import pandas as pd
import gradio as gr

# Load dataset
df = pd.read_csv("hf_app_data.csv")

# Make sure product_name is text
df["product_name"] = df["product_name"].astype(str)

# Function to analyze product
def analyze_product(product_query):
    product_query = str(product_query).strip()

    if not product_query:
        return "Please enter a product ID or keyword."

    results = df[df["product_name"].str.contains(product_query, case=False, na=False)]

    if results.empty:
        return "Product not found. Try another ID or keyword."

    top = results.sort_values(by="popularity_score", ascending=False).head(3)

    output = f"Found {len(results)} match(es). Showing top 3:\n\n"

    for _, row in top.iterrows():
        output += (
            f"Product: {row['product_name']}\n"
            f"Popularity Score: {row['popularity_score']} ({row['popularity_class']})\n"
            f"Rating: {row['avg_rating']}\n"
            f"Sentiment: {row['avg_sentiment']}\n"
            f"Monthly Sales: {row['avg_monthly_sales']}\n"
            f"Price: EUR {row['avg_price']}\n"
            f"Recommendation: {row['recommendation']}\n"
            f"{'-'*40}\n"
        )

    return output


# Gradio UI
iface = gr.Interface(
    fn=analyze_product,
    inputs=gr.Textbox(
        label="Enter Product ID (e.g. B003U925C4)",
        placeholder="Example: B003U925C4"
    ),
    outputs=gr.Textbox(
        label="Analysis",
        lines=18
    ),
    title="Product Popularity Analyzer",
    description="Analyze product popularity based on sentiment, ratings, and sales data."
)

iface.launch()