app.py

import cv2
from keras.models import load_model,Model
model = load_model('Model1')


import numpy as np
import gradio as gr


class_to_label = {
    0: 'Apple Scab',
    1: 'Apple_Black Rot',
    2: 'Bacterial Spot_Pepper',
    3: 'Bacterial Spot_peach',
    4: 'Bacterial Spot_tmt',
    5: 'Black Rot_grape',
    6: 'Cedar Apple Rust',
    7: 'Cercospora Leaf Spot_corn',
    8: 'Common Rust_corn',
    9: 'Early Blight_potato',
    10: 'Early Blight_tmt',
    11: 'Esca (Black Measles)_grape',
    12: 'Healthy_Apple',
    13: 'Healthy_Pepper',
    14: 'Healthy_cherry',
    15: 'Healthy_corn',
    16: 'Healthy_grape',
    17: 'Healthy_peach',
    18: 'Healthy_potato',
    19: 'Healthy_strb',
    20: 'Healthy_tmt',
    21: 'Late Blight_potato',
    22: 'Late Blight_tmt',
    23: 'Leaf Blight_grape',
    24: 'Leaf Scorch_strb',
    25: 'Northern Leaf Blight_corn',
    26: 'Powdery Mildew_cherry',
    27: 'Septoria Leaf Spot_tmt',
    28: 'Yellow Leaf Curl Virus_tmt'
}
def process1(img):
    
    img1 = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) 
    img2 = cv2.resize(img1, (256,256))
    img_tensor = np.expand_dims(img2, axis=0)
    fast_pred = model(img_tensor, training=False)
    k = np.argmax(fast_pred)
    s=class_to_label[k]
    
    return s

demo = gr.Interface(
    fn=process1,
    inputs=[gr.Image()],
    outputs=['number'],  
)
demo.launch()