app21

app.py

@@ -36,9 +36,36 @@ from huggingface_hub import from_pretrained_keras
 
 import tensorflow as tf
 from tensorflow import keras
+from PIL import Image
 
 # Cell 1: Image Classification Model
-model1 = from_pretrained_keras("rocioadlc/EfficientNetV2L")
+model1 = from_pretrained_keras("ALVHB95/finalsupermodelofthedestiny")
+
+# Define class labels
+class_labels = ['cardboard', 'compost', 'glass', 'metal', 'paper', 'plastic', 'trash']
+
+# Function to predict image label and score
+def predict_image(input):
+    # Resize the image to the size expected by the model
+    image = input.resize((224, 224))
+    # Convert the image to a NumPy array
+    image_array = tf.keras.preprocessing.image.img_to_array(image)
+    # Normalize the image
+    image_array /= 255.0
+    # Expand the dimensions to create a batch
+    image_array = tf.expand_dims(image_array, 0)
+    # Predict using the model
+    predictions = model1.predict(image_array)
+    
+    # Get the predicted class label
+    predicted_class_index = tf.argmax(predictions, axis=1).numpy()[0]
+    predicted_class_label = class_labels[predicted_class_index]
+    
+    # Get the confidence score of the predicted class
+    confidence_score = predictions[0][predicted_class_index]
+    
+    # Return input image path, predicted class label, and confidence score
+    return input, {predicted_class_label: confidence_score}
 
 def predict_image(input_img):
     predictions = model1.predict(input_img)
@@ -47,7 +74,7 @@ def predict_image(input_img):
 image_gradio_app = gr.Interface(
     fn=predict_image,
     inputs=gr.Image(label="Select waste candidate", sources=['upload', 'webcam'], type="pil"),
-    outputs=[gr.Image(label="Processed Image"), gr.Label(label="Result", num_top_classes=2)],
+    outputs=[gr.Image(label="Processed Image"), gr.Label(label="Result")],
     title="What kind of waste do you have?",
 )