Update app.py

app.py

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+import gradio as gr
+import transformers
+
+# Load a pre-trained model.
+model = transformers.AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-large")
+
+# Define a function to generate text.
+def generate_text(text):
+  """Generates text based on a given prompt."""
+
+  # Tokenize the input text.
+  input_ids = model.tokenizer.encode(text, return_tensors="pt")
+
+  # Generate text.
+  output_ids = model.generate(input_ids=input_ids, max_length=100, num_beams=5)
+
+  # Decode the output text.
+  output_text = model.tokenizer.decode(output_ids[0])
+
+  return output_text
+
+# Define the Gradio interface.
+chat_box = gr.inputs.Textbox(label="Chat Box")
+chat_button = gr.Button("Send")
+chat_response = gr.outputs.Textbox(label="Chat Response")
+
+# Connect the inputs and outputs to the generate_text function.
+chat_button.click(generate_text, chat_box, chat_response)
+
+# Launch the Gradio interface.
+interface = gr.Interface([chat_box, chat_button], [chat_response])
+interface.launch()