st.markdown(“—“)
st.footer(“Add RAM, CPU, and VRAM usage meters here.”)

“`

4. Run the Streamlit application by executing the following command in your terminal:
“`
streamlit run app.py
“`

The application will launch in your default web browser, displaying the Streamlit interface with the specified layout and functionality.
1.run(question)
response_2 = llm_chain_2.run(question)
response_3 = llm_chain_3.run(question)

get_together = “””Several experts have been asked this””” “{question}”

answer = response_1 + response_2 + response_3
fusion = PromptTemplate(template=get_together, input_variables=[“answer”])

wood_for_trees = OpenAI(temperature=0.1, max_tokens=3000)
final = LLMChain(prompt=fusion, llm=wood_for_trees)
conclusion = final.run(answer)

# Streamlit app
st.set_page_config(layout=”wide”)

# Header
st.header(“Model Information”)
st.write(“Add information about the model here.”)

# Footer
st.markdown(“—“)
st.write(“RAM Usage:”)
st.progress(0.5) # Placeholder value, update with actual RAM usage
st.write(“CPU Usage:”)
st.progress(0.7) # Placeholder value, update with actual CPU usage
st.write(“VRAM Usage:”)
st.progress(0.3) # Placeholder value, update with actual VRAM usage

# Columns
left_column, center_column, right_column = st.beta_columns(3)

# Left column (thought processes or intermediary outputs)
left_column.header(“Thought Processes / Intermediary Outputs”)
left_column.write(“Thought process 1”)
left_column.write(“Thought process 2”)
left_column.write(“Thought process 3”)

# Center column (user input text area)
with center_column:
st.header(“User Input”)
prompt_input = st.text_area(“Enter your prompt here:”, value=question)

# Check if prompt is provided and enable default button
if prompt_input:
default_button_state = False
else:
default_button_state = True

default_button = st.button(“Use Default Prompt”, key=”default_button”, disabled=default_button_state)

# Check if prompt is submitted
if st.button(“Submit”):
# Change to response column
center_column.empty()
right_column.empty()
center_column.header(“Response”)
right_column.write(“User Prompt:”)
right_column.write(prompt_input)

# Perform processing
# … Code for processing the prompt and generating response …

# Display the response
center_column.write(“Response:”)
center_column.write(“Response content goes here”)

# Right column (user input prompt)
right_column.header(“User Input Prompt”)
right_column.write(“Prompt content goes here”)
“`

Please note that you need to install the required dependencies by running `pip install streamlit` and `pip install langchain` in your environment. Also, make sure to modify the code according to your specific needs, such as adding the actual RAM, CPU, and VRAM usage values and customizing the model information and processing logic.
streamlit run app.py

You can now view your Streamlit app in your browser.

Local URL: http://localhost:8501
Network URL: http://192.168.254.74:8501

2023-06-12 13:41:05.626 Uncaught app exception
Traceback (most recent call last):
File “G:forestOfThoughts.envlibsite-packagesstreamlitruntimescriptrunnerscript_runner.py”, line 565, in _run_script
exec(code, module.__dict__)
File “G:forestOfThoughtsapp.py”, line 84, in
fusion = PromptTemplate(template=get_together, input_variables=[“answer”])
File “pydanticmain.py”, line 341, in pydantic.main.BaseModel.__init__
pydantic.error_wrappers.ValidationError: 1 validation error for PromptTemplate
__root__
Invalid prompt schema; check for mismatched or missing input parameters. ‘response_1’ (type=value_error)
continue the code
Apologies for the mistake. It seems that there is an issue with the input parameters in the `PromptTemplate` of the `fusion` variable. Instead of directly passing the response variables (`response_1`, `response_2`, `response_3`) as input variables, we need to pass them as values in the `input_variables` list.

To fix the issue, please modify the code as follows:

“`python
# …

response_1 = llm_chain_1.run(question)
response_2 = llm_chain_2.run(question)
response_3 = llm_chain_3.run(question)

answer = response_1 + response_2 + response_3
fusion = PromptTemplate(template=get_together, input_variables=[“response_1”, “response_2”, “response_3”, “answer”])

# …
“`

After making this change, run the Streamlit application again with the following command:

“`
streamlit run app.py
“`

Now, the application should start successfully without any validation errors.
Certainly! Here’s the continuation of the code:

“`python
# … (Previous code)

# Streamlit app
st.set_page_config(layout=”wide”)

# Header
st.header(“Model Information”)
st.write(“Add information about the model here.”)

# Columns
left_column, center_column, right_column = st.beta_columns(3)

# Center column (user input text area)
with center_column:
st.header(“User Input”)
prompt_input = st.text_area(“Enter your prompt here:”, value=question)

# Check if prompt is provided and enable default button
if prompt_input:
default_button_state = False
else:
default_button_state = True

default_button = st.button(“Use Default Prompt”, key=”default_button”, disabled=default_button_state)

# Perform processing
# … Code for processing the prompt and generating response …

# Display the response
center_column.write(“Response:”)
center_column.write(“Response content goes here”)

# Right column (user input prompt)
right_column.header(“User Input Prompt”)
right_column.write(“Prompt content goes here”)

# Stop processing button
if st.button(“Stop Processing”):
# … Code to stop the processing …
pass

# … (Rest of the code)
“`

You can further customize the code to include the actual processing logic using the langchain library, such as initializing the models, creating prompt templates, running the LLM chains, and displaying the results. Additionally, you can handle the “Stop Processing” button by adding the relevant code to stop the ongoing processing task.
http://localhost:8501
Network URL: http://192.168.254.74:8501

2023-06-12 13:43:47.619 Uncaught app exception
Traceback (most recent call last):
File “G:forestOfThoughts.envlibsite-packagesstreamlitruntimescriptrunnerscript_runner.py”, line 565, in _run_script
exec(code, module.__dict__)
File “app.py”, line 89, in
conclusion = final.run(answer)
File “G:forestOfThoughts.envlibsite-packageslangchainchainsbase.py”, line 236, in run
return self(args[0], callbacks=callbacks)[self.output_keys[0]]
File “G:forestOfThoughts.envlibsite-packageslangchainchainsbase.py”, line 123, in __call__
inputs = self.prep_inputs(inputs)
File “G:forestOfThoughts.envlibsite-packageslangchainchainsbase.py”, line 206, in prep_inputs
raise ValueError(
ValueError: A single string input was passed in, but this chain expects multiple inputs ({‘response_3’, ‘response_1’, ‘response_2’, ‘answer’}). When a chain expects multiple inputs, please call it by passing in a dictionary, eg `chain({‘foo’: 1, ‘bar’: 2})`
2023-06-12 13:43:49.615 Uncaught app exception
Traceback (most recent call last):
File “G:forestOfThoughts.envlibsite-packagesstreamlitruntimescriptrunnerscript_runner.py”, line 565, in _run_script
exec(code, module.__dict__)
File “G:forestOfThoughtsapp.py”, line 89, in
conclusion = final.run(answer)
File “G:forestOfThoughts.envlibsite-packageslangchainchainsbase.py”, line 236, in run
return self(args[0], callbacks=callbacks)[self.output_keys[0]]
File “G:forestOfThoughts.envlibsite-packageslangchainchainsbase.py”, line 123, in __call__
inputs = self.prep_inputs(inputs)
File “G:forestOfThoughts.envlibsite-packageslangchainchainsbase.py”, line 206, in prep_inputs
raise ValueError(
ValueError: A single string input was passed in, but this chain expects multiple inputs ({‘response_3’, ‘response_1’, ‘response_2’, ‘answer’}). When a chain expects multiple inputs, please call it by passing in a dictionary, eg `chain({‘foo’: 1, ‘bar’: 2})`
how do I execute this line in a windows VSCODE environment? cp tree-of-thoughts/experiements/ -r ~.local/lib/python3.10/site-packages/

Apologies for the oversight. It seems that the `final` chain expects multiple inputs as a dictionary, but we are currently passing a single string (`answer`) as the input. To resolve this issue, we need to pass the inputs as a dictionary.

Please modify the code as follows:

“`python
# …

answer = {
“response_1”: response_1,
“response_2”: response_2,
“response_3”: response_3,
“answer”: answer
}
fusion = PromptTemplate(template=get_together, input_variables=[“response_1”, “response_2”, “response_3”, “answer”])

conclusion = final.run(answer)

# …
“`

After making this change, run the Streamlit application again with the following command:

“`
streamlit run app.py
“`

Now, the application should run successfully without any errors.
To execute the command `cp tree-of-thoughts/experiments/ -r ~/.local/lib/python3.10/site-packages/` in a Windows environment using Visual Studio Code (VSCode), you’ll need to use the appropriate Windows command for copying files/folders (`xcopy` or `robocopy`) instead of the Unix-based `cp` command.

Here’s the equivalent command using `robocopy`:

“`shell
robocopy tree-of-thoughts/experiments/ “%USERPROFILE%.locallibpython3.10site-packages” /E
“`

This command will recursively copy the contents of the `tree-of-thoughts/experiments/` directory to the specified destination directory `%USERPROFILE%.locallibpython3.10site-packages`, including any subdirectories and files (`/E` flag).

You can open the integrated terminal in VSCode by going to the `View` menu and selecting `Terminal`. Then you can run the `robocopy` command in the terminal. Make sure you are in the correct working directory before executing the command.

Alternatively, you can open a regular Command Prompt or PowerShell window outside of VSCode and run the `robocopy` command there.
robocopy tree-of-thoughts/experiments/ “%USERPROFILE%.locallibpython3.10site-packages” /E

——————————————————————————-
ROBOCOPY :: Robust File Copy for Windows
——————————————————————————-

Started : Monday, June 12, 2023 2:39:04 PM
Source : G:TheCompilertree-of-thoughtsexperiments
Dest : C:UsersSarah.locallibpython3.10site-packages

Files : *.*

Options : *.* /S /E /DCOPY:DA /COPY:DAT /R:1000000 /W:30

——————————————————————————

2023/06/12 14:39:04 ERROR 2 (0x00000002) Accessing Source Directory G:TheCompilertree-of-thoughtsexperiments
The system cannot find the file specified.
The error message indicates that the source directory `G:TheCompilertree-of-thoughtsexperiments` cannot be found. Make sure you provide the correct path to the source directory containing the `experiments` folder.

Please verify the following:

1. Ensure that the `tree-of-thoughts` directory is located in the `G:` drive and that it contains the `experiments` folder.

2. Double-check the spelling and capitalization of the directory names to ensure they match exactly.

If the directory structure or path is different, modify the source directory path accordingly in the `robocopy` command.
G:TheCompilertree-of-thoughtsexperiements
Apologies for the confusion. It seems there’s a typo in the path you provided. The correct path should be:

“`
G:TheCompilertree-of-thoughtsexperiments
“`

Please double-check the spelling of the `experiments` folder in the path. Once you have confirmed the correct path, execute the `robocopy` command with the updated path:

“`
robocopy G:TheCompilertree-of-thoughtsexperiments “%USERPROFILE%.locallibpython3.10site-packages” /E
“`

This command should copy the contents of the `experiments` folder to the specified destination.
robocopy G:TheCompilertree-of-thoughtsexperiments “%USERPROFILE%.locallibpython3.10site-packages” /E

——————————————————————————-
ROBOCOPY :: Robust File Copy for Windows
——————————————————————————-

Started : Monday, June 12, 2023 2:46:38 PM
Source : G:TheCompilertree-of-thoughtsexperiments
Dest : C:UsersSarah.locallibpython3.10site-packages

Files : *.*

Options : *.* /S /E /DCOPY:DA /COPY:DAT /R:1000000 /W:30

——————————————————————————

2023/06/12 14:46:38 ERROR 2 (0x00000002) Accessing Source Directory G:TheCompilertree-of-thoughtsexperiments
The system cannot find the file specified.
Apologies for the confusion. It seems that the specified source directory `G:TheCompilertree-of-thoughtsexperiments` cannot be found.

Please verify that the path `G:TheCompilertree-of-thoughtsexperiments` exists and contains the files you want to copy. Ensure that the folder name is correct and that there are files within the directory.

If the directory exists and the files are present, you can try using the full path without the trailing backslash in the source directory:

“`shell
robocopy “G:TheCompilertree-of-thoughtsexperiments” “%USERPROFILE%.locallibpython3.10site-packages” /E
“`

This command should copy the files and directories from `G:TheCompilertree-of-thoughtsexperiments` to `%USERPROFILE%.locallibpython3.10site-packages` recursively.
what does this do: echo “export OPENAI_API_KEY=” >> ~/.bashrc

The command `echo “export OPENAI_API_KEY=” >> ~/.bashrc` appends a new line to the `.bashrc` file in your home directory (`~`). It exports the environment variable `OPENAI_API_KEY` and assigns it the value “.

Environment variables are used to store configuration settings or values that can be accessed by programs running on your system. By adding this line to the `.bashrc` file, you ensure that the `OPENAI_API_KEY` environment variable is set every time you open a new terminal session or start a new shell.
echo “export OPENAI_API_KEY=” >> ~/.bashrc
The system cannot find the path specified.
It appears that you encountered an error because the specified path `~/.bashrc` could not be found. The `~` symbol represents the home directory, so `~/.bashrc` refers to the `.bashrc` file located in your user’s home directory.

Please make sure you are running the command in a Unix-like environment, such as Linux or macOS, where the `.bashrc` file is commonly used. If you are using a Windows environment, you can try using the equivalent file `.bash_profile` or modify the command accordingly.

Alternatively, you can manually open the `.bashrc` file in a text editor and add the line `export OPENAI_API_KEY=` at the end of the file. Save the changes and restart your terminal for the environment variable to take effect.
I don’t have a .bashc file
If you’re using a Windows environment, the equivalent file to `.bashrc` is typically `.bash_profile` or `.profile`. You can try using one of these files instead.

Here’s the modified command for adding the environment variable to the `.bash_profile` file:

“`
echo “export OPENAI_API_KEY=” >> ~/.bash_profile
“`

If you don’t have either `.bash_profile` or `.profile`, you can create a new file named `.env` in your project directory and add the following line to it:

“`
OPENAI_API_KEY=
“`

Remember to replace “ with your actual OpenAI API key.

After saving the file, you can load the environment variables from the `.env` file in your Python code using a library like `python-dotenv`.
python created a .env folder
If Python created a `.env` folder instead of a `.env` file, it means that you might have used a different method to create the environment variables.

To set environment variables using a `.env` folder, you can follow these steps:

1. Create a folder named `.env` in your project directory.
2. Inside the `.env` folder, create a file named `openai.env`.
3. Open the `openai.env` file and add the following line to it:

“`
OPENAI_API_KEY=
“`

Replace “ with your actual OpenAI API key.

4. Save the `openai.env` file.

To load the environment variables from the `.env` folder in your Python code, you can use a library like `python-dotenv`. Here’s an example of how to use it:

“`python
from dotenv import load_dotenv

# Load the environment variables from the .env folder
load_dotenv(dotenv_path=”.env/openai.env”)

# Now you can access the environment variables
openai_api_key = os.getenv(“OPENAI_API_KEY”)
“`

Make sure you have the `python-dotenv` library installed (`pip install python-dotenv`) before running your code.
Let’s take this step by step. I will type ok when ready for the next step, ok?
Sure, I’ll guide you step by step. Please type “ok” when you’re ready for the next step.
Here are the instructions I need to follow:

# Architectural Analysis for ‘Tree of Thoughts’ Based Programming Tool

In this analysis, we will describe four potential architectures for creating a programming tool based on the Tree of Thoughts (ToT) paradigm. Our aim is to take a specification for a product and produce a suite of unit tests with an LLM (Language Learning Model) that leverages ToT. This system would then use these tests as an evaluation score to guide the generation of the final program.

The primary components we need for such a system are:

1. An interpreter to translate the product specification into a format understandable by the LLM.
2. A generator that utilizes the LLM with ToT to produce a suite of unit tests.
3. A ToT-LLM runner that uses the unit tests as an evaluation score to generate the final program.

### Architecture 1: Monolithic System

In this architecture, all components of the system are built as a single, tightly-coupled unit. The upside is simplicity, but the downside is that it might be hard to maintain or modify parts of the system independently.

#### Potential Pseudocode:

“`python
class ToTProgramming:
def __init__(self, spec, LLM, unit_tests):
self.spec = spec
self.LLM = LLM
self.unit_tests = unit_tests

def translate_spec(self):
return self.spec_interpreter(self.spec)

def generate_unit_tests(self):
return self.test_generator(self.LLM, self.spec)

def generate_program(self):
return self.ToT_runner(self.LLM, self.spec, self.unit_tests)
“`

### Architecture 2: Service Oriented Architecture (SOA)

In this architecture, each component is implemented as an independent service that communicates with others via well-defined APIs. This offers greater modularity and scalability but could increase complexity.

#### Potential Pseudocode:

“`python
class SpecInterpreter:
def translate(self, spec):
return translated_spec

class TestGenerator:
def generate(self, LLM, spec):
return unit_tests

class ToTRunner:
def generate_program(self, LLM, spec, unit_tests):
return program
“`

### Architecture 3: Microservices Architecture

Here, we decompose the system into even smaller, loosely-coupled services. Each service does one thing well. This architecture offers great modularity and scalability, allows for better distribution of development tasks, and enables components to be updated or replaced independently. However, it increases the complexity of service orchestration and data sharing.

### Architecture 4: Event-Driven Architecture

In this setup, components interact through asynchronous events. This allows for high flexibility and scalability, but might increase complexity, especially for understanding the flow of data and control.

For each of the above architectures, some actionable steps include:

1. Define the interfaces between components (e.g., how should the specification be formatted so the LLM can understand it?)
2. Implement the LLM-based generator that produces a suite of unit tests from the specification.
3. Implement the runner that uses the ToT paradigm to guide the LLM in producing the final program, utilizing the unit tests as evaluation scores.
4. Rigorously test each component and the whole system.
5. Evaluate and iteratively refine the system based on feedback and metrics (e.g., how well does the generated program match the specification? How effective are the generated unit tests?)

# To-Do List for Making Tree of Thoughts Programming Tool Production Ready

## Planning and Design

– [ ] Define the product vision and the scope of the system
– [ ] Determine system requirements and constraints
– [ ] Choose the system architecture based on the requirements and constraints
– [ ] Design the interfaces between system components
– [ ] Define the format for product specification input
– [ ] Plan the LLM training pipeline
– [ ] Design the strategy for generating unit tests
– [ ] Plan how to use these tests as evaluation scores in the ToT paradigm

## Implementation

– [ ] Set up the development environment
– [ ] Implement the specification interpreter
– [ ] Implement the unit test generator leveraging the LLM
– [ ] Implement the ToT-LLM runner
– [ ] Write unit tests for each individual component
– [ ] Write integration tests for the system as a whole

## Testing and Refinement

– [ ] Run unit tests and fix any issues
– [ ] Run integration tests and fix any issues
– [ ] Test the system with various types of product specifications
– [ ] Evaluate the effectiveness of generated unit tests
– [ ] Evaluate how well the final program matches the specification
– [ ] Refine the system based on the results of testing and evaluation

## Production Preparation

– [ ] Review and optimize the code for efficiency and readability
– [ ] Develop a user-friendly interface for entering product specifications
– [ ] Implement error handling and exception mechanisms
– [ ] Set up a logging system for tracking system behavior and troubleshooting
– [ ] Prepare documentation, including user guides and technical documentation

## Deployment

– [ ] Set up the production environment
– [ ] Migrate the system to the production environment
– [ ] Conduct final tests in the production environment
– [ ] Launch the system
– [ ] Monitor system performance and troubleshoot any issues

## Post-Production

– [ ] Gather user feedback and monitor user satisfaction
– [ ] Maintain and update the system based on user feedback and new requirements
– [ ] Plan and implement new features as needed

Okay, let’s start with the first step: defining the product vision and the scope of the system. Please type “ok” when you’re ready to proceed.
ko
Great! The product vision and scope define the overall purpose and boundaries of the system. It helps clarify what the system aims to achieve and what functionalities it should include. It’s important to have a clear understanding of the problem the system is solving and the target audience.

To define the product vision and scope, consider the following questions:

1. What problem does the Tree of Thoughts (ToT) programming tool aim to solve?
2. Who are the target users of the tool?
3. What are the key features and functionalities the tool should provide?
4. Are there any specific constraints or limitations to consider?

Take some time to think about these questions and provide your answers.
ok
Great! Whenever you’re ready, please provide your answers to the following questions:

I’m here to assist you further with any additional questions or guidance you may need.
what is the python run command
The Python run command is `python`. It is followed by the name of the Python script you want to run. Here’s an example:

“`shell
python my_script.py
“`

In this example, `my_script.py` is the name of the Python script that you want to execute. Make sure you navigate to the directory where the script is located before running the command.

If you have any command-line arguments to pass to the script, you can include them after the script name. For example:

“`shell
python my_script.py arg1 arg2
“`

Here, `arg1` and `arg2` are the command-line arguments that will be passed to the script.

Note that the exact command may vary depending on your operating system and how Python is configured on your machine.
is this running in a loop? python test.py
None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won’t be available and only tokenizers, configuration and file/data utilities can be used.
Command output: ‘Hello, World’

[‘Create a class structure for the calculator that will allow for the addition, subtraction, multiplication, and division functions to be implemented in Python code, and provide the necessary terminal commands for any user input.’]
[‘Create a class for the calculator that will store the functions for addition, subtraction, multiplication, and division. For each of the functions, create an algorithm that will execute the calculation and return the result. Additionally, provide the terminal commands necessary’]
[“Create a class for the calculator and define the methods for each of the operations (addition, subtraction, multiplication, and division) in Python. Additionally, define the terminal commands needed to run the calculator, in the format ‘Terminal”]
[‘Create a class for each of the operations (addition, subtraction, multiplication, and division) and define the algorithm for each class to perform the desired operation. Additionally, create a main class
to call each of the operations and execute the’]
[‘Create a class structure that outlines the functions required for the calculator to perform the addition, subtraction, multiplication, and division operations in Python, as well as any terminal commands that are needed to execute the program.’]
[‘To ensure the most optimal programming language for the simple calculator, it is important to consider the speed and reliability of the language. Additionally, it is important to consider any terminal commands that may be necessary for the implementation of the calculator.’]
[“To create a simple calculator in Python, we need to design a high-level architecture that breaks down the operations into classes and algorithms. Additionally, we need to identify any terminal commands that may be necessary and provide them in the format ‘Termin”]
[‘To create the most optimal architecture for the simple calculator, we need to define the classes and algorithms needed to perform the operations of addition, subtraction, multiplication, and division in Python. Additionally, if any terminal commands are necessary to complete the’]
[“Create a class for the calculator that will contain the methods for addition, subtraction, multiplication, and division. Additionally, create a main method that will call the appropriate methods based on
the user’s input.”]
[‘Create a class to represent the calculator, which will include methods for each of the operations (addition, subtraction, multiplication, and division). The class should also include a main method that will take user input and call the appropriate methods to’]
[‘Create a class for the calculator that will contain the methods for addition, subtraction, multiplication, and division. Additionally, create a main method that will call the methods in the calculator class and allow the user to interact with the calculator.’]
[‘Create a class for the calculator that will contain the methods for performing the operations of addition, subtraction, multiplication, and division. Additionally, create methods for each of the operations that will take in two arguments and return the result of the operation’]
[‘Create a class for the calculator that contains the methods for addition, subtraction, multiplication, and division. Each method should be written in a way that is optimized for speed and reliability. Additionally, provide any terminal commands needed to execute the methods’]
[‘Create a class for each calculator operation (addition, subtraction, multiplication, and division) that contains the logic for performing the desired operation. Provide a main class that will call each of the individual classes to perform the desired calculation. Additionally’]
[‘The architecture should include a class for each operation (addition, subtraction, multiplication, and division) that will contain the logic for performing the operation. Each class should have a method for performing the operation, and the main class should include’]
[‘Create a class for the calculator with methods for addition, subtraction, multiplication, and division. Then use the most optimal programming language for Python to create the calculator. Finally, provide any terminal commands needed to execute the program.’]
[‘Create a class for the calculator that will contain the methods necessary to perform addition, subtraction, multiplication, and division. Identify the most efficient and reliable algorithms for each of these operations and implement them in the class. Additionally, provide any’]
[“Create a class structure that will be used to implement the calculator functions, such as addition, subtraction, multiplication, and division. Identify the necessary algorithms and create a flowchart to illustrate the program’s execution. Additionally, if any terminal”]
[‘Create a class structure for the calculator that can be used to store the necessary algorithms and functions needed to perform the addition, subtraction, multiplication, and division operations in Python. Additionally, provide any terminal commands needed to execute the code in the’]
[“Create a class structure in Python to store the calculator’s functionality, such as addition, subtraction, multiplication, and division. Define methods in the class for each of the calculator’s operations to ensure the most efficient and reliable architecture. If”]
[‘Create a class structure that will house the code for the calculator, with methods for each mathematical operation. Additionally, create a main method that will call each of these methods and ensure that
the calculator operates correctly.’]
[‘Create a class for the calculator that will define the methods for addition, subtraction, multiplication, and division. Then define the parameters for each of these methods and the logic for how they will be executed. Finally, create a main function to’]
[‘Create a class structure for the calculator that includes methods for each of the operations (addition, subtraction, multiplication, and division). Implement the operations in Python code and test the calculator to ensure it works correctly.’]
[‘To begin the architectural analysis, create a class for the calculator that will contain the methods for addition, subtraction, multiplication, and division. Additionally, create a main method that will call the methods and provide the terminal commands needed to execute the’]
[‘Create a class structure that implements the four functions (addition, subtraction, multiplication, and division) and define the algorithms needed to execute each of the operations. Additionally, provide
any terminal commands necessary to run the calculator in Python.’]
[‘Create a class structure that can store the user input and the results of the calculator operations and define the logic for the operations.’]
[‘Create a class structure to house the four functions of the calculator (addition, subtraction, multiplication, and division) and define the algorithms necessary to perform each operation. Additionally, provide any terminal commands necessary to execute the program in the format’]
[‘Create a class to represent the calculator and include all the necessary functions (addition, subtraction, multiplication, and division) in the class. Include a main function that will allow the user to input the desired operation and the two numbers to’]
[‘Create a class for the calculator that will contain functions for each of the operations (addition, subtraction, multiplication, and division) and use the most efficient algorithm for each operation to ensure the fastest and most reliable performance.’]
[‘Create a class in Python for the calculator, which will house the methods for addition, subtraction, multiplication, and division. Additionally, create a main method that will call each of the four methods and output the result.’]
[‘Create a class structure that contains functions for addition, subtraction, multiplication, and division in Python, as well as any necessary terminal commands to execute the calculator.’]
[‘Create a class for the calculator that includes methods for each of the operations (add, subtract, multiply, divide) and use an object-oriented approach to design the architecture. Additionally, test the
code to ensure it is running correctly and use’]
[‘To begin the architectural analysis, create a class for the calculator that includes methods for addition, subtraction, multiplication, and division. Additionally, create functions that will take user inputs and call the appropriate methods to perform the desired calculations. Finally,’]
[‘Design a user interface for the calculator that is intuitive and easy to use for the user.’]
[‘Create a class that will contain the logic for each of the operations (addition, subtraction, multiplication and division) and create functions that will call the respective logic for each operation. Additionally, create a main function to call each of the’]
[‘Analyze the best way to structure the code to ensure that the calculator can perform the four operations efficiently and accurately. Research any additional terminal commands needed to execute the code, and document them in the format specified.’]
[‘Create a class for the calculator that will contain the methods for each of the operations and create a main method that will call each of the methods when the user inputs the desired operation.’]
[‘Create a class structure for the calculator which includes methods for addition, subtraction, multiplication, and division, and use the most efficient and reliable algorithms to ensure the calculator performs the operations quickly and accurately.’]
[“Create a class to handle the user input for the calculator, which will contain methods to parse the user’s input and call the appropriate methods for the calculator operations. Additionally, create a method to validate the user’s input before calling any of the”]
[‘Create a class for the calculator that contains the algorithms for addition, subtraction, multiplication and division. This class should be designed in such a way that it can be used to perform the operations quickly and reliably. Additionally, any terminal commands needed’]
[‘Create a class diagram to outline the architecture of the calculator, and identify the algorithms needed to perform the basic operations. Identify any terminal commands needed to execute the program and add them to the diagram.’]
[‘To begin the architectural analysis, create an outline of the classes and algorithms needed to create the simple calculator, and identify which terminal commands are necessary to execute the code in Python.’]
[‘Create a class for the calculator, with methods to perform the operations of addition, subtraction, multiplication, and division. Additionally, create a main method to call the operations and display the
results. Finally, provide any necessary terminal commands to run’]
[‘To create the most optimal architecture for the simple calculator, we need to break down the architecture into classes and algorithms, and provide the terminal commands needed to execute the calculator in Python.’]
[‘The architecture should be designed to ensure that the calculator is able to perform the operations quickly and reliably, so the code should be optimized for performance. Additionally, any terminal commands needed to run the calculator should be provided in a clear and concise format’]
[‘Create a class for the calculator that contains the methods for addition, subtraction, multiplication, and division. Additionally, create a main method to call the calculator class with the required parameters and output the result.’]
[‘Create a class for the calculator with methods for addition, subtraction, multiplication, and division. Use the most efficient algorithms for each operation to ensure the calculator is as fast and reliable as possible. Additionally, provide any terminal commands needed to run’]
[‘Create a class structure for the calculator that can store the different operations (addition, subtraction, multiplication, and division) and the associated algorithms needed to perform them in Python. Additionally, create functions to handle user input and output the results’]
[‘Create a class for the calculator that contains the methods for addition, subtraction, multiplication, and division. Create methods within the class that take user input and run the appropriate calculations. Provide terminal commands to run the calculator if needed.’]
[‘Create a class structure for the calculator, with methods for the addition, subtraction, multiplication, and division operations, and create a terminal command for each operation to allow users to easily
access the calculator.’]
[‘To create a simple calculator that can perform addition, subtraction, multiplication, and division in Python, the optimal architecture should include a class for each operation, and each class should contain the algorithms needed to perform the respective operation. Additionally, any’]
[‘Create a class structure to organize the calculator functions, such as addition, subtraction, multiplication, and division, and create an algorithm to execute the operations. Additionally, identify any terminal commands needed to run the calculator and provide them in the given’]
[‘Create a class for the calculator that contains the methods for addition, subtraction, multiplication, and division. This class should also contain a main method that will be used to call the other methods. Additionally, provide any terminal commands needed to run’]
[‘Create a class for the calculator that has methods for performing the four operations (addition, subtraction, multiplication, and division) and a main function that runs the program. Use the terminal command “python .py” to run’]
[‘Create a class structure for the calculator that includes methods for each of the operations (addition, subtraction, multiplication, and division), as well as any necessary terminal commands.’]
[‘Create a class structure for the calculator, including a class for each of the four operations (addition, subtraction, multiplication, and division). Define the functions for each operation, and use the terminal commands to execute the functions.’]
[‘Create a class for the calculator that will contain the methods for addition, subtraction, multiplication, and division. Each method should be written in the most optimal way for Python to ensure the fastest and most reliable performance. Additionally, provide any necessary’]
[‘Create a class for the calculator that stores the functions for addition, subtraction, multiplication, and division. Define the functions with parameters and provide the code to execute each operation. Then, create a main method to call each function and display’]
[‘To begin the architectural analysis, we need to create a class for the calculator that will contain the functions for addition, subtraction, multiplication, and division. This class should also contain the necessary terminal commands for running the calculator in Python.’]
[‘Create a class for the calculator that will contain the methods for addition, subtraction, multiplication, and division. Include a main method that will call the calculator methods when the user inputs the desired operation. Provide the user with a simple user interface’]
[‘Create a class structure that outlines the methods and functions needed to create the calculator, as well as any terminal commands needed to execute the program.’]
[‘Create a class to represent the calculator with methods for each of the operations (addition, subtraction, multiplication, and division) and use the Python programming language to write the code for the calculator. Additionally, use terminal commands to execute the’]
[‘The architecture should be designed to take advantage of the features of Python, such as object-oriented programming and built-in data structures, to create a simple calculator that is both fast and reliable. Additionally, any terminal commands needed to create the’]
[‘Create a class structure that includes a Calculator class with methods for addition, subtraction, multiplication, and division, and a Terminal class with methods to execute the appropriate commands.’]
[‘Create a class structure that outlines the functions and algorithms needed for the calculator, as well as any terminal commands needed to run the program.’]
[‘Create a class for the calculator that will handle the operations of addition, subtraction, multiplication, and division. Define the methods for each of these operations to ensure that the calculator functions correctly and efficiently.’]
[‘Create a class structure that can handle the four operations of the calculator (addition, subtraction, multiplication, and division) and assign the appropriate algorithms to each class to ensure the most
efficient and reliable operation. Additionally, provide any terminal commands’]
[‘Create a class structure for the calculator, including methods for each of the operations (addition, subtraction, multiplication, and division) and any other necessary functions. Ensure that the methods are optimized to provide the fastest, most reliable results.’]
[‘To begin the architectural analysis, we need to define the classes and algorithms needed to create the simple calculator. Additionally, we need to determine which programming language is the most optimal
for this task, and if any terminal commands are required, they should’]
[‘To create the most optimal architecture for the calculator, start by breaking down the problem into smaller tasks and identifying the necessary classes and algorithms needed to complete the task. Then, create the code for each class and algorithm in Python, and if any’]
[‘Create a main class for the calculator that will contain the methods for addition, subtraction, multiplication, and division. This class should also contain the code for the terminal commands, if needed.’]
[‘Create a class for each operation (addition, subtraction, multiplication, and division) and define the algorithms for each class that will perform the calculations. Then, use the terminal commands to test the code and ensure it is running correctly.’]
[‘Create a class for the calculator that can handle the operations of addition, subtraction, multiplication, and division. Create methods for each operation that can be called to perform the calculations. Make sure to include error handling for any invalid inputs.’]
[‘The first step in creating the calculator would be to create a class for each of the operations (addition, subtraction, multiplication, and division). Each class should contain functions that define the logic for each operation. Additionally, the code should’]
[“Create a class structure that outlines the algorithms and functions needed to create the calculator, such as a class for addition, subtraction, multiplication, and division. Additionally, provide any terminal commands needed to execute the code in the format ‘Terminal”]
[‘Create a class for the calculator that will contain the methods for addition, subtraction, multiplication, and division. Then create an algorithm for each of the operations that will use the methods in the calculator class to perform the operations. Finally, use’]
[‘Create a class structure for the calculator, with each class containing the methods needed to perform the operations of addition, subtraction, multiplication, and division. Additionally, create a main class that will act as the entry point for the calculator, and’
No, running the command `python test.py` does not inherently run the code in a loop. It simply executes the Python script `test.py` once. If you want the code to run in a loop, you need to include a loop structure (such as a `while` loop) within the script itself.
that is the output and it is still running
Apologies for the confusion. It seems that the code you provided is running in an infinite loop. The output you shared indicates that the code is continuously printing “Hello, World” without terminating.

To stop the program, you can manually interrupt the execution by pressing Ctrl + C in the terminal where the program is running. This keyboard shortcut will send a signal to terminate the program and should stop the infinite loop.

If you want to modify the code to prevent it from running indefinitely, you need to add a condition or loop control mechanism to exit the loop at a certain point. Without seeing the content of the “test.py” file, it’s difficult for me to provide specific guidance on how to modify the code to stop the loop.

Please provide the code from “test.py” if you’d like further assistance in fixing the loop issue.
Rewrite this using streamlit that will wait for user input. from tree_of_thoughts import OptimizedOpenAILanguageModel
from the_compiler import TerminalExecutor, TheCompiler

# Initialize the LLM
api_key = os.environ.get(“OPENAI_API_KEY”, “”)
LLM = OptimizedOpenAILanguageModel(api_key)

# Initialize the TerminalExecutor
terminal_executor = TerminalExecutor(config_file=”config_file.json”)

# Initialize The Compiler with the LLM and TerminalExecutor
compiler = TheCompiler(LLM, terminal_executor)

# Example usage
input_spec = “Create a simple calculator that can perform addition, subtraction, multiplication, and division”
k = 5
T = 3
b = 5
vth = 0.5
timeout = 10
confidence = 0.9
max_iterations = 5
convergence_threshold = 0.01
convergence_count = 5

# Call the compile method with the input problem and other params
solution = compiler.compile(input_spec, k, T, b, vth, timeout, confidence_threshold=confidence, max_iterations=max_iterations, convergence_threshold=convergence_threshold, convergence_count=convergence_count)

# Use the solution
print(f”solution: {solution}”)

# Extract terminal commands from the solution
terminal_commands = re.findall(r”TerminalCommand: (.+)”, solution)

# Execute terminal commands
for command in terminal_commands:
output = terminal_executor.execute(command)
if output:
print(f”Command output: {output}”) #

To rewrite the code using Streamlit and enable user input, you can create a Streamlit web application that prompts the user for input and displays the results. Here’s an example of how you can modify the code:

“`python
import streamlit as st
import os
from tree_of_thoughts import OptimizedOpenAILanguageModel
from the_compiler import TerminalExecutor, TheCompiler
import re

# Initialize the LLM
api_key = os.environ.get(“OPENAI_API_KEY”, “”)
LLM = OptimizedOpenAILanguageModel(api_key)

# Initialize the TerminalExecutor
terminal_executor = TerminalExecutor(config_file=”config_file.json”)

# Initialize The Compiler with the LLM and TerminalExecutor
compiler = TheCompiler(LLM, terminal_executor)

# Streamlit app
st.title(“Code Compiler”)

# Input form
input_spec = st.text_input(“Enter the problem specification”)
k = st.slider(“Enter the value for k”, min_value=1, max_value=10)
T = st.slider(“Enter the value for T”, min_value=1, max_value=10)
b = st.slider(“Enter the value for b”, min_value=1, max_value=10)
vth = st.slider(“Enter the value for vth”, min_value=0.1, max_value=1.0, step=0.1)
timeout = st.slider(“Enter the timeout value (seconds)”, min_value=1, max_value=60)
confidence = st.slider(“Enter the confidence threshold”, min_value=0.1, max_value=1.0, step=0.1)
max_iterations = st.slider(“Enter the max iterations”, min_value=1, max_value=10)
convergence_threshold = st.slider(“Enter the convergence threshold”, min_value=0.01, max_value=0.1, step=0.01)
convergence_count = st.slider(“Enter the convergence count”, min_value=1, max_value=10)

if st.button(“Compile”):
# Call the compile method with the input problem and other params
solution = compiler.compile(
input_spec,
k,
T,
b,
vth,
timeout,
confidence_threshold=confidence,
max_iterations=max_iterations,
convergence_threshold=convergence_threshold,
convergence_count=convergence_count
)

# Display the solution
st.write(“Solution:”, solution)

# Extract terminal commands from the solution
terminal_commands = re.findall(r”TerminalCommand: (.+)”, solution)

# Execute terminal commands
for command in terminal_commands:
output = terminal_executor.execute(command)
if output:
st.write(“Command output:”, output)
“`

You can save this code in a file, for example, `streamlit_app.py`, and run it using the following command in your terminal:

“`
streamlit run streamlit_app.py
“`

The Streamlit app will start, and you can interact with it by entering the required input values and clicking the “Compile” button. The solution and command outputs, if any, will be displayed on the app.