app.py

import gradio as gr
from ultralytics import YOLO
import torch
import cv2
import numpy as np
import wikipedia
from PIL import Image

# Load YOLO model for tree detection
yolo_model = YOLO("yolov8n.pt")

# Load MiDaS depth model
midas = torch.hub.load("intel-isl/MiDaS", "MiDaS_small")
midas.to("cpu").eval()
midas_transforms = torch.hub.load("intel-isl/MiDaS", "transforms").small

def estimate_tree_height(image):
    # Convert image to OpenCV format
    image = np.array(image)
    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

    # Object Detection (Tree)
    results = yolo_model(image_rgb)
    boxes = results[0].boxes.xyxy.cpu().numpy()  # Get bounding boxes
    labels = results[0].boxes.cls.cpu().numpy()

    tree_boxes = [box for box, label in zip(boxes, labels) if int(label) == 0]  # class 0 usually means 'person/tree'

    if not tree_boxes:
        return "No tree detected", None, None

    x1, y1, x2, y2 = tree_boxes[0]
    tree_crop = image[int(y1):int(y2), int(x1):int(x2)]

    # Depth estimation
    input_tensor = midas_transforms(Image.fromarray(image_rgb)).to("cpu")
    with torch.no_grad():
        depth_map = midas(input_tensor.unsqueeze(0))[0]
        depth_resized = torch.nn.functional.interpolate(
            depth_map.unsqueeze(0),
            size=image_rgb.shape[:2],
            mode="bicubic",
            align_corners=False
        ).squeeze().cpu().numpy()

    avg_depth = np.mean(depth_resized[int(y1):int(y2), int(x1):int(x2)])
    estimated_height_m = avg_depth * 1.8  # arbitrary scaling for demo

    # Wikipedia summary (simulate species info)
    try:
        summary = wikipedia.summary("tree", sentences=2)
    except Exception:
        summary = "Tree species information not available."

    return f"Estimated Tree Height: {estimated_height_m:.2f} meters", Image.fromarray(tree_crop), summary

# Gradio Interface
demo = gr.Interface(
    fn=estimate_tree_height,
    inputs=gr.Image(type="pil"),
    outputs=[
        gr.Textbox(label="Tree Height Estimate"),
        gr.Image(label="Detected Tree"),
        gr.Textbox(label="Tree Species Info")
    ],
    title="🌳 Tree Measurement App",
    description="Capture a tree image to estimate its height and get basic species info."
)

demo.launch()