File size: 2,483 Bytes
d1f76c1 |
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 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 |
import streamlit as st
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
def main():
st.title("Banana Analysis App")
st.write("Choose an option to analyze bananas")
# Options for banana analysis
analysis_option = st.radio("Choose an option", ["Banana Disease Detection", "Banana Ripeness Detection"])
# File uploader
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
# Display the uploaded image
st.image(uploaded_file, caption='Uploaded Image', use_column_width=True)
if st.button("Analyze"):
if analysis_option == "Banana Disease Detection":
predicted_label = predict_disease(uploaded_file)
st.success(f"Predicted disease: {predicted_label}")
elif analysis_option == "Banana Ripeness Detection":
predicted_label = predict_ripeness(uploaded_file)
st.success(f"Predicted ripeness: {predicted_label}")
if __name__ == '__main__':
main()
|