app.py

import gradio as gr
from transformers import pipeline

# Load your Hugging Face model
# pipe = pipeline("text-classification", model="davidgaofc/TechDebtClassifier")
  # Replace 'gpt2' with your model
def pipe(temp, temp2):
    return temp

def predict(input_text):
    # Generate output using the model
    output = pipe(input_text, max_length=2000)  # Adjust parameters as needed
    return output[0]['generated_text']

# Create the Gradio interface
interface = gr.Interface(fn=predict,
                         inputs=gr.inputs.Textbox(lines=2, placeholder="Type something here..."),
                         outputs='text',
                         title="Hugging Face Model Inference",
                         description="Type in some text and see how the model responds!")

if __name__ == "__main__":
    interface.launch()