System: 
Current model: gpt-4o
Current date: 2024-10-24T22:32:01.974Z

You are a helpful assistant. You can help me by answering my questions. You can also ask me questions.
User: Can we adapt this python code for interfacing with the amigochat api to the openai api format?
I want to use amigochat api with an openai compatible ui. I mean use the amigochat api instead of the openai ones, by using something like a proxy in python.

import requests
import json
import uuid
import os
from typing import Any, Dict, Optional, Generator

from webscout.AIutel import Optimizers
from webscout.AIutel import Conversation
from webscout.AIutel import AwesomePrompts, sanitize_stream
from webscout.AIbase import Provider, AsyncProvider
from webscout import exceptions

class AmigoChat(Provider):
    """
    A class to interact with the AmigoChat.io API.
    """

    AVAILABLE_MODELS = [
        "meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo",  # Llama 3
        "o1-mini",                                   # OpenAI O1 Mini
        "claude-3-sonnet-20240229",                  # Claude Sonnet
        "gemini-1.5-pro",                             # Gemini Pro
        "gemini-1-5-flash",                            # Gemini Flash
        "o1-preview",                                 # OpenAI O1 Preview
    ]

    def __init__(
        self,
        is_conversation: bool = True,
        max_tokens: int = 600,
        timeout: int = 30,
        intro: str = None,
        filepath: str = None,
        update_file: bool = True,
        proxies: dict = {},
        history_offset: int = 10250,
        act: str = None,
        model: str = "o1-preview",  # Default model
    ):
        """
        Initializes the AmigoChat.io API with given parameters.

        Args:
            is_conversation (bool, optional): Flag for chatting conversationally. Defaults to True.
            max_tokens (int, optional): Maximum number of tokens to be generated upon completion. Defaults to 600.
            timeout (int, optional): Http request timeout. Defaults to 30.
            intro (str, optional): Conversation introductory prompt. Defaults to None.
            filepath (str, optional): Path to file containing conversation history. Defaults to None.
            update_file (bool, optional): Add new prompts and responses to the file. Defaults to True.
            proxies (dict, optional): Http request proxies. Defaults to {}.
            history_offset (int, optional): Limit conversation history to this number of last texts. Defaults to 10250.
            act (str|int, optional): Awesome prompt key or index. (Used as intro). Defaults to None.
            model (str, optional): The AI model to use for text generation. Defaults to "o1-preview". 
                                    Options: "llama-three-point-one", "openai-o-one-mini", "claude", 
                                             "gemini-1.5-pro", "gemini-1.5-flash", "openai-o-one".
        """
        if model not in self.AVAILABLE_MODELS:
            raise ValueError(f"Invalid model: {model}. Choose from: {self.AVAILABLE_MODELS}")

        self.session = requests.Session()
        self.is_conversation = is_conversation
        self.max_tokens_to_sample = max_tokens
        self.api_endpoint = "https://api.amigochat.io/v1/chat/completions"
        self.stream_chunk_size = 64
        self.timeout = timeout
        self.last_response = {}
        self.model = model
        self.headers = {
            "Accept": "*/*",
            "Accept-Encoding": "gzip, deflate, br, zstd",
            "Accept-Language": "en-US,en;q=0.9,en-IN;q=0.8",
            "Authorization": "Bearer ",  # empty
            "Content-Type": "application/json",
            "DNT": "1",
            "Origin": "https://amigochat.io",
            "Priority": "u=1, i",
            "Referer": "https://amigochat.io/",
            "Sec-CH-UA": '"Microsoft Edge";v="129", "Not=A?Brand";v="8", "Chromium";v="129"',
            "Sec-CH-UA-Mobile": "?0",
            "Sec-CH-UA-Platform": '"Windows"',
            "Sec-Fetch-Dest": "empty",
            "Sec-Fetch-Mode": "cors",
            "Sec-Fetch-Site": "same-site",
            "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
                          "AppleWebKit/537.36 (KHTML, like Gecko) "
                          "Chrome/129.0.0.0 Safari/537.36 Edg/129.0.0.0",
            "X-Device-Language": "en-US",
            "X-Device-Platform": "web",
            "X-Device-UUID": str(uuid.uuid4()),
            "X-Device-Version": "1.0.22"
        }

        self.__available_optimizers = (
            method
            for method in dir(Optimizers)
            if callable(getattr(Optimizers, method)) and not method.startswith("__")
        )
        self.session.headers.update(self.headers)
        Conversation.intro = (
            AwesomePrompts().get_act(
                act, raise_not_found=True, default=None, case_insensitive=True
            )
            if act
            else intro or Conversation.intro
        )
        self.conversation = Conversation(
            is_conversation, self.max_tokens_to_sample, filepath, update_file
        )
        self.conversation.history_offset = history_offset
        self.session.proxies = proxies

    def ask(
        self,
        prompt: str,
        stream: bool = False,
        raw: bool = False,
        optimizer: str = None,
        conversationally: bool = False,
    ) -> Dict[str, Any]:
        """Chat with AI

        Args:
            prompt (str): Prompt to be send.
            stream (bool, optional): Flag for streaming response. Defaults to False.
            raw (bool, optional): Stream back raw response as received. Defaults to False.
            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
        Returns:
           dict : {}
        ```json
        {
           "text" : "How may I assist you today?"
        }
        ```
        """
        conversation_prompt = self.conversation.gen_complete_prompt(prompt)
        if optimizer:
            if optimizer in self.__available_optimizers:
                conversation_prompt = getattr(Optimizers, optimizer)(
                    conversation_prompt if conversationally else prompt
                )
            else:
                raise Exception(
                    f"Optimizer is not one of {self.__available_optimizers}"
                )

        # Define the payload
        payload = {
            "messages": [
                {"role": "system", "content": "Mai hu ba khabr"},
                {"role": "user", "content": conversation_prompt}
            ],
            "model": self.model,
            "frequency_penalty": 0,
            "max_tokens": 4000,
            "presence_penalty": 0,
            "stream": stream,  # Enable streaming
            "temperature": 0.5,
            "top_p": 0.95
        }

        def for_stream():
            try:
                # Make the POST request with streaming enabled
                with requests.post(self.api_endpoint, headers=self.headers, json=payload, stream=True) as response:
                    # Check if the request was successful
                    if response.status_code == 201:
                        # Iterate over the streamed response line by line
                        for line in response.iter_lines():
                            if line:
                                # Decode the line from bytes to string
                                decoded_line = line.decode('utf-8').strip()
                                if decoded_line.startswith("data: "):
                                    data_str = decoded_line[6:]
                                    if data_str == "[DONE]":
                                        break
                                    try:
                                        # Load the JSON data
                                        data_json = json.loads(data_str)
                                        
                                        # Extract the content from the response
                                        choices = data_json.get("choices", [])
                                        if choices:
                                            delta = choices[0].get("delta", {})
                                            content = delta.get("content", "")
                                            if content:
                                                yield content if raw else dict(text=content)
                                    except json.JSONDecodeError:
                                        print(f"Received non-JSON data: {data_str}")
                    else:
                        print(f"Request failed with status code {response.status_code}")
                        print("Response:", response.text)

            except requests.exceptions.RequestException as e:
                print("An error occurred while making the request:", e)

        def for_non_stream():
            # Accumulate the streaming response
            full_response = ""
            for chunk in for_stream():
                if not raw:  # If not raw, chunk is a dictionary 
                    full_response += chunk["text"]

            # Update self.last_response with the full text
            self.last_response.update(dict(text=full_response))
            self.conversation.update_chat_history(
                prompt, self.get_message(self.last_response)
            )
            return self.last_response

        return for_stream() if stream else for_non_stream()

    def chat(
        self,
        prompt: str,
        stream: bool = False,
        optimizer: str = None,
        conversationally: bool = False,
    ) -> str:
        """Generate response `str`
        Args:
            prompt (str): Prompt to be send.
            stream (bool, optional): Flag for streaming response. Defaults to False.
            optimizer (str, optional): Prompt optimizer name - `[code, shell_command]`. Defaults to None.
            conversationally (bool, optional): Chat conversationally when using optimizer. Defaults to False.
        Returns:
            str: Response generated
        """

        def for_stream():
            for response in self.ask(
                prompt, True, optimizer=optimizer, conversationally=conversationally
            ):
                yield self.get_message(response)

        def for_non_stream():
            return self.get_message(
                self.ask(
                    prompt,
                    False,
                    optimizer=optimizer,
                    conversationally=conversationally,
                )
            )

        return for_stream() if stream else for_non_stream()

    def get_message(self, response: dict) -> str:
        """Retrieves message only from response

        Args:
            response (dict): Response generated by `self.ask`

        Returns:
            str: Message extracted
        """
        assert isinstance(response, dict), "Response should be of dict data-type only"
        return response["text"]
    
if __name__ == '__main__':
    from rich import print
    ai = AmigoChat(model="o1-preview")
    response = ai.chat(input(">>> "))
    for chunk in response:
        print(chunk, end="", flush=True)
Assistant: Sure, I can help you adapt this code to use the AmigoChat API with an OpenAI-compatible UI. Essentially, you'll need to create a proxy that translates the OpenAI API requests to AmigoChat API requests. Here's a basic example of how you can achieve this:

1. **Create a Proxy Server**: This server will receive requests in the OpenAI API format and translate them to the AmigoChat API format.

2. **Modify the AmigoChat Class**: Adjust the class to handle requests in the OpenAI format.

Here's a simplified example:

```python
import requests
import json
import uuid
from flask import Flask, request, jsonify

app = Flask(__name__)

class AmigoChatProxy:
    def __init__(self, model="o1-preview"):
        self.api_endpoint = "https://api.amigochat.io/v1/chat/completions"
        self.headers = {
            "Authorization": "Bearer YOUR_AMIGOCHAT_API_KEY",
            "Content-Type": "application/json",
        }
        self.model = model

    def ask(self, prompt, max_tokens=600, temperature=0.5, top_p=0.95):
        payload = {
            "messages": [
                {"role": "system", "content": "You are a helpful assistant."},
                {"role": "user", "content": prompt}
            ],
            "model": self.model,
            "max_tokens": max_tokens,
            "temperature": temperature,
            "top_p": top_p
        }
        response = requests.post(self.api_endpoint, headers=self.headers, json=payload)
        return response.json()

amigo_chat = AmigoChatProxy()

@app.route('/v1/chat/completions', methods=['POST'])
def chat_completions():
    data = request.json
    prompt = data['messages'][0]['content']
    response = amigo_chat.ask(prompt)
    return jsonify(response)

if __name__ == '__main__':
    app.run(port=5000)
```

### Steps to Run the Proxy Server:
1. **Install Flask**: If you haven't already, install Flask using `pip install Flask`.
2. **Run the Server**: Save the above code to a file (e.g., `proxy_server.py`) and run it using `python proxy_server.py`.

### Usage:
- Point your OpenAI-compatible UI to `http://localhost:5000/v1/chat/completions` instead of the OpenAI API endpoint.

This is a basic example to get you started. You may need to adjust the code to handle additional parameters and error handling as per your requirements. Let me know if you need further assistance!
User: I think this code won't work, you need to handle everything and provide me with the full, production ready, proxy
Assistant: Certainly! Below is a more complete version of a proxy server using Flask that translates OpenAI-style requests to use the AmigoChat API. This version includes additional features for error handling and improved input parameter handling.

```python
import requests
from flask import Flask, request, jsonify
import uuid

app = Flask(__name__)

class AmigoChatProxy:
    def __init__(self, api_key, model="o1-preview"):
        self.api_endpoint = "https://api.amigochat.io/v1/chat/completions"
        self.headers = {
            "Authorization": f"Bearer {api_key}",
            "Content-Type": "application/json",
        }
        self.model = model

    def ask(self, messages, max_tokens, temperature, top_p):
        payload = {
            "messages": messages,
            "model": self.model,
            "max_tokens": max_tokens,
            "temperature": temperature,
            "top_p": top_p
        }
        try:
            response = requests.post(self.api_endpoint, headers=self.headers, json=payload)
            response.raise_for_status()
            return response.json()
        except requests.exceptions.RequestException as e:
            return {"error": str(e)}

@app.route('/v1/chat/completions', methods=['POST'])
def chat_completions():
    data = request.json

    try:
        messages = data['messages']
        max_tokens = data.get('max_tokens', 150)
        temperature = data.get('temperature', 0.5)
        top_p = data.get('top_p', 0.9)

        if not isinstance(messages, list) or not all(isinstance(m, dict) for m in messages):
            raise ValueError("Invalid 'messages' format, must be a list of dictionaries.")

        amigo_chat = AmigoChatProxy(api_key='YOUR_AMIGOCHAT_API_KEY')  # Replace with your actual API key
        response = amigo_chat.ask(messages, max_tokens, temperature, top_p)

        return jsonify(response)

    except Exception as e:
        return jsonify({"error": str(e)}), 400

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5000, debug=False)
```

### Key Features

- **Error Handling**: Both at the request sending level and input validation level.
  
- **Parameter Handling**: Supports `messages`, `max_tokens`, `temperature`, and `top_p` parameters typical of the OpenAI API.

- **Production-Ready**: Proper error handling and input validation.

### Deployment and Usage

1. **Install Required Libraries**: Ensure you have Flask and Requests installed.
   ```bash
   pip install Flask requests
   ```

2. **Environment and Security**: Replace `'YOUR_AMIGOCHAT_API_KEY'` with your actual API key. Consider using environment variables for secret management.

3. **Deploy the Server**: Save the script as `proxy_server.py` and run with Python.
   ```bash
   python proxy_server.py
   ```

4. **Configure Your Client**: Point your client to `http://<server-ip>:5000/v1/chat/completions`.

5. **Production Deployment**: Consider using a WSGI server like Gunicorn and hosting solutions like AWS, Azure or Heroku for production deployment. Ensure appropriate security settings for API keys and network access.

### Notes

- **Testing**: Make sure to test with various input scenarios to ensure reliability.
- **Customizations**: Adapt the server to include additional parameters or advanced error logging as needed. 

Feel free to reach out for further refinements or specific adjustments!
User: Just to give you some extra context: this is a valid request.
import requests

headers = {
    'Host': 'api.amigochat.io',
    'X-Device-Platform': 'android',
    'X-Device-Version': '11',
    'X-Device-Brand': 'samsung',
    'X-Device-Id': 'exynos9610',
    'X-App-Build-Number': '12',
    'X-App-Version': '1.0',
    'X-App-Default-Lang': 'it',
    'Content-Type': 'application/json',
    # 'Content-Length': '352',
    # 'Accept-Encoding': 'gzip, deflate, br',
    'User-Agent': 'okhttp/4.12.0',
}

json_data = {
    'messages': [
        {
            'role': 'system',
            'content': 'You are OpenAI o1.  If the response includes mathematical expressions, wrap them in markdown symbols ```math.',
        },
        {
            'role': 'user',
            'content': 'Write a python script for fibonacci pls',
        },
    ],
    'model': 'o1-preview',
    'frequency_penalty': 0,
    'max_tokens': 4000,
    'presence_penalty': 0,
    'stream': False,
    'temperature': 0.5,
    'top_p': 0.95,
}

response = requests.post('https://api.amigochat.io/v1/chat/completions', headers=headers, json=json_data, verify=False)

print(response.content)

# Note: json_data will not be serialized by requests
# exactly as it was in the original request.
#data = '{"messages":[{"role":"system","content":"You are OpenAI o1.  If the response includes mathematical expressions, wrap them in markdown symbols ```math."},{"role":"user","content":"How do I solve partial derivatives equations"}],"model":"o1-mini","frequency_penalty":0,"max_tokens":4000,"presence_penalty":0,"stream":false,"temperature":0.5,"top_p":0.95}'
#response = requests.post('https://api.amigochat.io/v1/chat/completions', headers=headers, data=data, verify=False)

This is the output of the script:
PS C:\TMP\aiassist\amigochat> python .\request.py
C:\Users\Giovanni\AppData\Roaming\Python\Python310\site-packages\urllib3\connectionpool.py:1043: InsecureRequestWarning: Unverified HTTPS request is being made to host 'api.amigochat.io'. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/1.26.x/advanced-usage.html#ssl-warnings
  warnings.warn(
b'data: {"id":"chatcmpl-AM0ehIENr7CCNTEkkGTUnYMycL2Lu","object":"chat.completion.chunk","created":1729809099,"model":"o1-mini-2024-09-12","system_fingerprint":null,"choices":[{"index":0,"delta":{"content":"Sure! Here\'s a Python script that generates the Fibonacci sequence up to a specified number of terms:\\n\\n```python\\ndef fibonacci(n):\\n    sequence = []\\n    a, b = 0, 1\\n    while len(sequence) < n:\\n        sequence.append(a)\\n        a, b = b, a + b\\n    return sequence\\n\\n# Example usage:\\nnum_terms = 10\\nprint(fibonacci(num_terms))\\n```\\n\\n**Explanation:**\\n\\n1. **Function Definition:**  \\n   The `fibonacci` function takes an integer `n` as input and returns a list containing the first `n` Fibonacci numbers.\\n\\n2. **Initialization:**  \\n   - `sequence`: An empty list to store the Fibonacci numbers.\\n   - `a, b = 0, 1`: Initializes the first two Fibonacci numbers.\\n\\n3. **Generating the Sequence:**  \\n   The `while` loop runs until the `sequence` list contains `n` elements. In each iteration:\\n   - Append the current value of `a` to `sequence`.\\n   - Update `a` and `b` to the next two Fibonacci numbers using the assignments `a, b = b, a + b`.\\n\\n4. **Example Usage:**  \\n   The script generates and prints the first 10 Fibonacci numbers.\\n\\n---\\n\\nIf you\'re interested in calculating the nth Fibonacci number using the **closed-form expression** known as **Binet\'s Formula**, here\'s how you can do it:\\n\\n```python\\nimport math\\n\\ndef fibonacci_binet(n):\\n    phi = (1 + math.sqrt(5)) / 2\\n    psi = (1 - math.sqrt(5)) / 2\\n    return int((phi**n - psi**n) / math.sqrt(5))\\n\\n# Example usage:\\nn = 10\\nprint(fibonacci_binet(n))\\n```\\n\\n**Binet\'s Formula:**\\n\\n```math\\nF(n) = \\\\frac{\\\\phi^n - \\\\psi^n}{\\\\sqrt{5}}\\n```\\n\\nwhere:\\n\\n```math\\n\\\\phi = \\\\frac{1 + \\\\sqrt{5}}{2}, \\\\quad \\\\psi = \\\\frac{1 - \\\\sqrt{5}}{2}\\n```\\n\\n**Explanation:**\\n\\n- **Constants:**\\n  - `phi` (\\\\(\\\\phi\\\\)) is the golden ratio.\\n  - `psi` (\\\\(\\\\psi\\\\)) is the conjugate of the golden ratio.\\n\\n- **Calculation:**  \\n  The formula calculates the nth Fibonacci number by taking the difference of \\\\(\\\\phi^n\\\\) and \\\\(\\\\psi^n\\\\) and then dividing by \\\\(\\\\sqrt{5}\\\\). The `int` function is used to convert the result to an integer since the formula can produce floating-point numbers due to the square root of 5.\\n\\n**Note:**  \\nWhile Binet\'s Formula is elegant, it may suffer from floating-point precision issues for large `n`. For practical purposes, especially with large numbers, iterative or memoized approaches are recommended.\\n\\n---\\n\\n**Alternative: Using Memoization for Efficient Computation**\\n\\n```python\\nfrom functools import lru_cache\\n\\n@lru_cache(maxsize=None)\\ndef fibonacci_memo(n):\\n    if n < 2:\\n        return n\\n    return fibonacci_memo(n-1) + fibonacci_memo(n-2)\\n\\n# Example usage:\\nn = 10\\nprint([fibonacci_memo(i) for i in range(n)])\\n```\\n\\n**Explanation:**\\n\\n- **Memoization:**  \\n  The `@lru_cache` decorator caches the results of the `fibonacci_memo` function, avoiding redundant calculations and significantly improving performance for large `n`.\\n\\n- **Function Logic:**  \\n  Recursively calculates the nth Fibonacci number by summing the two preceding numbers in the sequence.\\n\\nThis approach combines the simplicity of recursion with the efficiency gains from memoization."},"logprobs":null,"finish_reason":"stop"}]}\n\ndata: [DONE]\n\n'
Assistant: