app.py

import gradio as gr
from transformers import pipeline

# Load a text classification model
# Replace with your model's path or Hugging Face model name
model = pipeline(task="text-classification", model="adrienhongcs/clara-0")

def predict(input_text):
    # Get predictions from the model
    predictions = model(input_text)
    
    # Extract the label and score
    label = predictions[0]["label"]
    score = predictions[0]["score"]
    
    # Return the label and score
    return label, score

# Create the Gradio interface
gradio_app = gr.Interface(
    fn=predict,  # Function to call
    inputs=gr.Textbox(label="Enter a deduction backup doc text"),  # Text input
    outputs=[
        gr.Textbox(label="Predicted Reason"),  # Output for the label
        gr.Number(label="Confidence Score")   # Output for the score
    ],
    title="Clara the reason classifier (clara-0, trained on 8000 rows)",
    description="Enter a deduction backup (as text) to classify it and get the predicted label and confidence score."
)

# Launch the app
if __name__ == "__main__":
    gradio_app.launch()