Update app.py

app.py

@@ -1,71 +1,21 @@
 import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 import torch
-from transformers import AutoTokenizer, BartModel
 
-# Load the BART model and tokenizer
+# Load the BART tokenizer and model
 tokenizer = AutoTokenizer.from_pretrained("facebook/bart-base")
-model = BartModel.from_pretrained("facebook/bart-base")
+model = AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-base")
 
-# Define the user input widget
-user_input = gr.TextInput(
-    value="",
-    placeholder="Type something...",
-    rows=10,
-    cols=50,
-    fontsize=16,
-    padding=10,
-    border=True,
-    background="#f2f2f2"
-)
+def generate_response(user_input):
+    inputs = tokenizer(user_input, return_tensors="pt", max_length=512, truncation=True)
+    outputs = model.generate(**inputs)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
 
-# Define the chatbot output widget
-chatbot_output = gr.TextOutput(
-    value="",
-    fontsize=16,
-    padding=10,
-    border=True,
-    background="#ffffff"
-)
+input_textbox = gr.Textbox(lines=10, label="Enter your text here")
+output_textbox = gr.Textbox(label="Chatbot Response")
 
-# Define the button to trigger the chatbot response
-button = gr.Button(
-    label="Send",
-    fontsize=16,
-    padding=10,
-    border=True,
-    background="#4CAF50"
-)
+title = "Hugging Face BART Chatbot"
+description = "This chatbot uses the Hugging Face BART model to generate responses based on user input."
 
-# Define the callback function to call when the button is clicked
-def send_message():
-    # Get the user input text
-    input_text = user_input.value
-
-    # Tokenize the input text
-    inputs = tokenizer(input_text, return_tensors="pt")
-
-    # Run the input through the BART model to get the generated text
-    outputs = model(**inputs)
-    last_hidden_states = outputs.last_hidden_state
-
-    # Extract the generated text from the last hidden state
-    generated_text = tokenizer.generate(last_hidden_states, max_length=50, padding="max_length", truncation=True).strip()
-
-    # Update the chatbot output text
-    chatbot_output.value = generated_text
-
-# Add the widgets to the layout
-gr.layout(
-    gr.Row(
-        gr.Column(user_input),
-        gr.Column(button),
-        gr.Column(chatbot_output)
-    )
-)
-
-# Set up the button click event handler
-button.on_click(send_message)
-
-# Start the Gradio app
-if __name__ == "__main__":
-    gr.run()
+gr.Interface(fn=generate_response, inputs=input_textbox, outputs=output_textbox, title=title, description=description).launch()