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| # 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 | |
| # 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 | |
| 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() | |