Create app.py

app.py

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+import gradio as gr
+from ultralytics import YOLO
+from PIL import Image, ImageDraw
+import tempfile
+import os
+
+# Load your trained model
+model = YOLO("yolov8n.pt")  # Replace with your trained model .pt path
+
+# Detection function with confidence filter
+def detect_disease(image):
+    # Save input to a temporary file
+    temp = tempfile.NamedTemporaryFile(delete=False, suffix=".png")
+    image.save(temp.name)
+
+    # Run YOLOv8 inference
+    results = model(temp.name)[0]  # Get first result from list
+
+    # Filter predictions with confidence >= 0.5
+    filtered_boxes = []
+    for box in results.boxes.data:
+        x1, y1, x2, y2, score, cls_id = box.tolist()
+        if score >= 0.5:
+            filtered_boxes.append((x1, y1, x2, y2, score, int(cls_id)))
+
+    # Draw boxes on image
+    draw = ImageDraw.Draw(image)
+    class_names = model.names
+
+    for x1, y1, x2, y2, score, cls_id in filtered_boxes:
+        draw.rectangle([x1, y1, x2, y2], outline="red", width=3)
+        draw.text((x1, y1 - 10), f"{class_names[cls_id]}: {score:.2f}", fill="red")
+
+    return image
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=detect_disease,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Image(type="pil"),
+    title="🌿 Plant Disease Detector (YOLOv8)",
+    description="Upload a leaf image. This model will detect plant diseases using YOLOv8. Only results with confidence ≥ 50% are shown."
+)
+
+# Launch app
+interface.launch()