Update app.py

app.py

@@ -1,63 +1,65 @@
-import pickle
-from sentence_transformers import SentenceTransformer, util
-import torch
-import gradio as gr
-from fastapi import FastAPI, Query
-from fastapi.middleware.cors import CORSMiddleware
-from fastapi.responses import JSONResponse
-import nest_asyncio
-import threading
-
-# Load model
-with open("chatbot.pkl", "rb") as f:
-    data = pickle.load(f)
-
-questions = data["questions"]
-answers = data["answers"]
-question_embeddings = data["embeddings"]
-model = SentenceTransformer('all-MiniLM-L6-v2')
-
-def chat(user_question, threshold=0.4, top_k=1):
-    user_embedding = model.encode(user_question, convert_to_tensor=True)
-    cos_scores = util.cos_sim(user_embedding, question_embeddings)[0]
-    top_results = torch.topk(cos_scores, k=top_k)
-
-    for score, idx in zip(top_results.values, top_results.indices):
-        if score.item() >= threshold:
-            return answers[idx]
-
-    return "Sorry, I am not able to answer that."
-
-# --- FastAPI app for query params ---
-api = FastAPI()
-api.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-@api.get("/")
-def get_chat(question: str = Query(..., description="Your question here")):
-    answer = chat(question)
-    return JSONResponse({"answer": answer})
-
-# --- Gradio interface ---
-iface = gr.Interface(
-    fn=chat,
-    inputs=gr.Textbox(lines=2, placeholder="Ask a question..."),
-    outputs=gr.Textbox(),
-    title="FAQ Chatbot",
-    description="Ask any question and get answers from the FAQ."
-)
-
-# To run both Gradio and FastAPI in the same Space
-def run_gradio():
-    iface.launch(server_name="0.0.0.0", server_port=7860)
-
-import uvicorn, nest_asyncio
-nest_asyncio.apply()
-threading.Thread(target=run_gradio, daemon=True).start()
-
-# Run FastAPI
-uvicorn.run(api, host="0.0.0.0", port=8000)
+import pickle
+import torch
+from sentence_transformers import SentenceTransformer, util
+import gradio as gr
+from fastapi import FastAPI, Query
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse
+import threading
+import uvicorn
+import nest_asyncio
+
+# --------------- Load model & embeddings ---------------
+with open("chatbot.pkl", "rb") as f:
+    data = pickle.load(f)
+
+questions = data["questions"]
+answers = data["answers"]
+question_embeddings = data["embeddings"]
+
+model = SentenceTransformer('all-MiniLM-L6-v2')
+
+def chat(user_question, threshold=0.4, top_k=1):
+    """Return best answer or fallback if none found."""
+    user_embedding = model.encode(user_question, convert_to_tensor=True)
+    cos_scores = util.cos_sim(user_embedding, question_embeddings)[0]
+    top_results = torch.topk(cos_scores, k=top_k)
+
+    for score, idx in zip(top_results.values, top_results.indices):
+        if score.item() >= threshold:
+            return {"matched_question": questions[idx], "answer": answers[idx], "score": score.item()}
+
+    return {"matched_question": None, "answer": "Sorry, I am not able to answer that.", "score": None}
+
+# --------------- FastAPI API ---------------
+api = FastAPI()
+api.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+@api.get("/")
+def get_chat(question: str = Query(..., description="Your question here")):
+    response = chat(question)
+    return JSONResponse(response)
+
+# --------------- Gradio UI ---------------
+iface = gr.Interface(
+    fn=lambda q: chat(q)["answer"],
+    inputs=gr.Textbox(lines=2, placeholder="Ask a question..."),
+    outputs=gr.Textbox(),
+    title="FAQ Chatbot",
+    description="Ask any question and get answers from the FAQ."
+)
+
+def run_gradio():
+    iface.launch(server_name="0.0.0.0", server_port=7860)
+
+# --------------- Run both FastAPI + Gradio ---------------
+nest_asyncio.apply()
+threading.Thread(target=run_gradio, daemon=True).start()
+
+# Run FastAPI on port 8000
+uvicorn.run(api, host="0.0.0.0", port=8000)