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import gradio as gr
import tensorflow as tf
import numpy as np
from PIL import Image
# Constants
IMAGE_SIZE = (300, 300)
# Class index to label mapping
CLASS_NAMES = {
0: "A healthy tomato leaf",
1: "A tomato leaf with Leaf Mold",
2: "A tomato leaf with Target Spot",
3: "A tomato leaf with Late Blight",
4: "A tomato leaf with Early Blight",
5: "A tomato leaf with Bacterial Spot",
6: "A tomato leaf with Septoria Leaf Spot",
7: "A tomato leaf with Tomato Mosaic Virus",
8: "A tomato leaf with Tomato Yellow Leaf Curl Virus",
9: "A tomato leaf with Spider Mites Two-spotted Spider Mite"
}
# Load the model from Hugging Face Hub
model_url = "https://huggingface.co/chimithecat/penyakit_tomat/resolve/main/Tomato_Models.h5"
model_path = tf.keras.utils.get_file("Tomato_Models.h5", model_url)
model = tf.keras.models.load_model(model_path)
# Prediction function
def classify(image):
if image is None:
return "Tidak ada gambar.", ""
if not isinstance(image, Image.Image):
image = Image.fromarray(image)
img = image.resize(IMAGE_SIZE)
img_array = np.array(img) / 255.0
img_array = np.expand_dims(img_array, axis=0)
predictions = model.predict(img_array)[0]
predicted_index = int(np.argmax(predictions))
confidence = float(predictions[predicted_index])
label = CLASS_NAMES[predicted_index]
return f"{label}", f"Confidence: {confidence*100:.2f}%"
# UI with Gradio Blocks
with gr.Blocks(theme=gr.themes.Soft()) as app:
gr.Markdown(
"""
# 🍅 Tomato Leaf Disease Classifier
Upload a photo of a tomato leaf to detect its potential disease.
"""
)
with gr.Row():
with gr.Column():
image_input = gr.Image(type="pil", label="Upload Leaf Image")
submit_button = gr.Button("Analyze", variant="primary")
with gr.Column():
result_output = gr.Text(label="Prediction Result")
confidence_output = gr.Text(label="Confidence")
submit_button.click(
fn=classify,
inputs=image_input,
outputs=[result_output, confidence_output],
api_name="predict"
)
app.launch(show_api=True)
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