Update app.py

app.py

@@ -1,9 +1,24 @@
-from fastapi import FastAPI, WebSocket, WebSocketDisconnect
-import json
+import os
+import openai
+from fastapi import FastAPI, WebSocket
+from fastapi.responses import HTMLResponse
+import gradio as gr
+
+openai.api_key = os.getenv("OPENAI_API_KEY")  # Use an environment variable
 
 app = FastAPI()
 
-# A dictionary to store WebSocket connections
+# Serve the index.html page via Gradio
+def serve_ui():
+    with open("index.html", "r") as f:
+        return f.read()
+
+gradio_app = gr.Interface(fn=serve_ui, inputs=[], outputs="html")
+
+# Mount Gradio inside FastAPI
+app.mount("/", gradio_app)
+
+# WebSocket signaling for WebRTC (basic forwarder)
 connections = {}
 
 @app.websocket("/ws/{client_id}")
@@ -12,12 +27,28 @@ async def websocket_endpoint(websocket: WebSocket, client_id: str):
     connections[client_id] = websocket
     try:
         while True:
-            # Wait for a message from the client (offer, answer, ICE candidates)
-            message = await websocket.receive_text()
-            # Broadcast the message to the other client
-            for conn_id, conn in connections.items():
-                if conn_id != client_id:
-                    await conn.send_text(message)
-    except WebSocketDisconnect:
-        connections.pop(client_id)
-        await websocket.close()
+            data = await websocket.receive_text()
+            # Forward message to all other clients
+            for cid, conn in connections.items():
+                if cid != client_id:
+                    await conn.send_text(data)
+    except:
+        connections.pop(client_id, None)
+
+# GPT-4o-based Interview Q&A endpoint
+@app.post("/ask")
+async def ask_question(payload: dict):
+    user_input = payload.get("message", "")
+    if not user_input:
+        return {"response": "No question provided."}
+    
+    response = openai.ChatCompletion.create(
+        model="gpt-4o",
+        messages=[
+            {"role": "system", "content": "You are an AI interviewer. Ask one question at a time."},
+            {"role": "user", "content": user_input}
+        ]
+    )
+    
+    reply = response.choices[0].message["content"]
+    return {"response": reply}