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app.py

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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 = f"{working_dir}/trained model/crop_disease_detection_model.h5"
+model = tf.keras.models.load_model(model_path)
+class_indices = json.load(open(f"{working_dir}/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('Plant Disease Classifier')
+
+# 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)
+
+
+
+
+

crop_disease_detection_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:503e768444890fce8ecedd5d8fe0c8425016ed8109bb17134a0da09662802ab8
+size 573695016