Update app.py

app.py

@@ -7,54 +7,56 @@ from tensorflow.keras.preprocessing import image
 from tensorflow.keras.models import load_model
 from tensorflow.keras.applications.efficientnet import preprocess_input
 
-# Load the trained model
+
 model = load_model("efficent_net224B0.h5")
 
-# Define the classes
+
 waste_labels = {0: 'Fibres', 1: 'Nanowires', 2: 'Particles', 3: 'Powder'}
 
-# Define the Gradio interface
+
 def classify_image(pil_image):
-    # Convert PIL.Image to Numpy array
+    
     img = image.img_to_array(pil_image)
     
-    # Resize to the model's expected input size
+ 
     img = tf.image.resize(img, (224, 224))
     
-    # Expand dimensions to create a batch size of 1
+    
     img = np.expand_dims(img, axis=0)
     
-    # Preprocess the input for the EfficientNet model
+    
     img = preprocess_input(img)
     
-    # Make prediction
+
     prediction = model.predict(img)
     
-    # Get predicted class and confidence
+    
     predicted_class = np.argmax(prediction)
     predicted_class_name = waste_labels[predicted_class]
     confidence = prediction[0, np.argmax(prediction)]
 
-    # Get class names and probabilities for all classes
+    
     class_names = list(waste_labels.values())
     probabilities = prediction[0]
 
-    # Plot bar chart and save it to a file
+    print(class_names)
+    print(probabilities)
+   
     plt.bar(class_names, probabilities, color='blue')
     plt.xlabel('Waste Classes')
     plt.ylabel('Probability')
     plt.title('Prediction Probabilities')
     plt.savefig('prediction_plot.png')
 
-    # Prepare output text with all class probabilities
+    
     output_text = f"Predicted Class: {predicted_class_name}, Confidence: {confidence:.4f}\n"
     for class_name, prob in zip(class_names, probabilities):
         output_text += f"{class_name}: {prob:.4f}\n"
 
     return output_text, 'prediction_plot.png'
 
-# Create the Gradio interface
+
 iface = gr.Interface(fn=classify_image, inputs="image", outputs=["text", "image"],live=True)
 
-# Launch the Gradio interface
+
 iface.launch()