Create app.py

app.py

@@ -0,0 +1,53 @@
+import gradio as gr
+import cv2
+import numpy as np
+from tensorflow.keras.models import load_model
+from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
+
+model = load_model('/content/drive/MyDrive/AI SDG 3/klasifikasi_jerawat_model_klmpk2.keras')
+
+acne_labels = {
+    0: 'papules',
+    1: 'nodules',
+    2: 'pustules',
+    3: 'comedones'
+}
+
+def detect_acne(image, threshold=0.5):
+    image_resized = cv2.resize(image, (224, 224))
+    input_data = preprocess_input(np.expand_dims(image_resized, axis=0))
+
+    predictions = model.predict(input_data)
+
+    detections = []
+    for i, prediction in enumerate(predictions[0]):
+        if prediction > threshold:
+            detections.append({
+                'class': acne_labels[i],
+                'confidence': float(prediction)
+            })
+
+    annotated_image = image.copy()
+    if detections:
+        for detection in detections:
+            height, width, _ = image.shape
+            xmin, ymin = int(width * 0.25), int(height * 0.25)
+            xmax, ymax = int(width * 0.75), int(height * 0.75)
+
+            color = (203, 0, 203)  # Purple-red
+            cv2.rectangle(annotated_image, (xmin, ymin), (xmax, ymax), color, 2)
+
+        return annotated_image, f"Detected acne: {detections}"
+    else:
+        return annotated_image, "No acne detected. Congrats!"
+
+interface = gr.Interface(
+    fn=detect_acne,
+    inputs=gr.Image(type="numpy", label="Upload an image"),  
+    outputs=[
+        gr.Image(type="numpy", label="Annotated Image"),     
+        gr.Textbox(label="Detection Result")                
+    ]
+)
+
+interface.launch()