Update app.py

app.py

@@ -39,7 +39,7 @@ from tensorflow.keras.preprocessing import image
 import numpy as np
 
 # Load the trained model
-model = r'./model12_acc99_kera.h5'
+model = tf.keras.models.load_model('./model12_acc99_kera.h5')
 
 # Define the class names
 classes = ['Colon Adenocarcinoma', 'Colon Benign Tissue', 'Lung Adenocarcinoma', 'Lung Benign Tissue', 'Lung Squamous Cell Carcinoma']
@@ -48,27 +48,25 @@ classes = ['Colon Adenocarcinoma', 'Colon Benign Tissue', 'Lung Adenocarcinoma',
 def predict(img):
     # Resize and preprocess the image
     img = img.resize((224, 224))
-    # img_array = image.img_to_array(img)
-    # img_array = np.expand_dims(img_array, axis=0)
-    # img_array = img_array / 255.0  # Normalize the image
-    # img = image.resize((224,224))
-    img_array = tf.keras.preprocessing.image.img_to_array(img)
-    img_array = tf.expand_dims(img_array, 0)
-
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    
     # Make predictions
     predictions = model.predict(img_array)
-    print(f"Predictions: {predictions}")  # Debug: Print the raw prediction values
-
     predicted_class_index = np.argmax(predictions[0])
     predicted_class = classes[predicted_class_index]
     
-    return predicted_class
+    # Return the predicted class and the raw predictions
+    return predicted_class, predictions[0]
 
 # Create a Gradio interface
 iface = gr.Interface(
     fn=predict, 
     inputs=gr.Image(type='pil'), 
-    outputs=gr.Textbox(label="Prediction"), 
+    outputs=[
+        gr.Textbox(label="Prediction"),
+        gr.Label(label="Raw Predictions")
+    ], 
     title="Lung and Colon Cancer Detection",
     description="Upload an image of histopathological tissue to detect if it is a type of lung or colon cancer."
 )