import gradio as gr
import pandas as pd
import os
import cv2
import tempfile

# --- Paths ---
GT_FILE = "data/train_logs.csv"  # Ground truth lens changes
LEADERBOARD_FILE = "leaderboard.csv"

# Load ground truth
if os.path.exists(GT_FILE):
    gt_df = pd.read_csv(GT_FILE)
else:
    gt_df = pd.DataFrame(columns=["frame", "lens"])

# Initialize leaderboard if not exists
if not os.path.exists(LEADERBOARD_FILE):
    pd.DataFrame(columns=["Name", "Score"]).to_csv(LEADERBOARD_FILE, index=False)

# --- Simple lens detection placeholder ---
# Replace with your actual detection logic
def detect_lens_changes(video_path):
    cap = cv2.VideoCapture(video_path)
    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    preds = pd.DataFrame({"frame": range(total_frames), "lens": ["A"]*total_frames})
    cap.release()
    return preds

# --- Evaluation function ---
def evaluate_video(video_file, name):
    try:
        tmp_video = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
        tmp_video.write(video_file.read())
        tmp_video.close()

        pred_df = detect_lens_changes(tmp_video.name)

        merged = pd.merge(gt_df, pred_df, on="frame", how="left")
        if merged.empty or "lens_x" not in merged.columns or "lens_y" not in merged.columns:
            return "Error: Ground truth or prediction missing columns.", pd.read_csv(LEADERBOARD_FILE)

        merged.fillna("", inplace=True)
        score = (merged["lens_x"] == merged["lens_y"]).mean()
        score = round(float(score), 4)

        # Update leaderboard
        lb_df = pd.read_csv(LEADERBOARD_FILE)
        lb_df.loc[len(lb_df)] = [name, score]
        lb_df = lb_df.sort_values("Score", ascending=False).reset_index(drop=True)
        lb_df.to_csv(LEADERBOARD_FILE, index=False)

        return f"Your Score: {score}", lb_df

    except Exception as e:
        return f"Error: {str(e)}", pd.read_csv(LEADERBOARD_FILE)

# --- Instructions ---
instructions = """
# 🏆 Video Lens Detection Hackathon

**Instructions:**
1. Download dataset from `data/` folder.
2. Videos contain eye test recordings.
3. Train your model locally to detect lens changes per frame.
4. Upload your video predictions here.
5. Enter your name to appear on the leaderboard.
"""

# --- Gradio UI ---
with gr.Blocks() as demo:
    gr.Markdown(instructions)

    with gr.Row():
        name_input = gr.Textbox(label="Your Name")
        video_input = gr.File(label="Upload your video", file_types=[".mp4"])

    output_text = gr.Textbox(label="Score / Error", interactive=False)
    leaderboard = gr.Dataframe(pd.read_csv(LEADERBOARD_FILE), label="Leaderboard")

    submit_btn = gr.Button("Submit")
    submit_btn.click(
        fn=evaluate_video,
        inputs=[video_input, name_input],
        outputs=[output_text, leaderboard]
    )

demo.launch()