mysite/interpreter/interpreter.py

import os
import shutil
import hmac
import hashlib
import base64
import subprocess
import time
from mysite.logger import logger
import async_timeout
import asyncio
import mysite.interpreter.interpreter_config 

GENERATION_TIMEOUT_SEC=60

def set_environment_variables():
    os.environ["OPENAI_API_BASE"] = "https://api.groq.com/openai/v1"
    os.environ["OPENAI_API_KEY"] = os.getenv("api_key")
    os.environ["MODEL_NAME"] = "llama3-8b-8192"
    os.environ["LOCAL_MODEL"] = "true"

# Set the environment variable.
def chat_with_interpreter(
    message, history, a=None, b=None, c=None, d=None
):  # , openai_api_key):
    # Set the API key for the interpreter
    # interpreter.llm.api_key = openai_api_key
    if message == "reset":
        interpreter.reset()
        return "Interpreter reset", history
    full_response = ""
    # add_conversation(history,20)
    user_entry = {"role": "user", "type": "message", "content": message}
    #messages.append(user_entry)
    # Call interpreter.chat and capture the result
    messages = []
    recent_messages = history[-20:]
    for conversation in recent_messages:
        user_message = conversation[0]
        user_entry = {"role": "user", "content": user_message}
        messages.append(user_entry)
        assistant_message = conversation[1]
        assistant_entry = {"role": "assistant", "content": assistant_message}
        messages.append(assistant_entry)

    user_entry = {"role": "user", "content": message}
    messages.append(user_entry)
    #system_prompt = {"role": "system", "content": "あなたは日本語の優秀なアシスタントです。"}
    #messages.insert(0, system_prompt)

    for chunk in interpreter.chat(messages, display=False, stream=True):
        # print(chunk)
        # output = '\n'.join(item['content'] for item in result if 'content' in item)
        full_response = format_response(chunk, full_response)
        yield full_response  # chunk.get("content", "")

    yield full_response + rows  # , history
    return full_response, history

async def completion(message: str, history, c=None, d=None, prompt="あなたは日本語の優秀なアシスタントです。"):
    from groq import Groq
    client = Groq(api_key=os.getenv("api_key"))
    messages = []
    recent_messages = history[-20:]
    for conversation in recent_messages:
        user_message = conversation[0]
        user_entry = {"role": "user", "content": user_message}
        messages.append(user_entry)
        assistant_message = conversation[1]
        assistant_entry = {"role": "assistant", "content": assistant_message}
        messages.append(assistant_entry)

    user_entry = {"role": "user", "content": message}
    messages.append(user_entry)
    system_prompt = {"role": "system", "content": prompt}
    messages.insert(0, system_prompt)
    async with async_timeout.timeout(GENERATION_TIMEOUT_SEC):
        try:
            stream = client.chat.completions.create(
                model="llama3-8b-8192",
                messages=messages,
                temperature=1,
                max_tokens=1024,
                top_p=1,
                stream=True,
                stop=None,
            )
            all_result = ""
            for chunk in stream:
                current_content = chunk.choices[0].delta.content or ""
                all_result += current_content
                yield current_content
            yield all_result
            #return all_result
        except asyncio.TimeoutError:
            raise HTTPException(status_code=504, detail="Stream timed out")