import gradio as gr
from transformers import pipeline

# Load models
summarizer = pipeline(
    "summarization",
    model="Manish014/review-summariser-gpt-config1",
    tokenizer="Manish014/review-summariser-gpt-config1",
    device=0  # Use GPU if available
)

sentiment_analyzer = pipeline("sentiment-analysis")

# Inference function
def analyze_review(text):
    if not text.strip():
        return "❗ Please enter a product review.", "❗ Sentiment unavailable."
    
    summary = summarizer(
        text,
        max_length=80,
        min_length=10,
        num_beams=4,
        early_stopping=True,
        length_penalty=1.2
    )[0]["summary_text"]

    sentiment = sentiment_analyzer(text)[0]
    sentiment_label = f"{sentiment['label']} ({round(sentiment['score'] * 100, 2)}%)"

    return summary, sentiment_label

# Example inputs
examples = [
    ["This product leaks water and smells like burnt plastic."],
    ["Absolutely loved the screen resolution and battery life."],
    ["Worst purchase I've made. Do not recommend at all."],
    ["The headphones are okay. Battery is good but fit is not comfortable."],
    ["The fan is extremely loud and doesn't cool much."]
]

# Build UI
with gr.Blocks(theme=gr.themes.Base()) as demo:
    gr.Markdown("## 📝 Review Summariser GPT - Config 1")
    gr.Markdown("Enter a detailed product review below to receive a helpful summary ✂️ and predicted sentiment 📊.")

    with gr.Row():
        review_input = gr.Textbox(label="🗣️ Product Review", lines=5, placeholder="Write your review here...")

    with gr.Row():
        summary_output = gr.Textbox(label="✂️ Summary", lines=2)
        sentiment_output = gr.Textbox(label="📊 Sentiment", lines=1)

    with gr.Row():
        analyze_btn = gr.Button("🔍 Analyze")
        clear_btn = gr.Button("🧹 Clear")

    analyze_btn.click(analyze_review, inputs=review_input, outputs=[summary_output, sentiment_output])
    clear_btn.click(lambda: ("", "", ""), outputs=[review_input, summary_output, sentiment_output])

    gr.Examples(examples=examples, inputs=review_input, label="🔁 Try Example Reviews")

    with gr.Accordion("ℹ️ About this App", open=False):
        gr.Markdown(
            """
            This application uses a fine-tuned T5 model to summarize lengthy product reviews into short summaries and also classifies the sentiment as Positive or Negative.
            - Model: `Manish014/review-summariser-gpt-config1`
            - Summarization by 🤗 Transformers
            - Sentiment by `distilbert-base-uncased-finetuned-sst-2-english`
            """
        )

# Run app
if __name__ == "__main__":
    demo.launch()