import gradio as gr
from transformers import pipeline

# Load the model from Hugging Face Hub
model_repo = "Ahmed-El-Sharkawy/my-sentiment-model"  # Replace with your actual repo name
nlp_pipeline = pipeline("text-classification", model=model_repo)

# Gradio app function
def classify_text(input_text):
    if input_text.strip() == "":
        return "Please enter some text."
    result = nlp_pipeline(input_text)
    label = result[0]['label']
    score = result[0]['score']

    # Map Hugging Face labels to readable format
    label_map = {'LABEL_0': "Negative", 'LABEL_1': "Positive"}
    mapped_label = label_map.get(label, label)

    return f"Prediction: {mapped_label}, Confidence Score: {score:.4f}"

# Gradio interface
iface = gr.Interface(
    fn=classify_text,
    inputs="text",
    outputs="text",
    title="Text Classification App",
    description="Classify text as Positive (1) or Negative (0)"
)

# Launch the app
iface.launch()