import gradio as gr
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Load dataset
url = "https://raw.githubusercontent.com/laxmimerit/All-CSV-ML-Data-Files-Download/master/IMDB-Movie-Data.csv"
df = pd.read_csv(url)

# --- Visualization Functions ---
def show_data(n):
    """Show top n rows of the dataset."""
    return df.head(n)

def plot_visualizations(choice):
    """Generate different plots based on user choice."""
    plt.figure(figsize=(8, 5))
    if choice == "Rating Distribution":
        sns.histplot(df["Rating"], bins=15, kde=True)
        plt.title("Distribution of Movie Ratings")
    elif choice == "Top 10 Directors":
        top_directors = df["Director"].value_counts().head(10)
        sns.barplot(x=top_directors.values, y=top_directors.index)
        plt.title("Top 10 Directors by Movie Count")
    elif choice == "Genre Frequency":
        all_genres = df["Genre"].str.split(",").explode().str.strip()
        top_genres = all_genres.value_counts().head(10)
        sns.barplot(x=top_genres.values, y=top_genres.index)
        plt.title("Top 10 Movie Genres")
    elif choice == "Revenue vs Rating":
        sns.scatterplot(x="Rating", y="Revenue (Millions)", data=df)
        plt.title("Revenue vs Rating")
    plt.tight_layout()
    return plt.gcf()

# --- Gradio Interface ---
with gr.Blocks() as demo:
    gr.Markdown("# 🎬 IMDB Movie Data Explorer")
    gr.Markdown("Explore insights from the IMDB Movie Dataset with visualizations.")
    
    with gr.Tab("📊 View Data"):
        n_slider = gr.Slider(5, 50, value=10, step=5, label="Select number of rows")
        data_output = gr.Dataframe(label="Dataset Preview")
        n_slider.change(show_data, inputs=n_slider, outputs=data_output)
        
    with gr.Tab("📈 Visualize"):
        choice = gr.Radio(
            ["Rating Distribution", "Top 10 Directors", "Genre Frequency", "Revenue vs Rating"],
            label="Select Visualization",
        )
        plot_output = gr.Plot(label="Visualization")
        choice.change(plot_visualizations, inputs=choice, outputs=plot_output)
    
    gr.Markdown("Made with ❤️ using Gradio + Seaborn")

demo.launch()