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| # Imporiting Necessary Libraries | |
| import streamlit as st | |
| from PIL import Image | |
| import io | |
| import numpy as np | |
| import tensorflow as tf | |
| from utils import clean_image, get_prediction, make_results | |
| # Loading the Model and saving to cache | |
| def load_model(path): | |
| # Xception Model | |
| xception_model = tf.keras.models.Sequential([ | |
| tf.keras.applications.xception.Xception(include_top=False, weights='imagenet', input_shape=(512, 512, 3)), | |
| tf.keras.layers.GlobalAveragePooling2D(), | |
| tf.keras.layers.Dense(4,activation='softmax') | |
| ]) | |
| # DenseNet Model | |
| densenet_model = tf.keras.models.Sequential([ | |
| tf.keras.applications.densenet.DenseNet121(include_top=False, weights='imagenet',input_shape=(512, 512, 3)), | |
| tf.keras.layers.GlobalAveragePooling2D(), | |
| tf.keras.layers.Dense(4,activation='softmax') | |
| ]) | |
| # Ensembling the Models | |
| inputs = tf.keras.Input(shape=(512, 512, 3)) | |
| xception_output = xception_model(inputs) | |
| densenet_output = densenet_model(inputs) | |
| outputs = tf.keras.layers.average([densenet_output, xception_output]) | |
| model = tf.keras.Model(inputs=inputs, outputs=outputs) | |
| # Loading the Weights of Model | |
| model.load_weights(path) | |
| return model | |
| # Removing Menu | |
| hide_streamlit_style = """ | |
| <style> | |
| #MainMenu {visibility: hidden;} | |
| footer {visibility: hidden;} | |
| </style> | |
| """ | |
| st.markdown(hide_streamlit_style, unsafe_allow_html=True) | |
| # Loading the Model | |
| model = load_model('model.h5') | |
| # Title and Description | |
| st.title('Plant Diesease Detection') | |
| st.write("Just Upload your Plant's Leaf Image and get predictions if the plant is healthy or not") | |
| # Setting the files that can be uploaded | |
| uploaded_file = st.file_uploader("Choose a Image file", type=["png", "jpg"]) | |
| # If there is a uploaded file, start making prediction | |
| if uploaded_file != None: | |
| # Display progress and text | |
| progress = st.text("Crunching Image") | |
| my_bar = st.progress(0) | |
| i = 0 | |
| # Reading the uploaded image | |
| image = Image.open(io.BytesIO(uploaded_file.read())) | |
| st.image(np.array(Image.fromarray( | |
| np.array(image)).resize((700, 400), Image.ANTIALIAS)), width=None) | |
| my_bar.progress(i + 40) | |
| # Cleaning the image | |
| image = clean_image(image) | |
| # Making the predictions | |
| predictions, predictions_arr = get_prediction(model, image) | |
| my_bar.progress(i + 30) | |
| # Making the results | |
| result = make_results(predictions, predictions_arr) | |
| # Removing progress bar and text after prediction done | |
| my_bar.progress(i + 30) | |
| progress.empty() | |
| i = 0 | |
| my_bar.empty() | |
| # Show the results | |
| st.write(f"The plant {result['status']} with {result['prediction']} prediction.") | |