app.py

import tensorflow as tf
from tensorflow.keras.models import load_model
import numpy as np
import gradio as gr
model = load_model('potatodisease.h5')
class_names = ['Potato___Early_blight', 'Potato___Late_blight', 'Potato___healthy']
# Define predict function
def predict(img):
    img_array = tf.keras.preprocessing.image.img_to_array(img)
    img_array = tf.expand_dims(img_array, 0)
    predictions = model.predict(img_array)
    predicted_class = class_names[np.argmax(predictions[0])]
    confidence = round(100 * (np.max(predictions[0])), 2)
    return predicted_class, confidence
    

# Create Gradio interface
gr.Interface(fn=predict, inputs="image", outputs=["text","text"]).launch(inline=False)