import gradio as gr
from transformers import pipeline

# Your model repo
model_id = "globalbrandon/autotrain-empowerai-1"

# Load pipeline (auto-detects type based on model)
classifier = pipeline("text-classification", model=model_id)

# Define prediction function
def predict(text):
    try:
        result = classifier(text)[0]
        label = result["label"]
        score = round(result["score"] * 100, 2)
        return f"Prediction: {label} ({score}%)"
    except Exception as e:
        return f"Error: {str(e)}"

# Gradio interface
demo = gr.Interface(
    fn=predict,
    inputs=gr.Textbox(lines=4, placeholder="Enter your text here..."),
    outputs=gr.Textbox(label="Model Prediction"),
    title="Text Classifier",
    description="This app uses a Hugging Face AutoTrain model to classify text."
)

demo.launch()