main.py

import os
from dotenv import load_dotenv
from fastapi import FastAPI, HTTPException, Request
from fastapi.responses import StreamingResponse, HTMLResponse, JSONResponse
from pydantic import BaseModel
import httpx
import requests
import re
import json
from typing import Optional

load_dotenv()

app = FastAPI()

# Get API keys and secret endpoint from environment variables
api_keys_str = os.getenv('API_KEYS')
valid_api_keys = api_keys_str.split(',') if api_keys_str else []
secret_api_endpoint = os.getenv('SECRET_API_ENDPOINT')
secret_api_endpoint_2 = os.getenv('SECRET_API_ENDPOINT_2')
secret_api_endpoint_3 = os.getenv('SECRET_API_ENDPOINT_3')  # New endpoint for searchgpt

# Validate if the main secret API endpoints are set
if not secret_api_endpoint or not secret_api_endpoint_2 or not secret_api_endpoint_3:
    raise HTTPException(status_code=500, detail="API endpoint(s) are not configured in environment variables.")

# Define models that should use the secondary endpoint
alternate_models = {"gpt-4o-mini", "claude-3-haiku", "llama-3.1-70b", "mixtral-8x7b"}

class Payload(BaseModel):
    model: str
    messages: list
    stream: bool

def generate_search(query: str, stream: bool = True) -> str:
    headers = {"User-Agent": ""}
    prompt = [
        {"role": "user", "content": query},
    ]
    
    # Insert the system prompt at the beginning of the conversation history
    prompt.insert(0, {"content": "Be Helpful and Friendly", "role": "system"})
    
    payload = {
        "is_vscode_extension": True,
        "message_history": prompt,
        "requested_model": "searchgpt",
        "user_input": prompt[-1]["content"],
    }
    
    # Use the newly added SECRET_API_ENDPOINT_3 for the search API call
    chat_endpoint = secret_api_endpoint_3
    response = requests.post(chat_endpoint, headers=headers, json=payload, stream=True)
    
    # Collect streamed text content
    streaming_text = ""
    for value in response.iter_lines(decode_unicode=True, chunk_size=12):
        modified_value = re.sub("data:", "", value)
        if modified_value:
            try:
                json_modified_value = json.loads(modified_value)
                content = json_modified_value["choices"][0]["delta"]["content"]
                if stream:
                    yield f"data: {content}\n\n"
                streaming_text += content
            except:
                continue
    
    if not stream:
        yield streaming_text

@app.get("/searchgpt")
async def search_gpt(q: str, stream: Optional[bool] = False):
    if not q:
        raise HTTPException(status_code=400, detail="Query parameter 'q' is required")
    
    if stream:
        return StreamingResponse(
            generate_search(q, stream=True),
            media_type="text/event-stream"
        )
    else:
        # For non-streaming response, collect all content and return as JSON
        response_text = "".join([chunk for chunk in generate_search(q, stream=False)])
        return JSONResponse(content={"response": response_text})

@app.get("/", response_class=HTMLResponse)
async def root():
    # Open and read the content of index.html (in the same folder as the app)
    file_path = "index.html"

    try:
        with open(file_path, "r") as file:
            html_content = file.read()
        return HTMLResponse(content=html_content)
    except FileNotFoundError:
        return HTMLResponse(content="<h1>File not found</h1>", status_code=404)

async def get_models():
    async with httpx.AsyncClient() as client:
        try:
            response = await client.get(f"{secret_api_endpoint}/v1/models", timeout=3)
            response.raise_for_status()
            return response.json()
        except httpx.RequestError as e:
            raise HTTPException(status_code=500, detail=f"Request failed: {e}")

@app.get("/models")
async def fetch_models():
    return await get_models()

@app.post(["/chat/completions", "/v1/chat/completions"])
async def get_completion(payload: Payload, request: Request):
    # Use the correct endpoint depending on the model type (no authentication now 😉)
    endpoint = secret_api_endpoint_2 if payload.model in alternate_models else secret_api_endpoint
    
    # Use the payload directly as it includes stream and other user data
    payload_dict = payload.dict()
    print(payload_dict) # coz i m curious af heheheh :)
    #data is kept to me only so dont worry
    async def stream_generator(payload_dict):
        async with httpx.AsyncClient() as client:
            try:
                async with client.stream("POST", f"{endpoint}/v1/chat/completions", json=payload_dict, timeout=10) as response:
                    response.raise_for_status()
                    async for line in response.aiter_lines():
                        if line:
                            yield f"{line}\n"
            except httpx.HTTPStatusError as status_err:
                raise HTTPException(status_code=status_err.response.status_code, detail=f"HTTP error: {status_err}")
            except httpx.RequestError as req_err:
                raise HTTPException(status_code=500, detail=f"Streaming failed: {req_err}")
            except Exception as e:
                raise HTTPException(status_code=500, detail=f"An unexpected error occurred: {e}")
    
    return StreamingResponse(stream_generator(openai_payload), media_type="application/json")
@app.on_event("startup")
async def startup_event():
    print("API endpoints:")
    print("GET /")
    print("GET /models")
    print("GET /searchgpt")  # We now have the new search API
    print("POST /chat/completions")

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)