openai.error.APIError: HTTP code 502 from API (
502 Bad Gateway

502 Bad Gateway

nginx/1.18.0 (Ubuntu)
It looks like you’ve encountered a 502 Bad Gateway error from the OpenAI API. This error typically indicates that there’s an issue with the communication between your client and the OpenAI servers. It’s a server-side error, meaning the problem is on OpenAI’s end.

Here’s what you can do:

1. **Wait a bit:** Sometimes, these errors are temporary, and the issue may be resolved on OpenAI’s side. Give it some time and try your request again.

2. **Check OpenAI status:** Visit OpenAI’s status page or their official communication channels to see if they’ve reported any ongoing issues or maintenance.

3. **Review your code:** Ensure that your code and requests are correctly formatted and making proper use of the OpenAI API. Any mistakes in your implementation could lead to errors.

4. **Contact OpenAI support:** If the issue persists, reach out to OpenAI’s support for assistance. They can provide specific guidance based on the details of your situation.

If the problem continues, let me know, and I’ll do my best to help!
WHAT DOes this code do? # Copyright 2023 https://github.com/ShishirPatil/gorilla
#
# Licensed under the Apache License, Version 2.0 (the “License”);
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an “AS IS” BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import gradio as gr
import openai
import re

# There is no need for an API key let the following be as is
openai.api_key = “EMPTY”

# Set up the API base
#openai.api_base = “http://zanino.millennium.berkeley.edu:8000/v1”
# If there is any issue try using
openai.api_base = “http://34.132.127.197:8000/v1″

# Define function to get Gorilla response
def get_gorilla_response(prompt, model=”gorilla-7b-hf-v1”):
completion = openai.ChatCompletion.create(
model=model,
messages=[{“role”: “user”, “content”: prompt}]
)
return completion.choices[0].message.content

# Define function to parse output
def parse_output(text, model):
if model == “gorilla-7b-hf-v1”:
components = {}
components[‘domain’] = text.split(“<<>>:”)[1].split(“<<>>”)[0].strip()
components[‘api_call’] = text.split(“<<>>:”)[1].split(“<<>>”)[0].strip()
components[‘api_provider’] = text.split(“<<>>:”)[1].split(“<<>>”)[0].strip()
components[‘explanation’] = text.split(“<<>>:”)[1].split(“<<>>")[0].strip() components['code'] = text.split("<<>>:")[1].strip() return components elif model == "gorilla-mpt-7b-hf-v0": keys_to_remove = ['api_call', 'api_provider', 'explanation', 'code'] x = text.split(":") x.pop(0) for i in range(len(x)): for key in keys_to_remove: x[i] = x[i].replace(f'{key}','').replace(f", '{key}'", '').replace(f", '{key}':", '').replace(f"'{key}':", '').replace('''\"''','''"''').replace('''"\''','''"''').replace("""'""","""'""").replace("""'\""","""'""") components = { 'domain': x[0].strip("' ").replace("n<<<","").replace('"','').replace('',''), 'api_call': x[1].strip("' ").replace("n<<<","").replace('',''), 'api_provider': x[2].strip("' ").replace("n<<","").replace('',''), 'explanation': x[3].strip("' ").replace(r'n', 'n').replace('',''), 'code': x[4].strip("' ").replace(r'n', 'n').replace('','') } return components elif model == "gorilla-7b-th-v0": x = text.split(":") keys_to_remove = ['api_call', 'api_provider', 'explanation', 'code'] x.pop(0) for i in range(len(x)): for key in keys_to_remove: x[i] = x[i].replace(f", '{key}'", '').replace(f", '{key}':", '').replace(f"'{key}':", '').replace('''\"''','''"''').replace('''"\''','''"''').replace("""'""","""'""").replace("""'\""","""'""") components = { 'domain': x[0].strip("' "), 'api_call': x[1].strip("' "), 'api_provider': x[2].strip("' "), 'explanation': x[3].strip("' ").replace(r'n', 'n'), 'code': x[4].strip("' ").replace(r'n', 'n') } return components


# Define the function for the interface

def parse_and_display(prompt, model):

    text = get_gorilla_response(prompt, model)

    components = parse_output(text, model)

    domain = components['domain']

    api_call = components['api_call']

    api_provider = components['api_provider']

    explanation = components['explanation']

    code = components['code']

    return domain, api_call, api_provider, explanation, code
# Define example prompts

examples = [

    ["I would like to translate 'I feel very good today.' from English to French.","gorilla-7b-hf-v1"],

    ["I want to build a robot that can detecting objects in an image ‘cat.jpeg’. Input: [‘cat.jpeg’]","gorilla-7b-hf-v1"],

    ["I would like to translate from English to Chinese.","gorilla-7b-th-v0"],

]
# Create the Gradio interface

iface = gr.Interface(

    fn=parse_and_display,

    inputs=["text", gr.components.Dropdown(["gorilla-7b-hf-v1", "gorilla-7b-th-v0",  "gorilla-7b-tf-v0", "gorilla-mpt-7b-hf-v0"], label="Model")],

    outputs=[

        gr.components.Textbox(label="Domain"),

        gr.components.Textbox(label="API Call"),

        gr.components.Textbox(label="API Provider"),

        gr.components.Textbox(label="Explanation"),

        gr.components.Code(label="Code")

    ],

    title="Gorilla Gradio Explorer",

    description="Gorilla is an LLM that can pick the right API for your tasks. Check out the examples below. Learn more at gorilla.cs.berkeley.edu",

    examples=examples,

)
# Launch the interface and get the public gradio link

iface.launch()

This Python script defines a Gradio interface for interacting with the Gorilla language model. Gorilla is described as an LLM (Language Model) that can choose the appropriate API for various tasks.
Here's a breakdown of the script:
1. **License Information:** The script is released under the Apache License, Version 2.0.
2. **Import Statements:**

   - `gradio`: A library for creating UIs to interact with machine learning models.

   - `openai`: Presumably, this is the OpenAI GPT-3 API, which the Gorilla model interacts with.
3. **API Key Setup:**

   - `openai.api_key = "EMPTY"`: The API key is set to "EMPTY," suggesting that you might need to replace it with your actual OpenAI API key.
4. **API Base Setup:**

   - Two potential API base URLs are provided. If there's an issue with one, you're encouraged to try the other.
5. **Functions:**

   - `get_gorilla_response`: Sends a prompt to the Gorilla model and returns the response.

   - `parse_output`: Parses the model's output based on the specified model and extracts components like domain, API call, provider, explanation, and code.
6. **Gradio Interface Setup:**

   - `parse_and_display`: Combines the above functions to get a Gorilla response, parse it, and return specific components.

   - Inputs: Text prompt and a dropdown for selecting the Gorilla model.

   - Outputs: Textboxes for displaying the parsed components (Domain, API Call, API Provider, Explanation) and a code box for displaying the generated code.
7. **Example Prompts:**

   - A list of example prompts is provided, each with a corresponding Gorilla model selection.
8. **Gradio Interface Launch:**

   - The Gradio interface is created using the defined functions, inputs, and outputs.

   - The interface is launched, allowing users to interact with the Gorilla model and see the responses.
Overall, this script facilitates a user-friendly interface for experimenting with the Gorilla language model and exploring its capabilities in choosing the right API for given tasks.

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