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# Imporiting Necessary Libraries
import streamlit as st
from PIL import Image
import io
import numpy as np
import tensorflow as tf
from utils import clean_image, get_prediction, make_results
# Loading the Model and saving to cache
@st.cache(allow_output_mutation=True)
def load_model(path):
# Xception Model
xception_model = tf.keras.models.Sequential([
tf.keras.applications.xception.Xception(include_top=False, weights='imagenet', input_shape=(512, 512, 3)),
tf.keras.layers.GlobalAveragePooling2D(),
tf.keras.layers.Dense(4,activation='softmax')
])
# DenseNet Model
densenet_model = tf.keras.models.Sequential([
tf.keras.applications.densenet.DenseNet121(include_top=False, weights='imagenet',input_shape=(512, 512, 3)),
tf.keras.layers.GlobalAveragePooling2D(),
tf.keras.layers.Dense(4,activation='softmax')
])
# Ensembling the Models
inputs = tf.keras.Input(shape=(512, 512, 3))
xception_output = xception_model(inputs)
densenet_output = densenet_model(inputs)
outputs = tf.keras.layers.average([densenet_output, xception_output])
model = tf.keras.Model(inputs=inputs, outputs=outputs)
# Loading the Weights of Model
model.load_weights(path)
return model
# Removing Menu
hide_streamlit_style = """
<style>
#MainMenu {visibility: hidden;}
footer {visibility: hidden;}
</style>
"""
st.markdown(hide_streamlit_style, unsafe_allow_html=True)
# Loading the Model
model = load_model('model.h5')
# Title and Description
st.title('Plant Diesease Detection')
st.write("Just Upload your Plant's Leaf Image and get predictions if the plant is healthy or not")
# Setting the files that can be uploaded
uploaded_file = st.file_uploader("Choose a Image file", type=["png", "jpg"])
# If there is a uploaded file, start making prediction
if uploaded_file != None:
# Display progress and text
progress = st.text("Crunching Image")
my_bar = st.progress(0)
i = 0
# Reading the uploaded image
image = Image.open(io.BytesIO(uploaded_file.read()))
st.image(np.array(Image.fromarray(
np.array(image)).resize((700, 400), Image.ANTIALIAS)), width=None)
my_bar.progress(i + 40)
# Cleaning the image
image = clean_image(image)
# Making the predictions
predictions, predictions_arr = get_prediction(model, image)
my_bar.progress(i + 30)
# Making the results
result = make_results(predictions, predictions_arr)
# Removing progress bar and text after prediction done
my_bar.progress(i + 30)
progress.empty()
i = 0
my_bar.empty()
# Show the results
st.write(f"The plant {result['status']} with {result['prediction']} prediction.")
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