Update app.py

app.py

@@ -33,82 +33,11 @@
 # # Launch the interface
 # iface.launch()
 
-# import gradio as gr
-# import tensorflow as tf
-# from tensorflow.keras.preprocessing import image
-# import numpy as np
-# import logging
-
-# # Set up logging
-# logging.basicConfig(level=logging.DEBUG)
-
-# # Initialize the model variable
-# model = None
-
-# # Load the trained model
-# try:
-#     model_path = './model1_kera.h5'
-#     logging.info(f"Loading model from: {model_path}")
-#     model = tf.keras.models.load_model(model_path)
-#     logging.info("Model loaded successfully.")
-# except Exception as e:
-#     logging.error(f"Error loading model: {e}")
-
-# # 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):
-#     global model
-#     try:
-#         logging.debug("Received image for prediction.")
-        
-#         # 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)
-        
-#         logging.debug("Image preprocessed successfully.")
-        
-#         # Ensure the model is loaded
-#         if model is None:
-#             raise ValueError("Model is not loaded properly.")
-        
-#         # Make predictions
-#         predictions = model.predict(img_array)
-#         predicted_class_index = np.argmax(predictions[0])
-#         predicted_class = classes[predicted_class_index]
-        
-#         logging.debug(f"Prediction successful: {predicted_class}")
-        
-#         # Return the predicted class and the raw predictions
-#         return predicted_class, predictions[0].tolist()
-#     except Exception as e:
-#         logging.error(f"Error during prediction: {e}")
-#         # Print the error message in the output
-#         return str(e), str(e)
-
-# # Create a Gradio interface
-# iface = gr.Interface(
-#     fn=predict, 
-#     inputs=gr.Image(type='pil'), 
-#     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."
-# )
-
-# # Launch the interface
-# iface.launch()
 import gradio as gr
 import tensorflow as tf
-from tensorflow.keras.preprocessing import image as keras_image
-from PIL import Image
+from tensorflow.keras.preprocessing import image
 import numpy as np
 import logging
-import os
 
 # Set up logging
 logging.basicConfig(level=logging.DEBUG)
@@ -116,24 +45,14 @@ logging.basicConfig(level=logging.DEBUG)
 # Initialize the model variable
 model = None
 
-# Function to load the model
-def load_model():
-    global model
-    model_path = r'Model1_kera.h5'  # Replace with the actual path to your model file
-    if not os.path.exists(model_path):
-        logging.error(f"Model file does not exist at path: {model_path}")
-        return False
-    try:
-        logging.info(f"Loading model from: {model_path}")
-        model = tf.keras.models.load_model(model_path)
-        logging.info("Model loaded successfully.")
-        return True
-    except Exception as e:
-        logging.error(f"Error loading model: {e}")
-        return False
-
-# Load the model when the script starts
-model_loaded = load_model()
+# Load the trained model
+try:
+    model_path = './Model1_kera.h5'
+    logging.info(f"Loading model from: {model_path}")
+    model = tf.keras.models.load_model(model_path)
+    logging.info("Model loaded successfully.")
+except Exception as e:
+    logging.error(f"Error loading model: {e}")
 
 # Define the class names
 classes = ['Colon Adenocarcinoma', 'Colon Benign Tissue', 'Lung Adenocarcinoma', 'Lung Benign Tissue', 'Lung Squamous Cell Carcinoma']
@@ -146,8 +65,8 @@ def predict(img):
         
         # Resize and preprocess the image
         img = img.resize((224, 224))
-        img_array = keras_image.img_to_array(img)
-        img_array = tf.expand_dims(img_array, 0)  # Add batch dimension
+        img_array = image.img_to_array(img)
+        img_array = np.expand_dims(img_array, axis=0)
         
         logging.debug("Image preprocessed successfully.")
         
@@ -157,8 +76,7 @@ def predict(img):
         
         # Make predictions
         predictions = model.predict(img_array)
-        score = tf.nn.softmax(predictions[0])
-        predicted_class_index = tf.argmax(score).numpy()
+        predicted_class_index = np.argmax(predictions[0])
         predicted_class = classes[predicted_class_index]
         
         logging.debug(f"Prediction successful: {predicted_class}")
@@ -184,3 +102,85 @@ iface = gr.Interface(
 
 # Launch the interface
 iface.launch()
+# import gradio as gr
+# import tensorflow as tf
+# from tensorflow.keras.preprocessing import image as keras_image
+# from PIL import Image
+# import numpy as np
+# import logging
+# import os
+
+# # Set up logging
+# logging.basicConfig(level=logging.DEBUG)
+
+# # Initialize the model variable
+# model = None
+
+# # Function to load the model
+# def load_model():
+#     global model
+#     model_path = r'Model1_kera.h5'  # Replace with the actual path to your model file
+#     if not os.path.exists(model_path):
+#         logging.error(f"Model file does not exist at path: {model_path}")
+#         return False
+#     try:
+#         logging.info(f"Loading model from: {model_path}")
+#         model = tf.keras.models.load_model(model_path)
+#         logging.info("Model loaded successfully.")
+#         return True
+#     except Exception as e:
+#         logging.error(f"Error loading model: {e}")
+#         return False
+
+# # Load the model when the script starts
+# model_loaded = load_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):
+#     global model
+#     try:
+#         logging.debug("Received image for prediction.")
+        
+#         # Resize and preprocess the image
+#         img = img.resize((224, 224))
+#         img_array = keras_image.img_to_array(img)
+#         img_array = tf.expand_dims(img_array, 0)  # Add batch dimension
+        
+#         logging.debug("Image preprocessed successfully.")
+        
+#         # Ensure the model is loaded
+#         if model is None:
+#             raise ValueError("Model is not loaded properly.")
+        
+#         # Make predictions
+#         predictions = model.predict(img_array)
+#         score = tf.nn.softmax(predictions[0])
+#         predicted_class_index = tf.argmax(score).numpy()
+#         predicted_class = classes[predicted_class_index]
+        
+#         logging.debug(f"Prediction successful: {predicted_class}")
+        
+#         # Return the predicted class and the raw predictions
+#         return predicted_class, predictions[0].tolist()
+#     except Exception as e:
+#         logging.error(f"Error during prediction: {e}")
+#         # Print the error message in the output
+#         return str(e), str(e)
+
+# # Create a Gradio interface
+# iface = gr.Interface(
+#     fn=predict, 
+#     inputs=gr.Image(type='pil'), 
+#     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."
+# )
+
+# # Launch the interface
+# iface.launch()