import numpy as np
import pandas as pd 
import tensorflow as tf 
import streamlit as st

from PIL import Image

model = tf.keras.models.load_model(r"C:\Dhruv\potato diseases\my_model2.h5")


resize_and_rescale = tf.keras.Sequential([
    tf.keras.layers.experimental.preprocessing.Resizing(256,256),
    tf.keras.layers.experimental.preprocessing.Rescaling(1.0/255)
])

def predict(model, img):

    class_names = ['Potato___Early_blight', 'Potato___Late_blight', 'Potato___healthy']

    img_array = tf.keras.preprocessing.image.img_to_array(np.array(img))
    img_array = tf.expand_dims(img_array, 0)

    img_array = resize_and_rescale(img_array)

    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

st.title("Potato Disease Classification")

uploaded_image = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])

if uploaded_image:

    st.image(uploaded_image)
    img = Image.open(uploaded_image)
    
    predicted_class, confidence = predict(model=model, img=img)

    st.markdown(f"The predicted class is **{predicted_class}** with **{confidence}%** confidence")