Spaces:
Sleeping
Sleeping
import os | |
from fastapi import FastAPI, HTTPException | |
from fastapi.responses import StreamingResponse | |
import openai # Use OpenAI's official API library | |
from pydantic import BaseModel | |
# Initialize FastAPI app | |
app = FastAPI() | |
# Define request body model for the prompt | |
class PromptRequest(BaseModel): | |
prompt: str | |
# Initialize OpenAI client | |
token = os.getenv("GITHUB_TOKEN") | |
if not token: | |
raise ValueError("GITHUB_TOKEN environment variable not set") | |
# Initialize OpenAI API client with API key | |
openai.api_key = token # Set the OpenAI API key | |
# Async generator to stream chunks from OpenAI's API | |
async def stream_response(prompt: str): | |
try: | |
# Create streaming chat completion with OpenAI API | |
response = openai.ChatCompletion.create( | |
model="gpt-4", # Replace with the model you're using (e.g., gpt-3.5-turbo or gpt-4) | |
messages=[ | |
{"role": "system", "content": "You are a helpful assistant."}, | |
{"role": "user", "content": prompt} | |
], | |
temperature=1.0, | |
top_p=1.0, | |
stream=True # Enable streaming | |
) | |
# Yield each chunk of the response as it arrives | |
for chunk in response: | |
content = chunk.get("choices", [{}])[0].get("delta", {}).get("content", "") | |
if content: | |
yield content # Yield the generated content | |
except Exception as err: | |
yield f"Error: {str(err)}" | |
# Endpoint to handle the prompt and stream response | |
async def generate_response(request: PromptRequest): | |
try: | |
# Return a StreamingResponse with the async generator | |
return StreamingResponse( | |
stream_response(request.prompt), | |
media_type="text/event-stream" # Use text/event-stream for streaming | |
) | |
except Exception as err: | |
raise HTTPException(status_code=500, detail=f"Server error: {str(err)}") | |
# Health check endpoint for Hugging Face Spaces | |
async def health_check(): | |
return {"status": "healthy"} | |