Update app.py

app.py

@@ -1,32 +1,48 @@
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+import gradio as gr
+
+# Load pre-trained model
+tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
+model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
+
+# Global chat history
+chat_history_ids = None
+
 def chat(user_input, history=[]):
     global chat_history_ids
 
-    # Encode user input
+    # Tokenize user input
     new_input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors='pt')
 
-    # Append to chat history or start new
+    # Append to chat history
     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 response (LIMIT max_length and num_return_sequences)
+    # Generate response with controlled output
     chat_history_ids = model.generate(
         bot_input_ids,
-        max_length=1000,           # Can reduce to 500 if needed
+        max_length=500,  # shorter for safety
         pad_token_id=tokenizer.eos_token_id,
         do_sample=True,
         top_k=50,
         top_p=0.95,
-        temperature=0.7,
-        num_return_sequences=1
+        temperature=0.7
     )
 
-    # Decode only the new response part
+    # Decode model output
     response = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
 
-    # Truncate response if it’s too long (hard limit)
-    if len(response) > 1000:
-        response = response[:1000] + "..."
-
+    # Append to chat history
     history.append((user_input, response))
     return history, history
 
+# Create a Gradio ChatInterface
+chatbot_ui = gr.ChatInterface(
+    fn=chat,
+    title="Teen Mental Health Chatbot 🤖💬",
+    description="Talk to a supportive AI. Not a replacement for professional help.",
+)
 
+# Launch the app (required!)
+if __name__ == "__main__":
+    chatbot_ui.launch()