Create app.py

app.py

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+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()