app.py

import os
import asyncio
from fastapi import FastAPI, HTTPException, Query
from fastapi.responses import StreamingResponse
from openai import AsyncOpenAI
from collections import defaultdict

app = FastAPI()

# Define available models

AVAILABLE_MODELS = {
"openai/gpt-4.1": "OpenAI GPT-4.1",
"openai/gpt-4.1-mini": "OpenAI GPT-4.1-mini",
"openai/gpt-4.1-nano": "OpenAI GPT-4.1-nano",
"openai/gpt-4o": "OpenAI GPT-4o",
"openai/gpt-4o-mini": "OpenAI GPT-4o mini",
"openai/o4-mini": "OpenAI o4-mini",
"microsoft/MAI-DS-R1": "MAI-DS-R1",
"microsoft/Phi-3.5-MoE-instruct": "Phi-3.5-MoE instruct (128k)",
"microsoft/Phi-3.5-mini-instruct": "Phi-3.5-mini instruct (128k)",
"microsoft/Phi-3.5-vision-instruct": "Phi-3.5-vision instruct (128k)",
"microsoft/Phi-3-medium-128k-instruct": "Phi-3-medium instruct (128k)",
"microsoft/Phi-3-medium-4k-instruct": "Phi-3-medium instruct (4k)",
"microsoft/Phi-3-mini-128k-instruct": "Phi-3-mini instruct (128k)",
"microsoft/Phi-3-small-128k-instruct": "Phi-3-small instruct (128k)",
"microsoft/Phi-3-small-8k-instruct": "Phi-3-small instruct (8k)",
"microsoft/Phi-4": "Phi-4",
"microsoft/Phi-4-mini-instruct": "Phi-4-mini-instruct",
"microsoft/Phi-4-multimodal-instruct": "Phi-4-multimodal-instruct",
"ai21-labs/AI21-Jamba-1.5-Large": "AI21 Jamba 1.5 Large",
"ai21-labs/AI21-Jamba-1.5-Mini": "AI21 Jamba 1.5 Mini",
"mistral-ai/Codestral-2501": "Codestral 25.01",
"cohere/Cohere-command-r": "Cohere Command R",
"cohere/Cohere-command-r-08-2024": "Cohere Command R 08-2024",
"cohere/Cohere-command-r-plus": "Cohere Command R+",
"cohere/Cohere-command-r-plus-08-2024": "Cohere Command R+ 08-2024",
"deepseek/DeepSeek-R1": "DeepSeek-R1",
"deepseek/DeepSeek-V3-0324": "DeepSeek-V3-0324",
"meta/Llama-3.2-11B-Vision-Instruct": "Llama-3.2-11B-Vision-Instruct",
"meta/Llama-3.2-90B-Vision-Instruct": "Llama-3.2-90B-Vision-Instruct",
"meta/Llama-3.3-70B-Instruct": "Llama-3.3-70B-Instruct",
"meta/Llama-4-Maverick-17B-128E-Instruct-FP8": "Llama 4 Maverick 17B 128E Instruct FP8",
"meta/Llama-4-Scout-17B-16E-Instruct": "Llama 4 Scout 17B 16E Instruct",
"meta/Meta-Llama-3.1-405B-Instruct": "Meta-Llama-3.1-405B-Instruct",
"meta/Meta-Llama-3.1-70B-Instruct": "Meta-Llama-3.1-70B-Instruct",
"meta/Meta-Llama-3.1-8B-Instruct": "Meta-Llama-3.1-8B-Instruct",
"meta/Meta-Llama-3-70B-Instruct": "Meta-Llama-3-70B-Instruct",
"meta/Meta-Llama-3-8B-Instruct": "Meta-Llama-3-8B-Instruct",
"mistral-ai/Ministral-3B": "Ministral 3B",
"mistral-ai/Mistral-Large-2411": "Mistral Large 24.11",
"mistral-ai/Mistral-Nemo": "Mistral Nemo",
"mistral-ai/Mistral-large-2407": "Mistral Large (2407)",
"mistral-ai/Mistral-small": "Mistral Small",
"cohere/cohere-command-a": "Cohere Command A",
"core42/jais-30b-chat": "JAIS 30b Chat",
"mistral-ai/mistral-small-2503": "Mistral Small 3.1"
}

# Chat memory (in-memory)

chat_histories = defaultdict(list)
MAX_HISTORY = 100  # limit memory to avoid crashes

# Generate response stream

async def generate_ai_response(chat_id: str, model: str):
token = os.getenv("GITHUB_TOKEN")
if not token:
raise HTTPException(status_code=500, detail="GitHub token not configured")

```
endpoint = "https://models.github.ai/inference"

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 asyncio.wait_for(
        client.chat.completions.create(
            messages=chat_histories[chat_id],
            model=model,
            temperature=1.0,
            top_p=1.0,
            stream=True
        ),
        timeout=60  # Prevent hangs
    )

    async for chunk in stream:
        if chunk.choices and chunk.choices[0].delta.content:
            content = chunk.choices[0].delta.content
            yield content
            chat_histories[chat_id].append({"role": "assistant", "content": content})
            chat_histories[chat_id] = chat_histories[chat_id][-MAX_HISTORY:]

except asyncio.TimeoutError:
    yield "Error: Response timed out."
    raise HTTPException(status_code=504, detail="Model timed out.")
except Exception as err:
    yield f"Error: {str(err)}"
    raise HTTPException(status_code=500, detail="AI generation failed")
```

# Chat endpoint

@app.post("/generate")
async def generate\_response(
chat\_id: str = Query(..., description="Unique chat ID"),
prompt: str = Query(..., description="User message"),
model: str = Query("openai/gpt-4.1-mini", description="Model to use")
):
if not prompt:
raise HTTPException(status\_code=400, detail="Prompt cannot be empty")

```
chat_histories[chat_id].append({"role": "user", "content": prompt})
chat_histories[chat_id] = chat_histories[chat_id][-MAX_HISTORY:]

return StreamingResponse(
    generate_ai_response(chat_id, model),
    media_type="text/event-stream"
)
```

# Optional: reset chat history

@app.post("/reset")
async def reset_chat(chat_id: str = Query(..., description="ID of chat to reset")):
if chat_id in chat_histories:
chat_histories\[chat_id].clear()
return {"message": f"Chat {chat_id} history reset."}
else:
raise HTTPException(status_code=404, detail="Chat ID not found")

# For ASGI servers like Uvicorn

def get\_app():
return app