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| import streamlit as st | |
| from PIL import Image | |
| import pandas as pd | |
| import numpy as np | |
| from keras.models import load_model | |
| model = load_model('./model.h5') | |
| # Classification report data | |
| data = { | |
| "classes": ['Pepper__bell___Bacterial_spot', | |
| 'Pepper__bell___healthy', | |
| 'Potato___Early_blight', | |
| 'Potato___Late_blight', | |
| 'Potato___healthy', | |
| 'Tomato_Bacterial_spot', | |
| 'Tomato_Early_blight', | |
| 'Tomato_Late_blight', | |
| 'Tomato_Leaf_Mold', | |
| 'Tomato_Septoria_leaf_spot', | |
| 'Tomato_Spider_mites_Two_spotted_spider_mite', | |
| 'Tomato__Target_Spot', | |
| 'Tomato__Tomato_YellowLeaf__Curl_Virus', | |
| 'Tomato__Tomato_mosaic_virus', | |
| 'Tomato_healthy'], | |
| "precision": [0.99, 0.99, 0.98, 0.83, 0.41, 0.99, 0.74, 1.00, 1.00, 0.91, 0.95, 0.60, 1.00, 0.87, 0.91], | |
| "recall": [0.99, 0.86, 1.00, 0.99, 1.00, 0.74, 0.90, 0.83, 0.71, 0.97, 0.64, 0.99, 0.98, 0.98, 1.00], | |
| "f1-score": [0.99, 0.92, 0.99, 0.90, 0.58, 0.85, 0.82, 0.90, 0.83, 0.94, 0.77, 0.74, 0.99, 0.92, 0.95], | |
| "support": [199, 296, 213, 205, 28, 399, 206, 379, 187, 359, 326, 290, 629, 81, 331] | |
| } | |
| # Create a DataFrame | |
| df = pd.DataFrame(data) | |
| def get_predictions(immg): | |
| # from skimage import io | |
| # from keras.preprocessing import image | |
| import keras.utils as image | |
| #path='imbalanced/Scratch/Scratch_400.jpg' | |
| # "C:\Users\prati\Desktop\AML 3104 final project\PlantVillage\Pepper__bell___Bacterial_spot\00f2e69a-1e56-412d-8a79-fdce794a17e4___JR_B.Spot 3132.JPG" | |
| # pth = immg | |
| # show_img=image.load_img(pth, grayscale=False, target_size=(200, 200)) | |
| disease_class = data["classes"] | |
| x = image.img_to_array(immg) | |
| x = np.expand_dims(x, axis = 0) | |
| x = np.array(x, 'float32') | |
| x /= 255 | |
| custom = model.predict(x) | |
| print(custom[0]) | |
| # x = x.reshape([64, 64]) | |
| #plt.gray() | |
| # plt.imshow(show_img) | |
| # plt.show() | |
| a=custom[0] | |
| ind=np.argmax(a) | |
| print('Prediction:',disease_class[ind]) | |
| return(disease_class[ind]) | |
| #User Interface--------------------------------------------------------- | |
| pred_flag = False | |
| def main(): | |
| st.label_visibility='collapse' | |
| st.title('Plant desise identification') | |
| st.write('# Detecting plant desise using deep learnings dense net model') | |
| # Display the DataFrame as a table in Streamlit | |
| st.header('Evaluation Report:') | |
| # Create two columns | |
| col1, col2 = st.columns(2) | |
| # Add content to the first column | |
| with col1: | |
| st.write('Classification Report:') | |
| st.table(df) | |
| # Add content to the second column | |
| with col2: | |
| st.write('Confussion matrix') | |
| st.image("./confussion_matrix.png",width=650) | |
| uploaded_file = st.file_uploader("", type=['jpg','png','jpeg']) | |
| if uploaded_file is not None: | |
| image = Image.open(uploaded_file) | |
| st.markdown('<p style="text-align: center;"><label>Image : </label></p>',unsafe_allow_html=True) | |
| st.image(image,width=500) | |
| # image = Image.open() | |
| if st.button("Predict"): | |
| resized_image = image.resize((64, 64)) | |
| pred_cls = get_predictions(resized_image) | |
| st.markdown('<p style="text-align: center;"><label>Prediction : </label></p>',unsafe_allow_html=True) | |
| # st.image(image,width=900) | |
| st.markdown(pred_cls) | |
| # result ='' | |
| # st.success('The output is {}'.format(result)) | |
| if __name__ == '__main__': # | |
| main() |