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import os | |
from fastapi import FastAPI, HTTPException, Query | |
from fastapi.responses import StreamingResponse | |
from openai import AsyncOpenAI | |
app = FastAPI() | |
# Define available models (you can expand this list) | |
AVAILABLE_MODELS = { | |
"openai/gpt-4.1-mini": "OpenAI GPT-4 Mini", | |
"deepseek/DeepSeek-V3-0324": "DeepSeek V3 0324", | |
# Add more models as needed | |
} | |
async def generate_ai_response(prompt: str, model: str): | |
# Configuration for unofficial GitHub AI endpoint | |
token = os.getenv("GITHUB_TOKEN") | |
if not token: | |
raise HTTPException(status_code=500, detail="GitHub token not configured") | |
endpoint = "https://models.github.ai/inference" | |
# Validate the model | |
if model not in AVAILABLE_MODELS: | |
raise HTTPException(status_code=400, detail=f"Model not available. Choose from: {', '.join(AVAILABLE_MODELS.keys())}") | |
client = AsyncOpenAI(base_url=endpoint, api_key=token) | |
try: | |
stream = await client.chat.completions.create( | |
messages=[ | |
{"role": "system", "content": "You are a helpful assistant named Orion and made by Abdullah Ali"}, | |
{"role": "user", "content": prompt} | |
], | |
model=model, | |
temperature=1.0, | |
top_p=1.0, | |
stream=True | |
) | |
async for chunk in stream: | |
if chunk.choices and chunk.choices[0].delta.content: | |
yield chunk.choices[0].delta.content | |
except Exception as err: | |
yield f"Error: {str(err)}" | |
raise HTTPException(status_code=500, detail="AI generation failed") | |
async def generate_response( | |
prompt: str = Query(..., description="The prompt for the AI"), | |
model: str = Query("openai/gpt-4.1-mini", description="The model to use for generation") | |
): | |
if not prompt: | |
raise HTTPException(status_code=400, detail="Prompt cannot be empty") | |
return StreamingResponse( | |
generate_ai_response(prompt, model), | |
media_type="text/event-stream" | |
) | |
def get_app(): | |
return app |