Create app.py

app.py

@@ -0,0 +1,58 @@
+import os
+from fastapi import FastAPI, HTTPException
+from fastapi.responses import StreamingResponse
+from openai import AsyncOpenAI
+from pydantic import BaseModel
+import asyncio
+
+# 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")
+endpoint = "https://models.github.ai/inference"
+model = "openai/gpt-4.1-mini"
+client = AsyncOpenAI(base_url=endpoint, api_key=token)
+
+# Async generator to stream chunks
+async def stream_response(prompt: str):
+    try:
+        # Create streaming chat completion
+        stream = await client.chat.completions.create(
+            messages=[
+                {"role": "system", "content": "You are a helpful assistant."},
+                {"role": "user", "content": prompt}
+            ],
+            temperature=1.0,
+            top_p=1.0,
+            model=model,
+            stream=True
+        )
+
+        # Yield each chunk as it arrives
+        async for chunk in stream:
+            if chunk.choices and len(chunk.choices) > 0:
+                content = chunk.choices[0].delta.content or ""
+                yield content
+
+    except Exception as err:
+        yield f"Error: {err}"
+
+# Endpoint to handle prompt and stream response
+@app.post("/generate")
+async def generate_response(request: PromptRequest):
+    try:
+        # Return a StreamingResponse with the async generator
+        return StreamingResponse(
+            stream_response(request.prompt),
+            media_type="text/plain"
+        )
+    except Exception as err:
+        raise HTTPException(status_code=500, detail=f"Server error: {err}")
+