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import os
import io
import pandas as pd
import numpy as np
import gradio as gr

# Matplotlib only (no seaborn), single chart per plot, no explicit colors
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from PIL import Image  # <-- add PIL for returning PIL.Image

DEFAULT_CSV = "Trend_of_Top_10_Hard_Skills.csv"  # Place at repo root

def load_dataframe(file):
    """Load CSV from uploaded file or the default CSV on repo root"""
    if file is not None:
        return pd.read_csv(file.name if hasattr(file, "name") else file)
    if os.path.exists(DEFAULT_CSV):
        return pd.read_csv(DEFAULT_CSV)
    raise gr.Error("No CSV provided and default file not found. Please upload a CSV.")

def prepare_dataframe(df):
    """
    Expect columns: YearMonth, <skill1>, <skill2>, ...
    YearMonth as YYYY-MM, convert to datetime for sorting, back to string for ticks.
    """
    if "YearMonth" not in df.columns:
        raise gr.Error("CSV must have a 'YearMonth' column.")
    dt = pd.to_datetime(df["YearMonth"], format="%Y-%m", errors="coerce")
    if dt.isna().any():
        dt = pd.to_datetime(df["YearMonth"], errors="coerce")
    df = df.copy()
    df["_dt"] = dt
    df = df.sort_values("_dt")
    for c in df.columns:
        if c not in ["YearMonth", "_dt"]:
            df[c] = pd.to_numeric(df[c], errors="coerce")
    return df

def list_skill_columns(df):
    return [c for c in df.columns if c not in ["YearMonth", "_dt"]]

def apply_smoothing(series, window):
    if window and window > 1:
        return series.rolling(window=window, min_periods=1).mean()
    return series

def normalize_series(series, mode):
    if mode == "none":
        return series
    s = series.copy()
    if mode == "min-max (per skill)":
        mn, mx = s.min(), s.max()
        return (s - mn) / (mx - mn) if mx > mn else s.fillna(0.0)
    if mode == "z-score (per skill)":
        mu, sd = s.mean(), s.std(ddof=0)
        return (s - mu) / sd if sd > 0 else s.fillna(0.0)
    return series

def plot_lines(df, selected_skills, smoothing_window, normalize_mode, show_markers, y_label):
    if not selected_skills:
        raise gr.Error("Please select at least one skill.")

    x_dt = df["_dt"]
    x_labels = df["YearMonth"].astype(str).tolist()

    fig = plt.figure(figsize=(10, 5.5))
    ax = fig.add_subplot(111)

    for skill in selected_skills:
        if skill not in df.columns:
            continue
        y = df[skill]
        y = apply_smoothing(y, smoothing_window)
        y = normalize_series(y, normalize_mode)
        if show_markers:
            ax.plot(x_dt, y, marker="o", label=skill)
        else:
            ax.plot(x_dt, y, label=skill)

    ax.set_xlabel("Year-Month")
    ax.set_ylabel(y_label if y_label.strip() else ("Normalized value" if normalize_mode!="none" else "Frequency"))
    ax.set_title("Trend of Selected Hard Skills")
    ax.legend(loc="best")
    ax.grid(True, which="both", axis="both", alpha=0.35)

    ax.set_xticks(x_dt)
    ax.set_xticklabels(x_labels, rotation=45, ha="right")

    fig.tight_layout()
    buf = io.BytesIO()
    fig.savefig(buf, format="png", dpi=160, bbox_inches="tight")
    plt.close(fig)
    buf.seek(0)
    # Return PIL Image instead of BytesIO to satisfy gr.Image postprocess
    return Image.open(buf)

def run(csv_file, selected_skills, smoothing_window, normalize_mode, show_markers, y_label):
    df = load_dataframe(csv_file)
    df = prepare_dataframe(df)
    available = list_skill_columns(df)
    if not selected_skills:
        selected_skills = available
    selected_skills = [s for s in selected_skills if s in available]
    img = plot_lines(df, selected_skills, smoothing_window, normalize_mode, show_markers, y_label)
    preview_cols = ["YearMonth"] + selected_skills
    preview = df[preview_cols].reset_index(drop=True)
    return img, gr.update(choices=available, value=selected_skills), preview

with gr.Blocks(title="Hard Skills Trend Line Chart") as demo:
    gr.Markdown("# Hard Skills Trend — Line Chart\\nUpload a CSV or place **Trend_of_Top_10_Hard_Skills.csv** in the repo root.")

    with gr.Row():
        with gr.Column(scale=1):
            csv_file = gr.File(label="Upload CSV (optional)", file_count="single", file_types=[".csv"])
            selected_skills = gr.CheckboxGroup(choices=[], label="Select skills to plot")
            smoothing_window = gr.Slider(1, 6, value=1, step=1, label="Smoothing (moving average window in months)")
            normalize_mode = gr.Dropdown(choices=["none", "min-max (per skill)", "z-score (per skill)"], value="none", label="Normalize")
            show_markers = gr.Checkbox(value=True, label="Show markers on lines")
            y_label = gr.Textbox(value="", label="Y-axis label (optional)")
            btn = gr.Button("Plot", variant="primary")

        with gr.Column(scale=1):
            out_img = gr.Image(label="Line Chart", type="pil")  # explicitly set type=pil
            out_table = gr.Dataframe(label="Data preview")

    btn.click(
        fn=run,
        inputs=[csv_file, selected_skills, smoothing_window, normalize_mode, show_markers, y_label],
        outputs=[out_img, selected_skills, out_table]
    )

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