Update app.py

app.py

@@ -1,23 +1,63 @@
+# import gradio as gr
+# import tensorflow as tf
+# from tensorflow.keras.preprocessing import image
+# import numpy as np
+
+# # Load the trained model
+# 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']
+
+# # Function to preprocess the uploaded image and make predictions
+# def predict(img):
+#     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
+
+#     predictions = model.predict(img_array)
+#     predicted_class = classes[np.argmax(predictions[0])]
+    
+#     return predicted_class
+
+# # Create a Gradio interface
+# iface = gr.Interface(
+#     fn=predict, 
+#     inputs=gr.Image(type='pil'), 
+#     outputs=gr.Textbox(label="Prediction"), 
+#     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."
+# )
+
+# # Launch the interface
+# iface.launch()
+
 import gradio as gr
 import tensorflow as tf
 from tensorflow.keras.preprocessing import image
 import numpy as np
 
 # Load the trained model
-model = tf.keras.models.load_model('./model12_acc99_kera.h5')
+model = tf.keras.models.load_model('path/to/your/saved_model')
 
 # Define the class names
 classes = ['Colon Adenocarcinoma', 'Colon Benign Tissue', 'Lung Adenocarcinoma', 'Lung Benign Tissue', 'Lung Squamous Cell Carcinoma']
 
 # Function to preprocess the uploaded image and make predictions
 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
 
+    # Make predictions
     predictions = model.predict(img_array)
-    predicted_class = classes[np.argmax(predictions[0])]
+    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
 
@@ -32,4 +72,3 @@ iface = gr.Interface(
 
 # Launch the interface
 iface.launch()
-