| import numpy as np | |
| import tensorflow as tf | |
| import gradio as gr | |
| from PIL import Image | |
| from io import BytesIO | |
| import matplotlib.pyplot as plt | |
| import os | |
| # Load the trained TensorFlow model using the Keras API | |
| model = tf.keras.models.load_model("potatoesV3.h5") | |
| # Define the class names for classification | |
| CLASS_NAMES = ["Early Blight", "Late Blight", "Healthy"] | |
| # Define the function for making predictions | |
| def classify_image(image): | |
| # Open the image using Pillow | |
| img = Image.fromarray(image.astype('uint8'), 'RGB') | |
| new_size = (256, 256) | |
| img_resized = img.resize(new_size) | |
| #img_array = np.array(img_resized) | |
| #img_scaled = img_array / 255.0 | |
| img_array = np.expand_dims(img_resized, axis=0) | |
| pred = model.predict(img_array) | |
| predicted_class = CLASS_NAMES[np.argmax(pred[0])] | |
| confidence = float(np.max(pred[0])) | |
| # Return the predicted class and confidence score | |
| return {"class": predicted_class, "confidence": confidence,"predict":pred[0]} | |
| #New updated def | |
| # def classify_image(file): | |
| # # Open the file using requests | |
| # response = requests.get(file) | |
| # img = Image.open(BytesIO(response.content)) | |
| # # Resize and preprocess the image | |
| # img_resized = img.convert("RGB").resize((256, 256)) | |
| # img_array = np.array(img_resized) / 255.0 | |
| # img_array = np.expand_dims(img_array, axis=0) | |
| # # Make predictions | |
| # pred = model.predict(img_array) | |
| # predicted_class = CLASS_NAMES[np.argmax(pred[0])] | |
| # confidence = float(np.max(pred[0])) | |
| # # Return the predicted class and confidence score | |
| # return {"class": predicted_class, "confidence": confidence,"predict":pred[0]} | |
| # Resize the image to the desired size | |
| # new_size = (256, 256) | |
| # img_resized = image.resize(new_size) | |
| # # Convert the image to a numpy array | |
| # img_array = np.array(img_resized) | |
| # # Rescale the image pixel values to [0, 1] | |
| # img_scaled = img_array / 255.0 | |
| # #img_array = np.array(img_scaled) | |
| # img_array = np.expand_dims(img_scaled, axis=0) | |
| # pred = model.predict(img_array) | |
| # # Display the rescaled image | |
| # #plt.imshow(img_array) | |
| # # Format the predicted class and confidence score | |
| # predicted_class = CLASS_NAMES[np.argmax(pred[0])] | |
| # confidence = float(np.max(pred[0])) | |
| # # Return the predicted class and confidence score | |
| # return {"class": predicted_class, "confidence": confidence,"predict":pred[0]} | |
| # img = (BytesIO(image)) | |
| # img = img.resize((256, 256)) | |
| # img_array = np.array(img) | |
| # img_array = np.expand_dims(img_array, axis=0) | |
| # pred = model.predict(img_array) | |
| # return pred[0][0] | |
| examples=[os.path.join(os.path.dirname(__file__),'7227b3db-c212-4370-8b42-443eea1577aa___RS_Early.B 7306.JPG','7456db33-766c-4a68-b924-ddf69d579981___RS_Early.B 6723.JPG','7486e823-64f7-4e43-ab51-26261b077fc2___RS_Early.B 6785.JPG','8829e413-5a7a-4680-b873-e71dfa9dbfe4___RS_LB 3974.JPG','9001b18c-b659-4c56-9dfb-0d0bf64a7b4a___RS_LB 4987.JPG','9009c86e-1205-4694-b0bb-ef7cf78dd104___RS_LB 3995.JPG','Potato_healthy-76-_0_2420.jpg','Potato_healthy-76-_0_6833.jpg','Potato_healthy-76-_0_7539.jpg')] | |
| # Define the Gradio interface for image input | |
| input_interface = gr.inputs.Image() | |
| # Define the Gradio interface for displaying the predicted class and confidence score | |
| output_interface = gr.Textbox() | |
| # Launch the Gradio interface with the classify_image function as the prediction function | |
| gr.Interface(fn=classify_image, inputs=input_interface, outputs=output_interface,Examples=examples).launch() |