Update app.py

app.py

@@ -4,11 +4,11 @@ import os
 import faiss
 import numpy as np
 import json
-from fastapi import FastAPI, Request
+from fastapi import FastAPI
 from pydantic import BaseModel
 from sentence_transformers import SentenceTransformer
 
-# ✅ Load vector data
+# Load data
 with open("texts.json", "r", encoding="utf-8") as f:
     texts = json.load(f)
 
@@ -18,13 +18,11 @@ embed_model = SentenceTransformer("all-MiniLM-L6-v2")
 API_KEY = os.environ.get("OPENROUTER_API_KEY")
 MODEL = "qwen/qwen-2.5-coder-32b-instruct:free"
 
-# ✅ Semantic search
 def get_context(query, top_k=5):
     query_vec = embed_model.encode([query])
     D, I = index.search(np.array(query_vec), top_k)
     return "\n".join([texts[i] for i in I[0]])
 
-# ✅ Chatbot response
 def chat_fn(message, history):
     headers = {
         "Authorization": f"Bearer {API_KEY}",
@@ -32,7 +30,7 @@ def chat_fn(message, history):
     }
 
     context = get_context(message)
-    messages = [{"role": "system", "content": f"You are CODEX Assistant by Mirxa Kamran. Use this context:\n{context}"}]
+    messages = [{"role": "system", "content": f"You are CODEX Assistant. Use this:\n{context}"}]
 
     for user, assistant in history:
         messages.append({"role": "user", "content": user})
@@ -51,31 +49,25 @@ def chat_fn(message, history):
 
     return reply
 
-# ✅ Gradio UI
-demo = gr.ChatInterface(
+# ✅ Gradio Interface
+gradio_ui = gr.ChatInterface(
     fn=chat_fn,
-    title="💻 CODEX Assistant by Mirxa Kamran",
-    description="Chat with a context-aware AI code assistant.",
+    title="💻 CODEX Assistant",
+    description="Ask me coding or technical questions!",
     theme="soft"
 )
 
-# ✅ FastAPI app
+# ✅ FastAPI App
 app = FastAPI()
 
-# ✅ Mount Gradio on root path
-app = gr.mount_gradio_app(app, demo, path="/")
+# ✅ Mount Gradio at `/`
+app = gr.mount_gradio_app(app, gradio_ui, path="/")
 
-# ✅ FastAPI POST API endpoint
+# ✅ FastAPI API Endpoint
 class ChatRequest(BaseModel):
     message: str
     history: list = []
 
 @app.post("/chat")
 def api_chat(req: ChatRequest):
-    reply = chat_fn(req.message, req.history)
-    return {"response": reply}
-
-# ✅ Run manually in local dev or on Spaces
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+    return {"response": chat_fn(req.message, req.history)}