| import streamlit as st
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| import tensorflow as tf
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| import numpy as np
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| from PIL import Image
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| from pathlib import Path
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|
|
| MODEL_PATH = Path(__file__).resolve().parent / "EfficientNetV2B0_best.h5"
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|
|
|
|
| CLASS_NAMES = [
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| "Tomato_Bacterial_spot",
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| "Tomato_Early_blight",
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| "Tomato_Late_blight",
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| "Tomato_Leaf_Mold",
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| "Tomato_Septoria_leaf_spot",
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| "Tomato_Spider_mites_Two_spotted_spider_mite",
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| "Tomato_Target_Spot",
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| "Tomato_Tomato_Yellow_Leaf_Curl_Virus",
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| "Tomato_Tomato_mosaic_virus",
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| "Tomato_Healthy",
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| ]
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|
|
| @st.cache_resource
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| def load_model(path: Path):
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| if not path.exists():
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| raise FileNotFoundError(f"Model tidak ditemukan: {path}")
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| return tf.keras.models.load_model(str(path))
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|
|
| def preprocess_image(_image: Image.Image, target_size=(224, 224)) -> np.ndarray:
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| image = _image.convert("RGB")
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| image = image.resize(target_size)
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| image_array = np.array(image).astype(np.float32)
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| image_array = tf.keras.applications.efficientnet_v2.preprocess_input(image_array)
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| image_array = np.expand_dims(image_array, axis=0)
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| return image_array
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|
|
| def predict_image(model, _image: Image.Image):
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| img_tensor = preprocess_image(_image)
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| preds = model.predict(img_tensor)
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| return preds[0]
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|
|
|
|
| def main():
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| st.set_page_config(
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| page_title="Tomato Leaf Disease Detector",
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| page_icon="🍅",
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| layout="centered",
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| )
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|
|
| st.title("Deteksi Penyakit Daun Tomat")
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| st.write(
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| "Unggah gambar daun tomat dan model akan memprediksi jenis penyakit (atau sehat) pada tanaman tersebut."
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| )
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|
|
| uploaded_file = st.file_uploader(
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| "Pilih gambar daun tomat", type=["png", "jpg", "jpeg"]
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| )
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|
|
| if uploaded_file is None:
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| st.info("Silakan unggah gambar untuk melakukan prediksi.")
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| return
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|
|
| try:
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| image = Image.open(uploaded_file)
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| except Exception as e:
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| st.error(f"Gagal membaca gambar: {e}")
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| return
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|
|
| st.image(image, caption="Gambar input", use_column_width=True)
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|
|
| try:
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| model = load_model(MODEL_PATH)
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| except Exception as e:
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| st.error(f"Gagal memuat model: {e}")
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| return
|
|
|
| with st.spinner("Menjalankan prediksi..."):
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| preds = predict_image(model, image)
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|
|
| num_classes = preds.shape[-1] if preds.ndim > 0 else 1
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| if num_classes != len(CLASS_NAMES):
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| class_names = [f"Class {i}" for i in range(num_classes)]
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| else:
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| class_names = CLASS_NAMES
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|
|
| top_idx = int(np.argmax(preds))
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| confidence = float(np.max(preds))
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| predicted_label = class_names[top_idx]
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|
|
| st.markdown("---")
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| st.subheader("Hasil Prediksi")
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| st.write(f"**Prediksi:** {predicted_label}")
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| st.write(f"**Kepercayaan:** {confidence * 100:.2f}%")
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|
|
| if len(preds) == len(class_names):
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| st.subheader("Distribusi Skor Kelas")
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| result = {class_names[i]: float(preds[i]) for i in range(len(preds))}
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| st.bar_chart(result)
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|
|
| st.write(
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| "\n*Catatan: Sesuaikan `CLASS_NAMES` di `app.py` jika urutan kelas dalam model Anda berbeda.*"
|
| )
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|
|
|
|
| if __name__ == "__main__":
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| main()
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|
|
