app.py

import gradio as gr
import tensorflow as tf
import numpy as np
from PIL import Image

# trained model load
model = tf.keras.models.load_model("plant_disease_model.h5")


# prediction function
def predict_plant(img):

    # resize same as training
    img = img.resize((150,150))

    img_array = np.array(img)/255.0

    # batch dimension add
    img_array = np.expand_dims(img_array, axis=0)

    prediction = model.predict(img_array)[0][0]

    if prediction > 0.5:
        return "Diseased Plant"
    else:
        return "Healthy Plant"


# interface
demo = gr.Interface(
    fn=predict_plant,
    inputs=gr.Image(type="pil"),
    outputs="text",
    title="Plant Disease Classifier"
)

demo.launch()