File size: 1,716 Bytes
990b5d5 d1f76c1 990b5d5 d1f76c1 990b5d5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 |
import gradio as gr
import numpy as np
from PIL import Image
from keras.models import load_model
# Load the pre-trained model for banana ripeness detection
banana_model = load_model("trained model/best_model.h5")
# Define class names for the banana disease detection
class_names_disease = {
0: 'BUNCHY_TOP',
1: 'CORDANA',
2: 'PANAMA',
3: 'SIGATOKA'
}
# Define class names for the banana ripeness detection
class_names_ripeness = ["Banana_G1", "Banana_G2", "Rotten"]
model = load_model("trained model/best_model.h5")
def preprocess_image(image):
img = Image.open(image)
img = img.resize((256, 256)) # Resize the image to the input size of the model
img_array = np.array(img)
img_array = img_array / 255.0 # Normalize the pixel values
img_array = np.expand_dims(img_array, axis=0) # Add batch dimension
return img_array
def predict(image):
img_array = preprocess_image(image)
predictions = model.predict(img_array)
predicted_class = np.argmax(predictions)
predicted_label = class_names_disease[predicted_class]
return predicted_label
def predict_disease(uploaded_file):
if uploaded_file is not None:
predicted_label = predict(uploaded_file)
return predicted_label
def predict_ripeness(image):
img_array = preprocess_image(image)
predictions = banana_model.predict(img_array)
predicted_class = np.argmax(predictions)
predicted_label = class_names_ripeness[predicted_class]
return predicted_label
inputs = gr.inputs.File(label="Upload an image...")
outputs = gr.outputs.Textbox(label="Prediction")
gr.Interface(fn=predict_disease, inputs=inputs, outputs=outputs, title="Banana Disease Detection").launch()
|