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import os
import json
from PIL import Image
import numpy as np
import tensorflow as tf
import streamlit as st
import matplotlib.pyplot as plt
# Load the pre-trained model and class indices
working_dir = os.path.dirname(os.path.abspath(__file__))
model_path = "crop_disease_detection_model.h5"
model = tf.keras.models.load_model(model_path)
class_indices = json.load(open("class_indices.json"))
# Function to Load and Preprocess the Image using Pillow
def load_and_preprocess_image(image_path, target_size=(224, 224)):
img = Image.open(image_path)
img = img.resize(target_size)
img_array = np.array(img)
img_array = np.expand_dims(img_array, axis=0)
img_array = img_array.astype('float32') / 255.
return img_array
# Function to Predict the Class of an Image
def predict_image_class(model, img_array, class_indices):
predictions = model.predict(img_array)
predicted_class_index = np.argmax(predictions, axis=1)[0]
predicted_class_name = class_indices[str(predicted_class_index)]
confidence_score = predictions[0][predicted_class_index]
return predicted_class_name, confidence_score
# Streamlit App
st.title('INTELLEGENT CROP DISEASE DETECTION')
# Upload and preprocess the image only once
uploaded_image = st.file_uploader("Upload an image...", type=["jpg", "jpeg", "png"])
if uploaded_image is not None:
img_array = load_and_preprocess_image(uploaded_image)
st.session_state.img_array = img_array
st.session_state.image_uploaded = True
# Display tabs for Identification and Visualization side by side
col1, col2 = st.columns(2)
with col1:
if st.button('Identification'):
st.session_state.tab_selected = 'Identification'
with col2:
if st.button('Visualization'):
st.session_state.tab_selected = 'Visualization'
selected_tab = st.session_state.get('tab_selected', 'Identification')
if st.session_state.get('image_uploaded', False):
if selected_tab == 'Identification':
st.header('Plant Disease Identification')
st.image(uploaded_image, caption='Uploaded Image', use_column_width=False)
predicted_class, confidence_score = predict_image_class(model, img_array, class_indices)
st.write(f'Prediction: {predicted_class} ({confidence_score:.2f} confidence)')
elif selected_tab == 'Visualization':
st.header('Confidence Scores Visualization')
plt.figure(figsize=(12, 6)) # Smaller graph size
plt.bar(class_indices.values(), model.predict(img_array)[0])
plt.xlabel('Class')
plt.ylabel('Confidence Score')
plt.xticks(rotation=90, ha='right')
plt.title('Confidence Scores for Predicted Classes')
st.pyplot(plt)
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