Upload app.py

app.py

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+import gradio as gr
+import torch
+import cv2
+import numpy as np
+from PIL import Image
+from ultralytics import YOLO
+
+# Load YOLO model
+model = YOLO("best.pt").to("cpu")
+
+def detect_and_crop(image):
+    """Detect objects using YOLO and crop them from the image."""
+    image_np = np.array(image)
+    results = model(image_np, conf=0.85, device='cpu')
+    
+    cropped_images = {}
+    for result in results:
+        for box in result.boxes:
+            x1, y1, x2, y2 = map(int, box.xyxy[0])
+            class_name = model.names[int(box.cls[0])]
+            cropped = image_np[y1:y2, x1:x2]
+            cropped_images[class_name] = Image.fromarray(cropped)
+    
+    return cropped_images
+
+def predict(image):
+    """Process image: detect objects and crop them."""
+    cropped_images = detect_and_crop(image)
+    return list(cropped_images.values()) if cropped_images else image
+
+# Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs="image",
+    outputs="image",
+    title="License Field Detection & Cropping"
+)
+
+iface.launch()