Update Gradio app with multiple files

README.md

@@ -7,8 +7,6 @@ sdk: gradio
 sdk_version: 4.44.0
 app_file: app.py
 pinned: false
-tags:
-- anycoder
 ---
 
 # AI Chatbot with Hugging Face Model

app.py

@@ -5,28 +5,29 @@ import os
 # Load the conversational model with HF token support
 # Using DialoGPT-medium for a larger, more capable chatbot
 token = os.getenv('HF_TOKEN')
-chatbot_model = pipeline("conversational", model="microsoft/DialoGPT-medium", token=token)
+chatbot_model = pipeline("text-generation", model="microsoft/DialoGPT-medium", token=token)
 
 def chat(message, history):
     # Build conversation string from history
     conversation_text = ""
     for user_msg, bot_msg in history:
         if user_msg:
-            conversation_text += f"User: {user_msg}\n"
+            conversation_text += f"@@PADDING@@ {user_msg} @@PADDING@@ "
         if bot_msg:
-            conversation_text += f"Bot: {bot_msg}\n"
+            conversation_text += f"{bot_msg} @@PADDING@@ "
     
     # Add current user message
-    conversation_text += f"User: {message}\nBot:"
+    conversation_text += f"@@PADDING@@ {message} @@PADDING@@ "
     
     # Generate response
-    result = chatbot_model(conversation_text)
+    result = chatbot_model(conversation_text, max_length=1000, num_return_sequences=1, temperature=0.8, do_sample=True, pad_token_id=50256)
     
-    # Extract the response (assuming the pipeline returns a string or dict)
-    if isinstance(result, list) and len(result) > 0:
-        response = result[0]['generated_text'].split("Bot:")[-1].strip()
-    else:
-        response = str(result).split("Bot:")[-1].strip()
+    # Extract the response
+    generated_text = result[0]['generated_text']
+    
+    # Split by padding and take the last part as response
+    parts = generated_text.split("@@PADDING@@")
+    response = parts[-1].strip() if parts else generated_text.strip()
     
     return response