| # app.py | |
| from transformers import pipeline | |
| import gradio as gr | |
| # Load the pretrained model using the pipeline | |
| pipe = pipeline("text-generation", model="hamaadayubkhan/Psychologist") | |
| # Define a function that will take user input and use the model to generate text | |
| def generate_text(input_text): | |
| response = pipe(input_text, max_length=100, num_return_sequences=1) | |
| return response[0]['generated_text'] | |
| # Set up Gradio interface | |
| interface = gr.Interface( | |
| fn=generate_text, | |
| inputs="text", | |
| outputs="text", | |
| title="Psychologist Text Generation", | |
| description="Enter a prompt and the model will generate a continuation of it." | |
| ) | |
| # Launch the app | |
| interface.launch() | |