| # original: | |
| #import gradio as gr | |
| #gr.Interface.load("models/sileod/deberta-v3-base-tasksource-nli").launch() | |
| # chatGPT prompt1: Rewrite this program in python and gradio to load an example file with two input text fields and output of the classification field. | |
| import gradio as gr | |
| def classify_text(text1, text2): | |
| # Load pre-trained model | |
| model = gr.load_model("models/sileod/deberta-v3-base-tasksource-nli") | |
| # Perform classification on input text | |
| output = model.predict([text1, text2])[0] | |
| return output | |
| # Create input fields | |
| input_text1 = gr.Textbox(label="Input Text 1") | |
| input_text2 = gr.Textbox(label="Input Text 2") | |
| # Create output field | |
| output_text = gr.Textbox(label="Classification Output") | |
| # Create Gradio interface | |
| gr.Interface(classify_text, | |
| inputs=[input_text1, input_text2], | |
| outputs=output_text, | |
| examples=[["Example Text 1", "Example Text 2"]]).launch() | |