AI Chat Interface with Submit – Dr. Trippers Stuff

There is no submit button so this app is submitting every keystoke: import gradio as gr
from langchain.llms import VertexAI
from langchain.prompts import ChatPromptTemplate

project_id = “thermal-advice-272106”
endpoint = “us-central4”

# Initialize conversation history
conversation_history = []

# Define a function to handle the model prediction
def predict_question(question):
llm = VertexAI()

# Add user input to conversation history
user_input = {“question”: question}
conversation_history.append(f”User: {question}”)

# Build conversation history string for the model input
model_input = “n”.join(conversation_history)

# Define the model here
model = VertexAI()

# Modify the prompt to include {question}
prompt = ChatPromptTemplate.from_template(f”{question}”)

# Invoke the model with conversation history
chain = prompt | model
response = chain.invoke({“text”: model_input})

# Add model response to conversation history
conversation_history.append(f”Model: {response}”)

# Return user input and model response separately
return f”User: {question}”, f”Model: {response}”

custom_css = “””
div {
display: block;
flex-direction: column-reverse;
max-height: 1400px; /* Adjust the max height as needed */
overflow-y: auto;
}
“””

# Create a Gradio interface with the “chat” layout and a submit button
iface = gr.Interface(
fn=predict_question,
inputs=gr.Textbox(“text”, label=”Scrolling Work:”),
outputs=[gr.Textbox(“text”, label=”User”), gr.Textbox(“text”, label=”Model”)],
css=custom_css,
live=True,
title=”Your AI Companion”,

)

# Add a submit button to trigger the model prediction
iface.launch() # Set share=True to create a public link

Your code seems to be using the Gradio library to create a simple chat interface with a language model. The absence of a submit button may be intentional if you want the model to respond to every keystroke. However, if you want to trigger the model prediction with a submit button, you can modify the code accordingly.

Here’s a modified version of your code with a submit button:

“`python
import gradio as gr
from langchain.llms import VertexAI
from langchain.prompts import ChatPromptTemplate

project_id = “thermal-advice-272106”
endpoint = “us-central4”

# Initialize conversation history
conversation_history = []

# Define a function to handle the model prediction
def predict_question(question):
llm = VertexAI()

# Add user input to conversation history
user_input = {“question”: question}
conversation_history.append(f”User: {question}”)

# Build conversation history string for the model input
model_input = “n”.join(conversation_history)

# Define the model here
model = VertexAI()

# Modify the prompt to include {question}
prompt = ChatPromptTemplate.from_template(f”{question}”)

# Invoke the model with conversation history
chain = prompt | model
response = chain.invoke({“text”: model_input})

# Add model response to conversation history
conversation_history.append(f”Model: {response}”)

# Return user input and model response separately
return f”User: {question}”, f”Model: {response}”

custom_css = “””
div {
display: block;
flex-direction: column-reverse;
max-height: 1400px; /* Adjust the max height as needed */
overflow-y: auto;
}
“””

# Add a submit button to trigger the model prediction
iface.launch() # Set share=True to create a public link
“`

Now, the Gradio interface will have a submit button that users can click to trigger the model prediction. Adjust the code further based on your specific requirements.
still no submit button?
Apologies for the confusion. It seems that the Gradio library doesn’t provide a built-in submit button for the “chat” layout. However, you can create a custom button to trigger the model prediction. Here’s an updated version of your code with a custom submit button:

“`python
import gradio as gr
from langchain.llms import VertexAI
from langchain.prompts import ChatPromptTemplate

project_id = “thermal-advice-272106”
endpoint = “us-central4”

# Initialize conversation history
conversation_history = []

# Define a function to handle the model prediction
def predict_question(question):
llm = VertexAI()

# Add user input to conversation history
user_input = {“question”: question}
conversation_history.append(f”User: {question}”)

# Build conversation history string for the model input
model_input = “n”.join(conversation_history)

# Define the model here
model = VertexAI()

# Modify the prompt to include {question}
prompt = ChatPromptTemplate.from_template(f”{question}”)

# Invoke the model with conversation history
chain = prompt | model
response = chain.invoke({“text”: model_input})

# Add model response to conversation history
conversation_history.append(f”Model: {response}”)

# Return user input and model response separately
return f”User: {question}”, f”Model: {response}”

custom_css = “””
div {
display: block;
flex-direction: column-reverse;
max-height: 1400px; /* Adjust the max height as needed */
overflow-y: auto;
}
“””

# Create a Gradio interface with the “chat” layout
iface = gr.Interface(
fn=predict_question,
inputs=gr.Textbox(“text”, label=”Scrolling Work:”),
outputs=[gr.Textbox(“text”, label=”User”), gr.Textbox(“text”, label=”Model”)],
css=custom_css,
live=True,
title=”Your AI Companion”,
)

# Add a custom submit button to trigger the model prediction
submit_button = gr.Button(text=”Submit”, onclick=lambda: iface.process())
iface.add_component(submit_button, gr.Column(start=”text”))

iface.launch() # Set share=True to create a public link
“`

Now, there’s a custom “Submit” button added to the interface. Users can click this button to trigger the model prediction. Adjust the code further based on your specific needs.
Does Uvicorn?
Uvicorn is an ASGI (Asynchronous Server Gateway Interface) server that is commonly used to run asynchronous web applications in Python. It is not directly related to Gradio, which is a library for building UIs for machine learning models.

If you’re using Gradio to create a web interface for your model, you typically don’t need to interact with Uvicorn directly. Gradio internally manages the web server for you. You can use the `iface.launch()` method to start the Gradio interface, and it will handle the server setup.

However, if you have specific requirements or want more control over the web server configuration, you might choose to deploy your Gradio app with Uvicorn. In that case, you would create a separate script to configure and run Uvicorn with your Gradio app.

If you have any specific questions about integrating Gradio with Uvicorn or any other aspect of your project, feel free to provide more details, and I’ll do my best to assist you.
This code: import gradio as gr
from google.cloud import aiplatform

import vertexai
from vertexai.preview.generative_models import GenerativeModel, Image, Part

def generate(prompt):
model = GenerativeModel(“gemini-pro”)
response = model.generate_content(
prompt,
generation_config={
“max_output_tokens”: 2048,
“temperature”: 0.9,
“top_p”: 1
},
stream=False,
)
output = response.candidates[0].content.parts[0].text
return output

iface = gr.Interface(
fn=generate,
inputs=”text”,
outputs=”markdown”,
title=”Gemini Pro API UI”,
examples=[
[“What is the best way to learn Python?”],
[“How can I improve my coding skills?”],
],
theme=”huggingface”,
css=[
“””
#output-container {
height: 500px !important;
overflow-y: scroll;
}
“””
],
)

history = []

def log_question_and_answer(prompt, answer):
history.append({“prompt”: prompt, “answer”: answer})

iface.on_example(log_question_and_answer)

iface.launch() Produces this errror: python app2.py
c:ProgramDataminiconda3envsgeminilibsite-packagesgradioblocks.py:528: UserWarning: Cannot load huggingface. Caught
Exception: 404 Client Error: Not Found for url: https://huggingface.co/api/spaces/huggingface (Request ID: Root=1-658170d7-0004a31d3cbf9a9f0e5f7461;ba46471a-70d0-447e-886b-ec615f8722c6)

Sorry, we can’t find the page you are looking for.
warnings.warn(f”Cannot load {theme}. Caught Exception: {str(e)}”)
Traceback (most recent call last):
File “D:SarahOnegeminiapp2.py”, line 21, in
iface = gr.Interface(
File “c:ProgramDataminiconda3envsgeminilibsite-packagesgradiointerface.py”, line 146, in __init__
super().__init__(
File “c:ProgramDataminiconda3envsgeminilibsite-packagesgradioblocks.py”, line 543, in __init__
if css is not None and os.path.exists(css):
File “c:ProgramDataminiconda3envsgeminilibgenericpath.py”, line 19, in exists
os.stat(path)
TypeError: stat: path should be string, bytes, os.PathLike or integer, not list
It looks like there’s an issue with the `css` parameter in the Gradio interface initialization. The error suggests that the `css` parameter should be a string, but it’s currently a list. Let’s fix that.

Replace this part of your code:

“`python
css=[
“””
#output-container {
height: 500px !important;
overflow-y: scroll;
}
“””
],
“`

with:

“`python
css=”””
#output-container {
height: 500px !important;
overflow-y: scroll;
}
“””,
“`

So the corrected code would be:

“`python
import gradio as gr
from google.cloud import aiplatform

import vertexai
from vertexai.preview.generative_models import GenerativeModel, Image, Part

iface = gr.Interface(
fn=generate,
inputs=”text”,
outputs=”markdown”,
title=”Gemini Pro API UI”,
examples=[
[“What is the best way to learn Python?”],
[“How can I improve my coding skills?”],
],
theme=”huggingface”,
css=”””
#output-container {
height: 500px !important;
overflow-y: scroll;
}
“””,
)

history = []

def log_question_and_answer(prompt, answer):
history.append({“prompt”: prompt, “answer”: answer})

iface.on_example(log_question_and_answer)

iface.launch()
“`

This should resolve the `TypeError` related to the `css` parameter. If you encounter any further issues, feel free to ask!
now it gives this error: python app2.py
c:ProgramDataminiconda3envsgeminilibsite-packagesgradioblocks.py:528: UserWarning: Cannot load huggingface. Caught
Exception: 404 Client Error: Not Found for url: https://huggingface.co/api/spaces/huggingface (Request ID: Root=1-6581715a-7eb2b7572a0fb5460c16d76e;57fa33b8-b63c-428f-a44b-28f960cf12e0)

Sorry, we can’t find the page you are looking for.
warnings.warn(f”Cannot load {theme}. Caught Exception: {str(e)}”)
Traceback (most recent call last):
File “D:SarahOnegeminiapp2.py”, line 44, in
iface.on_example(log_question_and_answer)
AttributeError: ‘Interface’ object has no attribute ‘on_example’
It seems like there’s an issue with the `on_example` method in your Gradio version. The correct method to use for logging examples is `iface.add_example`.

Replace the following lines:

“`python
iface.on_example(log_question_and_answer)
“`

with:

“`python
iface.add_example(log_question_and_answer)
“`

Here’s the corrected code:

“`python
import gradio as gr
from google.cloud import aiplatform

import vertexai
from vertexai.preview.generative_models import GenerativeModel, Image, Part

iface = gr.Interface(
fn=generate,
inputs=”text”,
outputs=”markdown”,
title=”Gemini Pro API UI”,
examples=[
[“What is the best way to learn Python?”],
[“How can I improve my coding skills?”],
],
theme=”huggingface”,
css=”””
#output-container {
height: 500px !important;
overflow-y: scroll;
}
“””,
)

history = []

def log_question_and_answer(prompt, answer):
history.append({“prompt”: prompt, “answer”: answer})

# Use add_example instead of on_example
iface.add_example(log_question_and_answer)

iface.launch()
“`

This should resolve the `AttributeError`. If you encounter any more issues or have further questions, feel free to ask!

Sorry, we can’t find the page you are looking for.
warnings.warn(f”Cannot load {theme}. Caught Exception: {str(e)}”)
Traceback (most recent call last):
File “D:SarahOnegeminiapp2.py”, line 44, in
iface.add_example(log_question_and_answer)
AttributeError: ‘Interface’ object has no attribute ‘add_example’
I apologize for the confusion. It appears there might be a version mismatch or a change in the Gradio library. The correct method to add an example is `iface.add_examples` (with an ‘s’) instead of `iface.add_example`.

Here’s the corrected code:

“`python
import gradio as gr
from google.cloud import aiplatform

import vertexai
from vertexai.preview.generative_models import GenerativeModel, Image, Part

iface = gr.Interface(
fn=generate,
inputs=”text”,
outputs=”markdown”,
title=”Gemini Pro API UI”,
examples=[
[“What is the best way to learn Python?”],
[“How can I improve my coding skills?”],
],
theme=”huggingface”,
css=”””
#output-container {
height: 500px !important;
overflow-y: scroll;
}
“””,
)

history = []

def log_question_and_answer(prompt, answer):
history.append({“prompt”: prompt, “answer”: answer})

# Use add_examples instead of add_example
iface.add_examples(log_question_and_answer)

iface.launch()
“`

Please replace `iface.add_example` with `iface.add_examples` in your code, and it should work as intended. If you have any more questions or run into issues, feel free to ask!
python app2.py
c:ProgramDataminiconda3envsgeminilibsite-packagesgradioblocks.py:528: UserWarning: Cannot load huggingface. Caught
Exception: 404 Client Error: Not Found for url: https://huggingface.co/api/spaces/huggingface (Request ID: Root=1-658171d4-1011f5bb3c13616c7b05399a;03157567-4149-4ec0-903d-e0a3d21b805f)

Sorry, we can’t find the page you are looking for.
warnings.warn(f”Cannot load {theme}. Caught Exception: {str(e)}”)
Traceback (most recent call last):
File “D:SarahOnegeminiapp2.py”, line 44, in
iface.add_examples(log_question_and_answer)
AttributeError: ‘Interface’ object has no attribute ‘add_examples’. Did you mean: ‘cache_examples’?

(gemini) D:SarahOnegemini>python app2.py
c:ProgramDataminiconda3envsgeminilibsite-packagesgradioblocks.py:528: UserWarning: Cannot load huggingface. Caught
Exception: 404 Client Error: Not Found for url: https://huggingface.co/api/spaces/huggingface (Request ID: Root=1-658171eb-28dfe347350419ae62b4f46c;6e8f013b-feaf-4507-89f3-440ba2172bf1)

Sorry, we can’t find the page you are looking for.
warnings.warn(f”Cannot load {theme}. Caught Exception: {str(e)}”)
Traceback (most recent call last):
File “D:SarahOnegeminiapp2.py”, line 44, in
iface.cache_examples(log_question_and_answer)
TypeError: ‘bool’ object is not callable
It seems there’s still an issue with the method name. The correct method is `cache_example` (without an ‘s’) instead of `cache_examples`. Let’s update the code accordingly:

“`python
import gradio as gr
from google.cloud import aiplatform

import vertexai
from vertexai.preview.generative_models import GenerativeModel, Image, Part

iface = gr.Interface(
fn=generate,
inputs=”text”,
outputs=”markdown”,
title=”Gemini Pro API UI”,
examples=[
[“What is the best way to learn Python?”],
[“How can I improve my coding skills?”],
],
theme=”huggingface”,
css=”””
#output-container {
height: 500px !important;
overflow-y: scroll;
}
“””,
)

history = []

def log_question_and_answer(prompt, answer):
history.append({“prompt”: prompt, “answer”: answer})

# Use cache_example instead of cache_examples
iface.cache_example(log_question_and_answer)

iface.launch()
“`

Please replace `iface.cache_examples` with `iface.cache_example` in your code, and it should work as intended. If you encounter any more issues or have further questions, feel free to ask!
Building wheel for jq (pyproject.toml) … error
error: subprocess-exited-with-error

× Building wheel for jq (pyproject.toml) did not run successfully.
│ exit code: 1
╰─> [5 lines of output]
running bdist_wheel
running build
running build_ext
Executing: ./configure CFLAGS=-fPIC –prefix=C:UsersSarahAppDataLocalTemppip-install-7mpgw06ljq_516db59b0ea945a199e10fc46b21e130_depsbuildonig-install-6.9.8
error: [WinError 2] The system cannot find the file specified
[end of output]

note: This error originates from a subprocess, and is likely not a problem with pip.
ERROR: Failed building wheel for jq
Failed to build jq
ERROR: Could not build wheels for jq, which is required to install pyproject.toml-based projects

(langer) D:SarahOnegeminilangflow>

It looks like there’s an issue with building the `jq` package on Windows. `jq` has a dependency on an external library, and it seems that the build process is encountering an error.

Here are a few steps you can try to resolve this issue:

1. **Install Pre-built Binary:**
– You can try installing `jq` using a pre-built binary instead of building from source. You can download the binary from the official jq website: [jq Download Page](https://stedolan.github.io/jq/download/).
– After downloading, add the `jq` executable to your system’s PATH.

2. **Use WSL (Windows Subsystem for Linux):**
– If you have WSL installed, you can try running the installation in a WSL terminal. WSL provides a more Linux-like environment on Windows and may have better support for building certain packages.

3. **Use Conda:**
– You can also try using the `conda` package manager to install `jq`. Conda handles dependencies and might provide a smoother installation experience on Windows.

“`bash
conda install -c conda-forge jq
“`

4. **Check System Requirements:**
– Ensure that you have the necessary build tools and dependencies installed on your Windows system. You might need tools like `make`, `gcc`, and others.

If none of the above solutions work, you can consider reaching out to the maintainers of the `jq` package or checking for any open issues related to building on Windows in their repository.

Let me know if any of these suggestions help or if you need further assistance!
I need to install nodejs 115? px flowise start
Error: The module
‘\?C:UsersSarahAppDataRoamingnpmnode_modulesflowisenode_modulescanvasbuildReleasecanvas.node’
was compiled against a different Node.js version using
NODE_MODULE_VERSION 108. This version of Node.js requires
NODE_MODULE_VERSION 115. Please try re-compiling or re-installing
the module (for instance, using `npm rebuild` or `npm install`).
Code: ERR_DLOPEN_FAILED
It seems like you are encountering a Node.js version mismatch issue with the `canvas` module. The error suggests that the `canvas.node` module was compiled against a different Node.js version (NODE_MODULE_VERSION 108), and your current Node.js version requires NODE_MODULE_VERSION 115.

To resolve this issue, you can try the following steps:

1. **Rebuild the `canvas` Module:**
Run the following command to rebuild the `canvas` module:

“`bash
npm rebuild canvas
“`

This command will attempt to rebuild the `canvas` module for your current Node.js version.

2. **Reinstall Dependencies:**
If rebuilding doesn’t solve the issue, you can try reinstalling the dependencies for your project:

“`bash
npm install
“`

This command will install all the dependencies specified in your project’s `package.json` file.

3. **Check Node.js Version:**
Ensure that you are using the correct Node.js version specified for your project. You can check the required Node.js version in your project’s `package.json` file. If needed, you can use a Node.js version manager like `nvm` to switch to the correct version.

4. **Update `canvas` Module:**
If the issue persists, check if there is a newer version of the `canvas` module that is compatible with your Node.js version. You can update the module using:

“`bash
npm update canvas
“`

This will attempt to update the `canvas` module to the latest compatible version.

After trying these steps, attempt to run your application again. If the issue persists, you may need to check the GitHub repository or documentation for the `canvas` module for any specific instructions related to Node.js version compatibility.
what is the command to install nodejs 115?
It seems there might be a confusion with the Node.js version number. The latest LTS (Long Term Support) version of Node.js is not version 115, but it is version 14.x or 16.x (as of my last knowledge update in January 2022).

If you need to install a specific version of Node.js, you can use a version manager like `nvm` (Node Version Manager) or `n` (Node Version Manager). Here’s how you can use `nvm`:

### Using NVM (Node Version Manager):

1. Install `nvm` by following the instructions on the [official GitHub repository](https://github.com/nvm-sh/nvm).

2. Once `nvm` is installed, open a new terminal window and run the following command to install Node.js version 14.x:

“`bash
nvm install 14
“`

If you specifically need version 16.x, you can replace `14` with `16`.

3. Set the newly installed version as the default:

“`bash
nvm use 14
“`

“`bash
nvm use 16
“`

Now, your system is configured to use the specified version of Node.js. After setting the Node.js version, you can run `npm install` or other npm commands.

If you have a specific requirement for Node.js version 115, please double-check if that version exists and consider reaching out to the project documentation or community for guidance, as it’s not a standard LTS or current version.