Update app.py

app.py

@@ -1,67 +1,68 @@
 import gradio as gr
 import faiss
-import numpy as np
 import json
-from sentence_transformers import SentenceTransformer
+import numpy as np
 import os
+from sentence_transformers import SentenceTransformer
+import requests
+
+# Load embedding model
+embed_model = SentenceTransformer("all-MiniLM-L6-v2")
 
-# Load your content and index
+# Load FAISS index and texts
+index = faiss.read_index("faiss_index.bin")
 with open("texts.json", "r") as f:
     texts = json.load(f)
 
-index = faiss.read_index("faiss_index.bin")
+# OpenRouter API Key (Set as secret in Hugging Face Space settings)
+API_KEY = os.environ.get("OPENROUTER_API_KEY")
+MODEL = "deepseek/deepseek-chat-v3-0324:free"
 
-# Load embedding model
-embed_model = SentenceTransformer("all-MiniLM-L6-v2")
+# 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 chat_fn(message, history):
+    # Search knowledge base
+    context = search_context(message)
 
-# Helper to retrieve relevant chunks
-def retrieve_chunks(query, k=5):
-    query_emb = embed_model.encode([query])
-    D, I = index.search(np.array(query_emb), k)
-    return [texts[i] for i in I[0] if i < len(texts)]
+    if not context.strip():
+        return "❌ Sorry, I can only answer questions related to the content of logiqcurve.com."
 
-# Main chatbot function
-def rag_chatbot(message, history, request: gr.Request):
-    user_header = request.headers.get("X-Source", "")
-    if user_header == "wordpress":
-        # RAG logic for WordPress embed
-        context_chunks = retrieve_chunks(message)
-        context = "\n".join(context_chunks)
-        if not context.strip():
-            return "❌ I'm sorry, I can only assist with questions related to LogiqCurve.com."
-        prompt = f"Answer based ONLY on this content:\n{context}\n\nQuestion: {message}"
-    else:
-        # Public fallback (general AI)
-        prompt = 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}"},
+    ]
 
-    # Use OpenRouter API
     headers = {
-        "Authorization": f"Bearer {os.environ.get('OPENROUTER_API_KEY')}",
+        "Authorization": f"Bearer {API_KEY}",
         "Content-Type": "application/json"
     }
 
     payload = {
-        "model": "deepseek/deepseek-chat-v3-0324:free",
-        "messages": [
-            {"role": "system", "content": "You are a helpful assistant."},
-            {"role": "user", "content": prompt}
-        ]
+        "model": MODEL,
+        "messages": messages
     }
 
     try:
-        import requests
-        res = requests.post("https://openrouter.ai/api/v1/chat/completions", headers=headers, json=payload)
-        res.raise_for_status()
-        return res.json()["choices"][0]["message"]["content"]
+        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"]
     except Exception as e:
-        return f"❌ API Error: {e}"
+        reply = f"❌ Error: {e}"
+
+    return reply
 
-# Gradio interface
-iface = gr.ChatInterface(
-    fn=rag_chatbot,
-    title="LogiqCurve WordPress Assistant",
-    description="Answers only based on the content of LogiqCurve.com",
+# 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.",
     theme="soft"
 )
 
-iface.launch()
+chatbot.launch()