streamlit.py

import streamlit as st
from keras.models import load_model
import numpy as np

model=load_model("crop_prediction_model.h5",compile=True)

labels=["Tomato Bacterial Spot","Early Blight","Healthy","Late Blight","Leaf Mold","Tomato Septoria leaf spot", "Tomato___Spider_mites Two spotted spider mite","Tomato___Target_Spot","Tomato___Tomato_mosaic_virus", "Tomato___Tomato_Yellow_Leaf_Curl_Virus"]

def classify_image(img):
    img = img.reshape((-1, 256, 256, 3))
    type=predict_crop(img)
    return type

def predict_crop(img):
    crop_class=model.predict(img)
    index=np.argmax(crop_class)
    label=labels[index]
#     return label

img = st.camera_input("Take a picture")

if img:
    class_of_plant=classify_image(img)
    st.write(class_of_plant)
else :
    st.write("Image is not clear.")