Update app.py

app.py

@@ -2,22 +2,25 @@ import gradio as gr
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
 
+# Load model
 model_name = "microsoft/DialoGPT-medium"
 
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name)
 
+
+# Chat function
 def reply(message, history):
     if not message.strip():
         return "Please enter a message."
 
     chat_history_ids = None
 
-    # ✅ FIX: handle history safely
+    # Handle previous conversation
     for msg in history:
         content = msg["content"]
 
-        # FIX: sometimes content is list
+        # Fix: if content is list → convert to string
         if isinstance(content, list):
             content = " ".join([str(x) for x in content])
 
@@ -31,7 +34,7 @@ def reply(message, history):
         else:
             chat_history_ids = torch.cat([chat_history_ids, ids], dim=-1)
 
-    # current input
+    # Current message
     new_input_ids = tokenizer.encode(message + tokenizer.eos_token, return_tensors="pt")
 
     if chat_history_ids is not None:
@@ -39,8 +42,10 @@ def reply(message, history):
     else:
         input_ids = new_input_ids
 
+    # Attention mask fix
     attention_mask = torch.ones_like(input_ids)
 
+    # Generate response
     output_ids = model.generate(
         input_ids,
         attention_mask=attention_mask,
@@ -53,24 +58,27 @@ def reply(message, history):
         repetition_penalty=1.2
     )
 
-    # extract response
+    # Extract only new response
     response_ids = output_ids[:, input_ids.shape[-1]:]
     response = tokenizer.decode(response_ids[0], skip_special_tokens=True)
 
+    # Fallback
     if response.strip() == "":
         response = "I'm here! How can I help you?"
 
     return response
 
+
+# UI
 demo = gr.ChatInterface(
     fn=reply,
     title="💬 Smart Dialogue System",
-    description="Full conversation chatbot (fixed)"
+    description="Full conversation chatbot using DialoGPT"
 )
 
+# Launch
 demo.launch(
     server_name="0.0.0.0",
     server_port=7860,
     ssr_mode=False
-)
 )