app.py

import gradio as gr
from core_pipeline import extract_frames, detect_trees, plot_detections
import numpy as np

def process_video(video_path):
    if not video_path:
        return None

    frames = extract_frames(video_path)
    results = []

    for i, frame in enumerate(frames[:3]):  # limit to 3 sample frames for preview
        detected, bboxes, confs, labels = detect_trees(frame)
        annotated = plot_detections(detected, bboxes)
        results.append(annotated)

    if results:
        preview = np.hstack(results)
        return preview
    return None

gr.Interface(
    fn=process_video,
    inputs=gr.Video(label="Upload Drone Video", type="filepath"),
    outputs=gr.Image(label="Tree Detections (Sample Frames)"),
    title="🌳 Tree Height Detection from Drone Video",
    description="Upload top-down drone video to detect trees and visualize sample frames. Height estimation is possible if SfM data is provided."
).launch()