app.py

import gradio as gr
import os
import uuid
from video_process import process_video
import cv2

MODEL_PATH = "last.pt"  # Your YOLO model
CONF_THRESHOLD = 0.2
PROCESSED_DIR = "processed_video"
DEMO_VIDEO = "demo.mp4"  # Demo video file name

os.makedirs(PROCESSED_DIR, exist_ok=True)

# ---------------- VIDEO VALIDATION ----------------
def validate_video(video_path):
    try:
        cap = cv2.VideoCapture(video_path)
        if not cap.isOpened():
            return False, "Cannot open video file"
        ret, frame = cap.read()
        cap.release()
        if not ret:
            return False, "Cannot read video frames"
        return True, "Video is valid"
    except Exception as e:
        return False, f"Video validation error: {str(e)}"

# ---------------- PROCESS VIDEO ----------------
def process_uploaded_video(video_path, confidence_threshold=0.2):
    if video_path is None:
        return "❌ No video provided", None

    unique_id = str(uuid.uuid4())[:8]
    output_filename = f"processed_{unique_id}.mp4"
    output_path = os.path.join(PROCESSED_DIR, output_filename)

    try:
        is_valid, msg = validate_video(video_path)
        if not is_valid:
            return f"❌ Input video error: {msg}", None

        print(f"Processing: {video_path}")

        process_video(
            input_video_path=video_path,
            output_video_path=output_path,
            model_path=MODEL_PATH,
            conf_threshold=confidence_threshold
        )

        if not os.path.exists(output_path):
            return "❌ Output video not created", None

        file_size = os.path.getsize(output_path)
        if file_size == 0:
            return "❌ Output video empty", None

        cap = cv2.VideoCapture(output_path)
        frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
        fps = cap.get(cv2.CAP_PROP_FPS)
        cap.release()

        return f"✅ Done! Frames: {frame_count}, FPS: {fps:.2f}, Size: {file_size/1024/1024:.2f} MB", output_path

    except Exception as e:
        return f"❌ Processing failed: {str(e)}", None

# ---------------- CLEAR STORAGE ----------------
def clear_processed_videos():
    try:
        for f in os.listdir(PROCESSED_DIR):
            os.remove(os.path.join(PROCESSED_DIR, f))
        return "✅ Cleared processed videos"
    except Exception as e:
        return f"❌ Error: {str(e)}"

# ---------------- LOAD DEMO VIDEO ----------------
def load_demo():
    return DEMO_VIDEO

# ---------------- UI ----------------
with gr.Blocks(title="Agriculture Land Video Segmentation", theme=gr.themes.Soft()) as demo:

    gr.Markdown("""
    # 🎥 Agriculture Land Video Segmentation (Drone)
    Upload a video or try the **preloaded demo video**.
    """)

    with gr.Row():
        with gr.Column():
            video_input = gr.Video(
                label="📁 Upload Video (Demo loaded by default)",
                value=DEMO_VIDEO,   # Auto load demo video
                height=300,
                show_download_button=True
            )

            confidence_slider = gr.Slider(
                minimum=0.1,
                maximum=1.0,
                value=0.2,
                step=0.05,
                label="🎯 Confidence Threshold"
            )

            with gr.Row():
                process_btn = gr.Button("🚀 Process Video", variant="primary")
                demo_btn = gr.Button("🎬 Load Demo Video")
                clear_btn = gr.Button("🗑️ Clear Storage")

        with gr.Column():
            status_output = gr.Textbox(label="📊 Status", max_lines=5)
            video_output = gr.Video(
                label="🎬 Processed Video",
                height=300,
                show_download_button=True
            )

    # Buttons
    process_btn.click(
        fn=process_uploaded_video,
        inputs=[video_input, confidence_slider],
        outputs=[status_output, video_output],
        show_progress=True
    )

    demo_btn.click(fn=load_demo, outputs=video_input)
    clear_btn.click(fn=clear_processed_videos, outputs=status_output)

    gr.Markdown("""
    ### 💡 Tips
    - Keep video < 100MB
    - Short videos process faster
    - Lower confidence = more detections
    """)

# ---------------- RUN ----------------
if __name__ == "__main__":
    demo.launch(debug=True, show_error=True)