Update app.py

app.py

@@ -1,4 +1,3 @@
-# Import libraries and dependencies for the UI and deep learning model
 import streamlit as st
 import tensorflow as tf
 import numpy as np
@@ -6,85 +5,101 @@ from PIL import Image
 from tensorflow import keras
 import os
 import warnings
-import random
 
-# Suppress warnings and configure TensorFlow settings
-warnings.filterwarnings("ignore")
+warnings.filterwarnings('ignore')
 os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
 
-# Set up Streamlit page configuration
-st.set_page_config(
-    page_title="PNEUMONIA Disease Detection",
-    page_icon=":skull:",
-    initial_sidebar_state="auto",
-)
+class ChestAI:
+    def __init__(self):
+        self.setup_page_config()
+        self.load_model()
+        self.setup_sidebar()
+        self.run_interface()
 
-# Hide Streamlit's main menu and footer
-st.markdown("""
-    <style>
-    #MainMenu {visibility: hidden;}
-    footer {visibility: hidden;}
-    </style>
-""", unsafe_allow_html=True)
+    def setup_page_config(self):
+        st.set_page_config(
+            page_title="ChestAI - Pneumonia Detection",
+            page_icon="🫁",
+            initial_sidebar_state="expanded"
+        )
+        
+        st.markdown("""
+            <style>
+                #MainMenu {visibility: hidden;}
+                footer {visibility: hidden;}
+                .stDeployButton {display:none;}
+            </style>
+        """, unsafe_allow_html=True)
 
-# Define a function to map model predictions to their class names
-def prediction_class(prediction):
-    for label, class_index in class_names.items():
-        if np.argmax(prediction) == class_index:
-            return label
+    @st.cache_resource
+    def load_model(self):
+        from huggingface_hub import from_pretrained_keras
+        keras.utils.set_random_seed(42)
+        self.model = from_pretrained_keras("ryefoxlime/PneumoniaDetection")
 
-# Configure sidebar content with description
-with st.sidebar:
-    st.title("Disease Detection")
-    st.markdown(
-        "Accurate detection of diseases in X-ray images. This helps users easily detect diseases and understand their potential causes."
-    )
+    def setup_sidebar(self):
+        with st.sidebar:
+            st.title("👋 Welcome to ChestAI")
+            st.markdown("""
+                ### About
+                ChestAI uses advanced deep learning to detect pneumonia in chest X-rays.
+                
+                ### How to use
+                1. Upload a chest X-ray image (JPG/PNG)
+                2. Wait for the analysis
+                3. View the results and confidence score
+                
+                ### Note
+                This tool is for educational purposes only. Always consult healthcare professionals for medical advice.
+            """)
 
-# Set file upload options
-st.set_option("deprecation.showfileUploaderEncoding", False)
+    def preprocess_image(self, image):
+        img_array = keras.preprocessing.image.img_to_array(image)
+        img_array = np.expand_dims(img_array, axis=0) / 255.0
+        return img_array
 
-# Load the model from Hugging Face Hub, with caching for optimization
-@st.cache_resource()
-def load_model():
-    from huggingface_hub import from_pretrained_keras
-    keras.utils.set_random_seed(42)
-    model = from_pretrained_keras("ryefoxlime/PneumoniaDetection")
-    return model
+    def get_prediction(self, image):
+        processed_image = self.preprocess_image(image)
+        prediction = self.model.predict(processed_image)
+        return prediction
 
-# Display loading spinner while model is being loaded
-with st.spinner("Model is being loaded.."):
-    model = load_model()
+    def display_results(self, prediction):
+        class_names = ["Normal", "Pneumonia"]
+        prediction_label = class_names[np.argmax(prediction)]
+        confidence = float(max(prediction[0]) * 100)
 
-# Set up file uploader to accept image files (JPEG or PNG)
-file = st.file_uploader(" ", type=["jpg", "png"])
+        st.markdown("### Analysis Results")
+        progress_bar = st.progress(0)
+        progress_bar.progress(confidence / 100)
+        
+        if prediction_label == "Normal":
+            st.success(f"Result: {prediction_label}")
+            st.balloons()
+        else:
+            st.warning(f"Result: {prediction_label}")
+        
+        st.info(f"Confidence: {confidence:.2f}%")
 
-# Preprocess and run the model on uploaded image
-def import_and_predict(image_data, model):
-    img_array = keras.preprocessing.image.img_to_array(image_data)
-    img_array = np.expand_dims(img_array, axis=0) / 255.0
-    predictions = model.predict(img_array)
-    return predictions
+    def run_interface(self):
+        st.title("🫁 ChestAI - Pneumonia Detection")
+        st.markdown("""
+            Upload a chest X-ray image to detect the presence of pneumonia.
+            The system will analyze the image and provide results with confidence scores.
+        """)
 
-# If no file is uploaded, prompt the user
-if file is None:
-    st.text("Please upload an image file")
-else:
-    # Display uploaded image and run predictions
-    image = keras.preprocessing.image.load_img(file, target_size=(224, 224), color_mode='rgb')
-    st.image(image, caption="Uploaded Image.", use_column_width=True)
-    predictions = import_and_predict(image, model)
-    
-    # Generate a random accuracy display
-    np.random.seed(42)
-    accuracy = random.randint(98, 99) + random.randint(0, 99) * 0.01
-    st.error("Accuracy: " + str(accuracy) + "%")
-    
-    # Define class names and display prediction results
-    class_names = ["Normal", "PNEUMONIA"]
-    prediction_label = class_names[np.argmax(predictions)]
-    
-    if prediction_label == "Normal":
-        st.balloons()
-        st.success("Detected Disease: " + prediction_label)
-    else:
-        st.warning("Detected Disease: " + prediction_label)
+        uploaded_file = st.file_uploader("Upload Chest X-ray Image", type=["jpg", "png"])
+
+        if uploaded_file is not None:
+            image = keras.preprocessing.image.load_img(
+                uploaded_file, 
+                target_size=(224, 224), 
+                color_mode='rgb'
+            )
+            st.image(image, caption="Uploaded X-ray Image", use_column_width=True)
+
+            with st.spinner("Analyzing image..."):
+                prediction = self.get_prediction(image)
+                self.display_results(prediction)
+
+if __name__ == "__main__":
+    app = ChestAI()