Update app.py

app.py

@@ -1,32 +1,30 @@
 import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
 
-
-
-
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-small")
+model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-small")
 
 # Chat history (global per session)
 chat_history_ids = None
 
 def vibebot_response(user_input, history=[]):
     global chat_history_ids
+    # Encode user input
+    new_input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors='pt')
 
-    # Add emotional, sarcastic but warm flavor to the input
-    styled_prompt = f"You are VibeBot: a sarcastic but emotionally aware Gen-Z AI therapist. You use wit, Gen-Z lingo, empathy, and occasional dark humor. But deep down, you're caring. Respond to this user message in that style: {user_input}"
-
-    # Encode styled input
-    new_input_ids = tokenizer.encode(styled_prompt + tokenizer.eos_token, return_tensors='pt')
-
-    # Maintain chat history
+    # Append to chat history if exists
     bot_input_ids = torch.cat([chat_history_ids, new_input_ids], dim=-1) if chat_history_ids is not None else new_input_ids
 
-    # Generate bot response
+    # Generate response
     chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
     response = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
 
+    # Append for gradio history
     history.append((user_input, response))
     return history, history
 
-
 # Gradio interface
 demo = gr.ChatInterface(fn=vibebot_response, title="VibeBot: Gen-Z Therapist",
                         chatbot=gr.Chatbot(height=400), theme="default")