Update app.py

app.py

@@ -1,8 +1,9 @@
 import torch
 from transformers import GPT2Tokenizer, GPT2LMHeadModel
+import gradio as gr
 
 # Load the custom model and tokenizer
-model_path =  'redael/model_udc'
+model_path = 'redael/model_udc'
 tokenizer = GPT2Tokenizer.from_pretrained(model_path)
 model = GPT2LMHeadModel.from_pretrained(model_path)
 
@@ -21,10 +22,10 @@ def generate_response(prompt, model, tokenizer, max_length=100, num_beams=1, tem
         max_length=max_length,
         num_return_sequences=1,
         pad_token_id=tokenizer.eos_token_id,
-        num_beams=num_beams,  # Use a lower number of beams
+        num_beams=num_beams,
         temperature=temperature,
         top_p=top_p,
-        repetition_penalty=repetition_penalty,  # Increased repetition penalty
+        repetition_penalty=repetition_penalty,
         early_stopping=True
     )
     response = tokenizer.decode(outputs[0], skip_special_tokens=True)
@@ -39,7 +40,7 @@ def generate_response(prompt, model, tokenizer, max_length=100, num_beams=1, tem
     response = ' '.join(clean_response)
     return response.strip()
 
-def respond(message, history: list[tuple[str, str]]):
+def respond(message, history):
     # Prepare the prompt from the history and the new message
     system_message = "You are a friendly chatbot."
     conversation = system_message + "\n"
@@ -56,10 +57,10 @@ def respond(message, history: list[tuple[str, str]]):
     
     return response
 
-# Gradio Chat Interface without customizable inputs
+# Gradio Chat Interface
 demo = gr.ChatInterface(
     respond
 )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()