| from helper.model import fetch_model, preprocess_image, classify_image |
| from PIL import Image |
| import streamlit as st |
| import io |
|
|
| st.set_page_config( |
| page_title="VGNet Prototype", |
| page_icon="🤖", |
| ) |
|
|
| st.title("Using Deep Learning Techniques on Philippine Corn Leaf Disease Classification") |
| st.write("This is the prototype of VGNet model trained using different optimizers") |
| st.write("Thesis Project by Group BDK of Mapua University") |
|
|
| st.image( |
| "resources/corn-field.png", |
| caption='A corn field', |
| width=700 |
| ) |
|
|
| st.header("How to use?") |
| st.markdown(""" |
| 1. Select a model. |
| 2. Select an image (Leaf Blight, Gray Leaf Spot, Common Rust). |
| 3. Upload the selected image here. |
| 4. The model will output the classification result if the image is (Leaf Blight, Gray Leaf Spot, Common Rust). |
| """) |
|
|
| uploaded_image = st.file_uploader( |
| "Choose an image...", |
| type=["bmp", "png", "jpg", "jpeg"] |
| ) |
|
|
| model_labels = [ |
| "VGNet + Adam", |
| "VGNet + Adan", |
| "VGNet + AdamW", |
| "VGNet + RMSprop", |
| "VGNet + AdaBound" |
| ] |
| model_option = st.selectbox( |
| "Select a Model:", |
| list(range(len(model_labels))), |
| format_func=lambda ind: model_labels[ind] |
| ) |
|
|
|
|
| |
| if uploaded_image is not None: |
|
|
| |
| image = preprocess_image( |
| Image.open( |
| uploaded_image |
| ) |
| ) |
|
|
| |
| model_optimizer_name = ["adam", "adan", "adamw", "rmsprop", "adabound"] |
| model = fetch_model(model_optimizer_name[model_option]) |
|
|
| |
| predicted_label = classify_image(model, image) |
|
|
| |
| st.image(image, clamp = True) |
| st.write(f"Prediction Result: {predicted_label}") |
|
|
