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