Update app.py

app.py

@@ -1,42 +1,45 @@
 import gradio as gr
 import faiss
-import json
 import numpy as np
+import json
 import os
-from sentence_transformers import SentenceTransformer
 import requests
+from sentence_transformers import SentenceTransformer
 
 # Load embedding model
 embed_model = SentenceTransformer("all-MiniLM-L6-v2")
 
-# Load FAISS index and texts
-index = faiss.read_index("faiss_index.bin")
+# Load your data
 with open("texts.json", "r") as f:
     texts = json.load(f)
 
-# OpenRouter API Key (Set as secret in Hugging Face Space settings)
+index = faiss.read_index("faiss_index.bin")
+text_embeddings = embed_model.encode(texts)
+
+# API info
 API_KEY = os.environ.get("OPENROUTER_API_KEY")
 MODEL = "deepseek/deepseek-chat-v3-0324:free"
 
-# Perform semantic search using FAISS
-def search_context(query, k=5):
-    query_embedding = embed_model.encode([query])
-    distances, indices = index.search(np.array(query_embedding), k)
-    relevant_chunks = [texts[i] for i in indices[0] if i < len(texts)]
-    return "\n".join(relevant_chunks)
+def get_relevant_context(question, top_k=3, threshold=0.6):
+    question_embedding = embed_model.encode([question])
+    distances, indices = index.search(np.array(question_embedding), top_k)
+    
+    context = []
+    for i, dist in zip(indices[0], distances[0]):
+        if dist < threshold:
+            context.append(texts[i])
+    return context
 
-def chat_fn(message, history):
-    # Search knowledge base
-    context = search_context(message)
+def chat_with_data(message, history):
+    context = get_relevant_context(message)
+    if not context:
+        return "❌ Sorry, I can only help with topics related to LogiqCurve."
 
-    if not context.strip():
-        return "❌ Sorry, I can only answer questions related to the content of logiqcurve.com."
+    context_text = "\n".join(context)
+    prompt = f"You are a helpful assistant for LogiqCurve. Use only the following context:\n\n{context_text}\n\nUser: {message}"
 
-    # Construct prompt
-    messages = [
-        {"role": "system", "content": "You are an assistant that only answers based on the provided CONTEXT. Do not answer from general knowledge. If the answer is not in the CONTEXT, reply with 'Sorry, I can only answer questions related to the content of logiqcurve.com.'"},
-        {"role": "user", "content": f"CONTEXT:\n{context}\n\nQUESTION:\n{message}"},
-    ]
+    messages = [{"role": "system", "content": "You are a helpful assistant that only uses provided context."}]
+    messages.append({"role": "user", "content": prompt})
 
     headers = {
         "Authorization": f"Bearer {API_KEY}",
@@ -49,20 +52,17 @@ def chat_fn(message, history):
     }
 
     try:
-        response = requests.post("https://openrouter.ai/api/v1/chat/completions", headers=headers, json=payload)
-        response.raise_for_status()
-        reply = response.json()["choices"][0]["message"]["content"]
+        res = requests.post("https://openrouter.ai/api/v1/chat/completions", headers=headers, json=payload)
+        res.raise_for_status()
+        reply = res.json()["choices"][0]["message"]["content"]
     except Exception as e:
         reply = f"❌ Error: {e}"
 
     return reply
 
-# Gradio Chat Interface (for Hugging Face Space)
-chatbot = gr.ChatInterface(
-    fn=chat_fn,
-    title="LogiqCurve ChatBot",
-    description="Ask anything about logiqcurve.com. This bot only answers based on its content.",
+gr.ChatInterface(
+    fn=chat_with_data,
+    title="LogiqCurve Assistant",
+    description="Ask anything about LogiqCurve (based on website data only)",
     theme="soft"
-)
-
-chatbot.launch()
+).launch()