Update app.py

app.py

@@ -1,25 +1,45 @@
 import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
 
-# Function to process the input
-def chatbot_response(user_input):
-    # Simple logic for chatbot (e.g., greeting or keywords detection)
-    if "hello" in user_input.lower():
-        return "Hello! How can I help you today?"
-    elif "how are you" in user_input.lower():
-        return "I'm doing great, thank you for asking!"
-    else:
-        return "I'm sorry, I didn't understand that. Can you rephrase?"
+# Load pre-trained Hugging Face model (DialoGPT-medium)
+model_name = "microsoft/DialoGPT-medium"
+model = AutoModelForCausalLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Chat history to maintain context
+chat_history_ids = None
+
+# Function to generate chatbot response using Hugging Face model
+def chatbot_response(user_input, chat_history):
+    # Tokenize user input and add chat history
+    new_user_input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors='pt')
+
+    # Append new input to the chat history
+    bot_input_ids = torch.cat([chat_history, new_user_input_ids], dim=-1) if chat_history is not None else new_user_input_ids
+
+    # Generate response from the model
+    chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id, 
+                                      temperature=0.7, top_k=50, top_p=0.95, num_return_sequences=1, 
+                                      no_repeat_ngram_size=3, do_sample=True)
+
+    # Decode the response and return
+    chat_history_ids = chat_history_ids[:, new_user_input_ids.shape[-1]:]  # Keep the new response only
+    bot_output = tokenizer.decode(chat_history_ids[0], skip_special_tokens=True)
+
+    return bot_output, chat_history_ids
 
 # Define the Gradio interface
 def create_interface():
     # Gradio interface setup
     with gr.Blocks() as demo:
-        gr.Markdown("## Chatbot Application")
+        gr.Markdown("## Chatbot powered by DialoGPT")
         with gr.Row():
             chat_box = gr.Chatbot()
             input_box = gr.Textbox(placeholder="Type a message...")
 
-        input_box.submit(chatbot_response, input_box, chat_box)
+        # Submit the input and get the response
+        input_box.submit(chatbot_response, [input_box, chat_box], [chat_box, gr.State()])
 
     return demo