app.py

from fastapi import FastAPI, Request
from pydantic import BaseModel
import openai
import uvicorn
import requests
import re
import os

app = FastAPI()

MODEL = "meta-llama/Llama-3.3-70B-Instruct"
HF_TOKEN = os.environ["HF_TOKEN"]
HB_TOKEN = os.environ["HB_TOKEN"]
PROMPTS_DOC_URL = os.environ["PROMPTS"]

def fetch_prompts_from_google_doc():
    print("Fetching prompts from Google Doc...")
    response = requests.get(PROMPTS_DOC_URL)
    if response.status_code != 200:
        raise Exception("Failed to fetch document")
    
    text = response.text
    prompts = {}

    pattern = r"\{BEGIN (.*?)\}([\s\S]*?)\{END \1\}"
    matches = re.findall(pattern, text)

    for key, content in matches:
        prompts[key.strip()] = content.strip()

    return prompts

class NavigatePrompt(BaseModel):
    message: str
    code: str

class DrivePrompt(BaseModel):
    instruction: str
    code: str

@app.post("/drive")
async def drive(prompt: DrivePrompt):
    prompts = fetch_prompts_from_google_doc()
    print("Received POST to /drive")
    print("Instruction:", prompt.instruction)

    system_prompt = f"""
    ### Unit Information ###
    {prompts.get('UNIT_INFORMATION_DRIVER', '')}
    
    ### Role Description ###
    {prompts.get('ROLE_DESCRIPTION_DRIVER', '')}
    
    ### Behavioral Instructions ###
    {prompts.get('BEHAVIORAL_INSTRUCTIONS_DRIVER', '')}
    """

    user_prompt = f"""
    ### Instruction ###
    {prompt.instruction}
    
    ### Existing Code ###
    {prompt.code}
    """

    client = openai.OpenAI(
        api_key=HB_TOKEN,
        base_url="https://api.hyperbolic.xyz/v1",
    )

    chat_completion = client.chat.completions.create(
        model="meta-llama/Meta-Llama-3-70B-Instruct",
        messages=[
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": user_prompt},
        ],
        temperature=0.7,
        max_tokens=1024,
    )

    reply = chat_completion.choices[0].message.content.strip()
    print("Drive response:", reply)
    return {"reply": reply}

@app.post("/navigate")
async def chat(prompt: NavigatePrompt):
    prompts = fetch_prompts_from_google_doc()
    print("Received POST request")
    print("Message:", prompt.message)

    system_prompt = f"""
    ### Unit Information ###
    {prompts.get('UNIT_INFORMATION_NAVIGATOR', '')}
    
    ### Role Description ###
    {prompts.get('ROLE_DESCRIPTION_NAVIGATOR', '')}
    
    ### Topic Information ###
    {prompts.get('TOPIC_INFORMATION_NAVIGATOR', '')}
    
    ### Task Description ###
    {prompts.get('TASK_DESCRIPTION_NAVIGATOR', '')}
    
    ### Reference Solution ###
    {prompts.get('REFERENCE_SOLUTION_NAVIGATOR', '')}
    
    ### Behavioral Instructions ###
    {prompts.get('BEHAVIORAL_INSTRUCTIONS_NAVIGATOR', '')}
    """
    
    user_prompt = f"""
    ### Message ###
    {prompt.message}
    
    ### Code ###
    {prompt.code}
    """
    
    client = openai.OpenAI(
        api_key=HB_TOKEN,
        base_url="https://api.hyperbolic.xyz/v1",
    )

    chat_completion = client.chat.completions.create(
        model="meta-llama/Meta-Llama-3-70B-Instruct",
        messages=[
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": user_prompt},
        ],
        temperature=0.7,
        max_tokens=1024,
    )

    text_response = chat_completion.choices[0].message.content

    print("Text generation done", text_response.strip())
    return {"reply": text_response.strip()}