Create app.py

app.py

@@ -1,34 +1,27 @@
 import gradio as gr
 from tensorflow.keras.models import load_model
 import numpy as np
+import os
 
-# Load the model
-model = load_model(r"C:\Users\uSeR\Documents\ai-tutor-ruangguru\project\waste_classifier_model.keras")
+# Load the model (ensure it's in the same directory as app.py)
+model_path = os.path.join(os.path.dirname(__file__), 'waste_classifier_model.keras')
+model = load_model(model_path)
 
 # Prediction function
 def classify_image(image):
-    # Ensure the image is in the expected format
     if image is None:
         return "No image provided."
-
-    # Convert the image to a numpy array
+    
     image = np.array(image)
 
-    # Check if the image has the expected shape and resize if necessary
     if image.shape != (128, 128, 3):
-        image = np.resize(image, (128, 128, 3))  # Resize the image to (128, 128, 3)
+        image = np.resize(image, (128, 128, 3))
 
-    # Normalize the image
     image = image / 255.0
-
-    # Add batch dimension
     image = np.expand_dims(image, axis=0)
 
-    # Make prediction
     prediction = model.predict(image)
-
-    # Interpret the prediction
-    class_label = 'Organic' if prediction[0][0] > 0.5 else 'Recycleable'  # Adjust labels based on your model's output
+    class_label = 'Organic' if prediction[0][0] > 0.5 else 'Recycleable'
     return class_label
 
 # Create Gradio interface