import gradio as gr
from transformers import pipeline

# Load Hugging Face sentiment model
SENTIMENT_MODEL_NAME = "distilbert/distilbert-base-uncased-finetuned-sst-2-english"
sentiment_classifier = pipeline("sentiment-analysis", model=SENTIMENT_MODEL_NAME)

# Function for prediction
def analyze_sentiment(text):
    if not text.strip():
        return "⚠️ Please enter some text."
    result = sentiment_classifier(text)[0]
    label = result["label"]
    score = result["score"]

    # Color-coded emoji results
    if label == "POSITIVE":
        return f"😊 Positive ({score:.2f})"
    else:
        return f"😡 Negative ({score:.2f})"

# Custom CSS to style the app
custom_css = """
#component-0 textarea {
    font-size: 16px !important;
    border: 2px solid #4CAF50 !important;
    border-radius: 10px !important;
    padding: 10px !important;
}
button {
    background-color: #4CAF50 !important;
    color: white !important;
    font-size: 16px !important;
    border-radius: 8px !important;
    padding: 10px 20px !important;
}
"""

# Gradio Interface
demo = gr.Interface(
    fn=analyze_sentiment,
    inputs=gr.Textbox(lines=4, placeholder="Type your feedback here..."),
    outputs=gr.Textbox(label="Sentiment Result"),
    title="✨ AI Sentiment Analysis App",
    description="Enter text below to analyze its sentiment (Positive/Negative) powered by Hugging Face Transformers.",
    theme="default",
    css=custom_css
)

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