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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
# 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(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]}
# 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]}
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.File(label="Upload an image file")
# 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() |