Update app.py

app.py

@@ -5,32 +5,34 @@ import faiss
 import numpy as np
 import json
 from sentence_transformers import SentenceTransformer
-from datetime import datetime
 
-# ✅ Load RAG context
+# ✅ Load context
 with open("texts.json", "r", encoding="utf-8") as f:
     texts = json.load(f)
 
 index = faiss.read_index("faiss_index.bin")
 embed_model = SentenceTransformer("all-MiniLM-L6-v2")
 
-# ✅ API setup
+# ✅ API Setup
 API_KEY = os.environ.get("OPENROUTER_API_KEY")
 MODEL = "meta-llama/llama-3.3-8b-instruct:free"
 
-# ✅ Get relevant context
-def get_context(query, top_k=5, threshold=0.45):
+# ✅ Context search
+def get_context(query, top_k=5, min_score=0.2):
     query_vec = embed_model.encode([query])
     D, I = index.search(np.array(query_vec), top_k)
-    if all(score < threshold for score in D[0]):
+
+    # If top score is too low, reject
+    if D[0][0] < min_score:
         return None
+
     return "\n".join([texts[i] for i in I[0]])
 
-# ✅ Chat logic
+# ✅ Chat function
 def chat_fn(message, history):
     context = get_context(message)
     if context is None:
-        return history[:-1] + [(message, "❌ Sorry! I cannot answer that.")], gr.update(visible=True)
+        return history[:-1] + [(message, "❌ Sorry! I cannot answer that.")]
 
     headers = {
         "Authorization": f"Bearer {API_KEY}",
@@ -40,7 +42,7 @@ def chat_fn(message, history):
     messages = [
         {
             "role": "system",
-            "content": f"You are a helpful assistant. Only answer using this context:\n{context}\nIf the answer is not in the context, reply with 'Sorry! I cannot answer that.'"
+            "content": f"You are a helpful assistant. Use ONLY this context to answer:\n{context}\nIf not found in context, reply: 'Sorry! I cannot answer that.'"
         }
     ]
 
@@ -62,16 +64,7 @@ def chat_fn(message, history):
     except Exception as e:
         reply = f"❌ Error: {e}"
 
-    return history[:-1] + [(message, reply)], gr.update(visible=True)
-
-# ✅ Export logs to file
-def export_logs(history):
-    timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
-    filename = f"chat_log_{timestamp}.txt"
-    log_text = "\n\n".join([f"You: {q}\nBot: {a}" for q, a in history])
-    with open(filename, "w", encoding="utf-8") as f:
-        f.write(log_text)
-    return filename
+    return history[:-1] + [(message, reply)]
 
 # ✅ UI
 with gr.Blocks(css="""
@@ -82,20 +75,17 @@ with gr.Blocks(css="""
     state = gr.State([])
 
     with gr.Row():
-        msg = gr.Textbox(placeholder="Type your message and press enter...", scale=8)
-        export_btn = gr.Button("Export Chat", scale=1, visible=False)
+        msg = gr.Textbox(placeholder="Type your message and press enter...", scale=9)
 
-    # Typing simulation
+    # Typing sim
     def user_send(message, history):
-        return "", history + [(message, "⏳ ...")], gr.update(visible=False)
+        return "", history + [(message, "⏳ ...")]
 
     def complete_chat(message, history):
         return chat_fn(message, history)
 
-    msg.submit(user_send, [msg, state], [msg, chatbot, export_btn]).then(
-        complete_chat, [msg, state], [chatbot, export_btn]
+    msg.submit(user_send, [msg, state], [msg, chatbot]).then(
+        complete_chat, [msg, state], [chatbot]
     )
 
-    export_btn.click(fn=export_logs, inputs=[state], outputs=gr.File(label="Download Chat Log"))
-
 demo.launch()