Update app.py

app.py

@@ -3,26 +3,27 @@ from sentence_transformers import SentenceTransformer
 from sklearn.metrics.pairwise import cosine_similarity
 import numpy as np
 import tempfile
+import os
 
-# Initialize embedding model
+# Load embedding model
 embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
 
 def create_chatbot(role, context, info, conv_starter, file):
     knowledge_chunks = []
     chunk_embeddings = None
-    
+
     if file:
-        # Use tempfile to handle uploaded file
+        # Save uploaded file to a temp file
         with tempfile.NamedTemporaryFile(delete=False) as temp:
             temp.write(file.read())
             temp_path = temp.name
-        
+
         with open(temp_path, 'r', encoding='utf-8') as f:
             text = f.read()
         os.unlink(temp_path)
-        
+
         knowledge_chunks = [chunk.strip() for chunk in text.split('\n\n') if chunk.strip()]
-        
+
         if knowledge_chunks:
             chunk_embeddings = embedding_model.encode(knowledge_chunks)
             status = f"✅ Loaded {len(knowledge_chunks)} knowledge chunks"
@@ -30,7 +31,8 @@ def create_chatbot(role, context, info, conv_starter, file):
             status = "❌ File is empty"
     else:
         status = "⚠️ No file uploaded"
-    
+
+    # Store all chatbot settings and knowledge in a dict (state)
     return status, {
         "role": role,
         "context": context,
@@ -41,52 +43,47 @@ def create_chatbot(role, context, info, conv_starter, file):
     }
 
 def respond(message, history, state):
-    # Check for information requests
+    # Special info queries
     if any(keyword in message.lower() for keyword in ["more info", "contact", "information", "email", "details"]):
         return state["info"]
-    
-    # Handle empty knowledge base
+
+    # No knowledge base loaded
     if not state.get("knowledge"):
         return "⚠️ Please upload knowledge base first"
-    
-    # Process query
+
+    # Embed user query
     query_embedding = embedding_model.encode([message])
     similarities = cosine_similarity(query_embedding, state["embeddings"])[0]
     max_index = np.argmax(similarities)
     max_similarity = similarities[max_index]
-    
-    # Return best match if above threshold
+
+    # If similar enough, return the best chunk
     if max_similarity > 0.45:
         return state["knowledge"][max_index]
-    
+
+    # Fallback
     return f"{state['role']}\n{state['context']}\nI can't help with that specific question."
 
 with gr.Blocks(theme=gr.themes.Soft()) as app:
     gr.Markdown("# 🤖 Custom Chatbot Creator")
     gr.Markdown("Configure every aspect of your chatbot below")
-    
+
     with gr.Row():
         with gr.Column(scale=1):
             gr.Markdown("## Configuration Panel")
-            
+
             with gr.Group():
-                role = gr.Textbox(label="Role", 
-                                value="AI Assistant specialized in technical queries")
-                context = gr.Textbox(label="Context", 
-                                   value="Focus on providing concise, accurate answers based on the knowledge base")
-                info = gr.Textbox(label="Contact Info", 
-                                value="For more information, contact support@example.com")
-                conv_starter = gr.Textbox(label="Conversation Starter", 
-                                        value="Ask me about topics in the knowledge base")
-            
+                role = gr.Textbox(label="Role", value="AI Assistant specialized in technical queries")
+                context = gr.Textbox(label="Context", value="Focus on providing concise, accurate answers based on the knowledge base")
+                info = gr.Textbox(label="Contact Info", value="For more information, contact support@example.com")
+                conv_starter = gr.Textbox(label="Conversation Starter", value="Ask me about topics in the knowledge base")
+
             with gr.Group():
-                file = gr.File(label="Knowledge Base (.txt only)", 
-                             file_types=[".txt"], 
-                             type="binary")
+                file = gr.File(label="Knowledge Base (.txt only)", file_types=[".txt"], type="binary")
                 create_btn = gr.Button("Create Chatbot", variant="primary")
-            
+
             status = gr.Textbox(label="Status", interactive=False)
-            
+
             gr.Markdown("### Instructions")
             gr.Markdown("1. Configure all fields\n2. Upload knowledge file\n3. Click 'Create Chatbot'\n4. Chat in the right panel")
 
@@ -95,17 +92,15 @@ with gr.Blocks(theme=gr.themes.Soft()) as app:
             state = gr.State({})
             chatbot = gr.ChatInterface(
                 respond,
-                chatbot=gr.Chatbot(height=500, 
-                                 avatar_images=(None, (None, "https://i.imgur.com/7kQEsHU.png"))),
-                textbox=gr.Textbox(placeholder="Type your message...", 
-                                 container=False, 
-                                 autofocus=True),
-                submit_btn="Ask",
-                retry_btn=None,
-                undo_btn=None,
-                clear_btn="Clear History"
+                chatbot=gr.Chatbot(
+                    height=500,
+                    type="messages",  # Use OpenAI-style messages for future compatibility
+                    avatar_images=(None, (None, "https://i.imgur.com/7kQEsHU.png"))
+                ),
+                textbox=gr.Textbox(placeholder="Type your message...", container=False, autofocus=True),
+                submit_btn="Ask"
             )
-    
+
     create_btn.click(
         create_chatbot,
         inputs=[role, context, info, conv_starter, file],