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import gradio as gr
import numpy as np
from tensorflow.keras.preprocessing import image
from tensorflow.keras.models import load_model
from PIL import Image
# Load model
model = load_model("plant_disease_model.h5") # You must include this file in your repo
IMG_SIZE = (224, 224)
class_names = ['Apple___Black_rot', 'Tomato___Early_blight', 'Potato___Late_blight'] # Update this to match your model
# Prediction function
def predict_plant_disease(img):
img = img.resize(IMG_SIZE)
img_array = image.img_to_array(img) / 255.0
img_array = np.expand_dims(img_array, axis=0)
predictions = model.predict(img_array)
index = np.argmax(predictions)
confidence = float(predictions[0][index])
disease_name = class_names[index]
confidence_text = f"{confidence:.2%}"
confidence_value = round(confidence, 2)
return disease_name, confidence_value, confidence_text
# Gradio UI
with gr.Blocks(css=".green-btn button {background-color: #2e7d32 !important; color: white;}") as demo:
gr.Markdown("<h1 style='text-align:center;'>🌿 Smart Plant Disease Detector</h1>")
with gr.Row():
with gr.Column(scale=1):
image_input = gr.Image(type="pil", label="📷 Upload Leaf Image")
predict_btn = gr.Button("🔍 Detect Disease", elem_classes="green-btn")
with gr.Column(scale=1):
disease_output = gr.Textbox(label="🪴 Detected Disease")
confidence_bar = gr.Slider(label="📊 Confidence Level", minimum=0, maximum=1, step=0.01, interactive=False)
confidence_text = gr.Textbox(label="🔢 Confidence (Text)")
predict_btn.click(fn=predict_plant_disease,
inputs=image_input,
outputs=[disease_output, confidence_bar, confidence_text])
demo.launch()
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