import gradio as gr
from transformers import pipeline

# Load the classification pipeline
classifier = pipeline(
    "sentiment-analysis", 
    model="Karzan/user_profile_skills_model", 
    return_all_scores=True, 
    top_k=10
)

# Define the prediction function
def classify_text_with_bars(text):
    # Perform classification
    results = classifier(text)
    # Prepare data for bar chart
    labels = []
    scores = []
    for result in results:
        for item in result:
            labels.append(item["label"])
            scores.append(item["score"])
    return {label: score for label, score in zip(labels, scores)}

# Create the Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("# Text Classification with Bar Chart Output")
    gr.Markdown("Enter text to classify, and view predictions with their scores in a bar chart.")
    with gr.Row():
        with gr.Column():
            input_text = gr.Textbox(label="Input Text", lines=3, placeholder="Type something...")
            classify_button = gr.Button("Classify")
        with gr.Column():
            output_chart = gr.Label(label="Classification Results (Bar Chart)", type="plot")

    classify_button.click(classify_text_with_bars, inputs=input_text, outputs=output_chart)

# Launch the app
demo.launch()