Update app.py

app.py

@@ -1,45 +1,48 @@
 import gradio as gr
-import faiss
-import numpy as np
+import requests
 import json
 import os
-import requests
+import faiss
+import numpy as np
 from sentence_transformers import SentenceTransformer
 
-# Load embedding model
-embed_model = SentenceTransformer("all-MiniLM-L6-v2")
-
-# Load your data
+# ✅ Load vector store and data
+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")
-text_embeddings = embed_model.encode(texts)
+# ✅ Load embedding model
+model = SentenceTransformer("all-MiniLM-L6-v2")
 
-# API info
+# ✅ OpenRouter setup
 API_KEY = os.environ.get("OPENROUTER_API_KEY")
 MODEL = "deepseek/deepseek-chat-v3-0324:free"
 
-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
+# ✅ Helper: Find top-k relevant context
+def get_relevant_context(query, k=5):
+    query_vector = model.encode([query])
+    scores, indices = index.search(np.array(query_vector), k)
+    return [texts[i] for i in indices[0] if i < len(texts)]
 
+# ✅ Chat logic
 def chat_with_data(message, history):
+    greetings = ["hi", "hello", "hey", "salam", "assalamualaikum", "good morning", "good evening"]
+    message_lower = message.lower().strip()
+
+    if any(greet in message_lower for greet in greetings):
+        return "👋 Hello! How can I assist you regarding LogiqCurve today?"
+
     context = get_relevant_context(message)
     if not context:
-        return "❌ Sorry, I can only help with topics related to LogiqCurve."
+        return "❌ Sorry, I can only answer questions related to LogiqCurve and its services."
 
     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}"
 
-    messages = [{"role": "system", "content": "You are a helpful assistant that only uses provided context."}]
-    messages.append({"role": "user", "content": prompt})
+    messages = [
+        {"role": "system", "content": "You are a helpful assistant that answers only using provided context."},
+        {"role": "user", "content": prompt}
+    ]
 
     headers = {
         "Authorization": f"Bearer {API_KEY}",
@@ -54,15 +57,14 @@ def chat_with_data(message, history):
     try:
         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"]
+        return res.json()["choices"][0]["message"]["content"]
     except Exception as e:
-        reply = f"❌ Error: {e}"
-
-    return reply
+        return f"❌ Error: {e}"
 
+# ✅ Gradio UI
 gr.ChatInterface(
     fn=chat_with_data,
-    title="LogiqCurve Assistant",
-    description="Ask anything about LogiqCurve (based on website data only)",
+    title="MK Assistant",
+    description="Ask questions related to LogiqCurve. Chat is limited to website-related content only.",
     theme="soft"
 ).launch()