| import streamlit as st |
| import tensorflow as tf |
| import numpy as np |
| from PIL import Image |
| import os |
|
|
| |
| IMG_SIZE = (170, 170) |
|
|
| CLASS_NAMES = [ |
| "Black Rot", |
| "ESCA", |
| "Healthy", |
| "Leaf Blight", |
| ] |
|
|
| MODEL_FILENAME = "grape_disease_model.h5" |
| |
|
|
|
|
| @st.cache_resource |
| def load_model(): |
| """ |
| Load the Keras model once and cache it. |
| """ |
| if not os.path.exists(MODEL_FILENAME): |
| st.error(f"Model file '{MODEL_FILENAME}' not found in this directory.") |
| st.stop() |
|
|
| model = tf.keras.models.load_model(MODEL_FILENAME) |
| return model |
|
|
|
|
| model = load_model() |
|
|
|
|
| def preprocess_image(img: Image.Image) -> np.ndarray: |
| """ |
| - Resize to IMG_SIZE |
| - Convert to numpy array |
| - Normalize (same as rescale=1./255 in your ImageDataGenerator) |
| - Add batch dimension |
| """ |
| img = img.resize(IMG_SIZE) |
| img_array = np.array(img) |
|
|
| |
| img_array = img_array.astype("float32") / 255.0 |
|
|
| |
| img_array = np.expand_dims(img_array, axis=0) |
| return img_array |
|
|
|
|
| def predict_image(img: Image.Image): |
| """ |
| Returns (predicted_label, confidence, all_probabilities) |
| for multi-class classification. |
| """ |
| x = preprocess_image(img) |
| preds = model.predict(x) |
|
|
| |
| probs = preds[0] |
| idx = int(np.argmax(probs)) |
| label = CLASS_NAMES[idx] |
| confidence = float(probs[idx]) |
| return label, confidence, probs |
|
|
|
|
| |
| st.set_page_config( |
| page_title="Grape Disease Classifier", |
| layout="centered" |
| ) |
|
|
| st.title("🍇 Grape Disease Classifier") |
| st.write( |
| "Upload a photo of a grape leaf. " |
| "The model predicts whether the leaf is **Black Rot**, **ESCA**, **Healthy**, " |
| "or **Leaf Blight**." |
| ) |
|
|
| uploaded_file = st.file_uploader( |
| "Upload an image (.jpg, .jpeg or .png)", |
| type=["jpg", "jpeg", "png"] |
| ) |
|
|
| if uploaded_file is not None: |
| image = Image.open(uploaded_file).convert("RGB") |
| st.image(image, caption="Uploaded image", use_column_width=True) |
|
|
| if st.button("Predict"): |
| with st.spinner("Running prediction..."): |
| label, confidence, probs = predict_image(image) |
|
|
| st.subheader(f"Prediction: **{label}**") |
| st.write(f"Confidence: **{confidence:.2%}**") |
|
|
| st.write("Class probabilities:") |
| for name, p in zip(CLASS_NAMES, probs): |
| st.write(f"- {name}: {float(p):.2%}") |
| else: |
| st.info("Upload an image above to get a prediction.") |
|
|
