Update app.py

app.py

@@ -1,50 +1,11 @@
 import gradio as gr
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
+from transformers_js_py import pipeline
 
-# 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)
+pipe = await pipeline('sentiment-analysis')
 
-# Chat history to maintain context
-chat_history_ids = None
+async def process(text):
+    return await pipe(text)
 
-# 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')
+demo = gr.Interface(fn=process, inputs="text", outputs="json")
 
-    # 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 powered by DialoGPT")
-        with gr.Row():
-            chat_box = gr.Chatbot()
-            input_box = gr.Textbox(placeholder="Type a message...")
-
-        # Submit the input and get the response
-        input_box.submit(chatbot_response, [input_box, chat_box], [chat_box, gr.State()])
-
-    return demo
-
-# Launch the interface
-interface = create_interface()
-
-# Launch Gradio app on Hugging Face Spaces
-interface.launch()
+demo.launch()