import pandas as pd
import matplotlib.pyplot as plt
import gradio as gr
import os
import zipfile

# ---------- EDA FUNCTION ----------
def run_eda(file):
    df = pd.read_csv(file.name)

    # Basic validation
    if "text" not in df.columns or "player" not in df.columns:
        return "CSV must contain 'player' and 'text' columns.", None

    # Drop missing text
    df = df.dropna(subset=["text"])

    # Create output directory
    os.makedirs("outputs", exist_ok=True)

    # ---------- Dataset Summary ----------
    summary = []
    summary.append(f"Number of rows: {df.shape[0]}")
    summary.append(f"Number of columns: {df.shape[1]}")
    summary.append("\nMissing values:")
    summary.append(str(df.isnull().sum()))

    # ---------- Text Length Analysis ----------
    df["text_length"] = df["text"].str.len()
    stats = df["text_length"].describe()
    summary.append("\nText Length Statistics:")
    summary.append(str(stats))

    # Save summary
    summary_path = "outputs/eda_summary.txt"
    with open(summary_path, "w") as f:
        f.write("\n".join(summary))

    # ---------- Plot: Text Length Distribution ----------
    plt.figure()
    plt.hist(df["text_length"], bins=50)
    plt.xlabel("Text Length (characters)")
    plt.ylabel("Frequency")
    plt.title("Distribution of Reddit Comment Lengths")
    text_len_plot = "outputs/text_length_distribution.png"
    plt.savefig(text_len_plot)
    plt.close()

    # ---------- Plot: Top Players ----------
    plt.figure()
    df["player"].value_counts().head(10).plot(kind="bar")
    plt.xlabel("Player")
    plt.ylabel("Number of Mentions")
    plt.title("Top 10 Most Mentioned NFL Players")
    player_plot = "outputs/top_players.png"
    plt.savefig(player_plot)
    plt.close()

    # ---------- Save Cleaned Dataset ----------
    cleaned_csv = "outputs/cleaned_nfl_data.csv"
    df.to_csv(cleaned_csv, index=False)

    # ---------- Zip Results ----------
    zip_path = "outputs/nfl_eda_results.zip"
    with zipfile.ZipFile(zip_path, "w") as zipf:
        zipf.write(summary_path)
        zipf.write(text_len_plot)
        zipf.write(player_plot)
        zipf.write(cleaned_csv)

    return (
        "\n".join(summary),
        zip_path
    )

# ---------- GRADIO UI ----------
interface = gr.Interface(
    fn=run_eda,
    inputs=gr.File(label="Upload NFL Reddit CSV"),
    outputs=[
        gr.Textbox(label="EDA Summary", lines=20),
        gr.File(label="Download All Results (ZIP)")
    ],
    title="NFL Reddit EDA & NLP Lab Analyzer",
    description=(
        "Upload an NFL Reddit CSV file to perform exploratory data analysis. "
        "This application generates dataset insights, visualizations, and "
        "a downloadable ZIP with results and plots."
    )
)

interface.launch()