Update app.py

app.py

@@ -1,41 +1,13 @@
 import gradio as gr
-import spaces
-import torch
-from fastapi import FastAPI
-from fastapi.middleware.cors import CORSMiddleware
+import uvicorn
+from app.main import app as fastapi_app
 
-# FastAPI app banao
-app = FastAPI()
+# Dummy Gradio (SDK detection ke liye)
+demo = gr.Interface(fn=lambda x: x, inputs="text", outputs="text")
 
-# CORS enable karo taaki frontend call kar sake
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
+# FastAPI app ko Gradio ke saath mount karo
+app = gr.mount_gradio_app(fastapi_app, demo, path="/ui")
 
-# --- API Routes ---
-@app.get("/documents")
-def get_documents():
-    # Yahan apne actual documents return karo
-    return {"documents": ["doc1.pdf", "doc2.pdf"]}
-
-@app.post("/ask")
-@spaces.GPU
-def ask_question(payload: dict):
-    question = payload.get("question", "")
-    # Yahan apna RAG / QA logic likho
-    zero = torch.Tensor([0]).to("cuda")
-    return {"answer": f"Answer for: {question}", "tensor": str(zero)}
-
-# --- Gradio UI (optional) ---
-@spaces.GPU
-def greet(n):
-    zero = torch.Tensor([0]).to("cuda")
-    return f"Hello {zero + n} Tensor"
-
-demo = gr.Interface(fn=greet, inputs=gr.Number(), outputs=gr.Text())
-
-# Gradio ko FastAPI ke andar mount karo
-app = gr.mount_gradio_app(app, demo, path="/ui")
+# Gradio SDK ki jagah khud uvicorn chalao
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860)