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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+pneumonia_model.keras filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,13 +1,13 @@
----
-title: Pneumonia XRay Classifier
-emoji: 🐢
-colorFrom: purple
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.39.0
-app_file: app.py
-pinned: false
-short_description: Pneumonia Detection from Chest X-rays
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Pneumonia XRay Classifier
+emoji: 🐢
+colorFrom: purple
+colorTo: purple
+sdk: streamlit
+sdk_version: 1.39.0
+app_file: app.py
+pinned: false
+short_description: Pneumonia Detection from Chest X-rays
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,70 @@
+# flask_app.py
+import os
+import numpy as np
+import cv2
+from flask import Flask, request, jsonify, render_template
+from tensorflow.keras.models import load_model  # type: ignore
+
+app = Flask(__name__)
+
+# Load the saved model
+model = load_model('pneumonia_model_final.keras')
+
+def preprocess_image(image):
+    """Preprocess the image for prediction."""
+    img = cv2.imread(image, cv2.IMREAD_GRAYSCALE)  # Read image as grayscale
+    if img is None:
+        return None
+    
+    img = cv2.resize(img, (224, 224))  # Resize to 224x224 pixels
+    img = img.astype(np.float32) / 255.0  # Normalize to [0, 1] range
+    img = (img * 255).astype(np.uint8)  # Convert back to 8-bit unsigned integer format
+    
+    img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)  # Convert to RGB
+    img = np.expand_dims(img, axis=0)  # Add batch dimension
+    return img
+
+@app.route('/')
+def home():
+    """Render the home page for uploading images."""
+    return render_template('index.html')
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    """Endpoint for making predictions."""
+    if 'file' not in request.files:
+        return jsonify({'error': 'No file provided.'}), 400
+
+    file = request.files['file']
+    
+    # Save the uploaded file temporarily
+    file_path = 'temp_image.jpg'
+    file.save(file_path)
+
+    # Preprocess the image
+    processed_image = preprocess_image(file_path)
+    if processed_image is None:
+        return jsonify({'error': 'Invalid image format.'}), 400
+
+    # Make a prediction
+    prediction = model.predict(processed_image)
+    probability = prediction[0][0]  # Get the probability score
+
+    # Calculate percentage probability
+    probability_percent = round(probability * 100, 2)
+    
+    # Classification threshold
+    threshold = 0.7  # Adjust threshold if necessary
+    is_pneumonia = probability > threshold
+
+    # Determine result message
+    result_text = f"Pneumonia" if is_pneumonia else f"Not Pneumonia"
+
+    # Remove the temporary file
+    os.remove(file_path)
+
+    # Render the result page with prediction details
+    return render_template('result.html', result=result_text, probability=probability_percent)
+
+if __name__ == '__main__':
+    app.run(debug=True)

load_and_display_xray_images.py

@@ -0,0 +1,79 @@
+import os
+import numpy as np
+import cv2
+from sklearn.model_selection import train_test_split
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
+from tensorflow.keras.callbacks import ModelCheckpoint
+
+# Function to load images from specified folders
+def load_images_from_folders(folders):
+    images = []
+    for folder in folders:
+        for filename in os.listdir(folder):
+            if filename.lower().endswith(('.png', '.jpg', '.jpeg')):  # Check for valid image extensions
+                img = cv2.imread(os.path.join(folder, filename), cv2.IMREAD_GRAYSCALE)  # Read image as grayscale
+                if img is not None:
+                    img = cv2.resize(img, (224, 224))  # Resize to 224x224 pixels
+                    img = img.astype(np.float32)  # Ensure the image is in float32 format
+                    img /= 255.0  # Normalize to [0, 1]
+                    images.append(img)
+                else:
+                    print(f"Failed to load image: {filename}")
+    return np.array(images)
+
+# Load normal and pneumonia images
+normal_folders = [
+    os.path.join('chest-xray-pneumonia', 'chest_xray', 'test', 'NORMAL'),
+    os.path.join('chest-xray-pneumonia', 'chest_xray', 'train', 'NORMAL'),
+    os.path.join('chest-xray-pneumonia', 'chest_xray', 'val', 'NORMAL'),
+]
+pneumonia_folders = [
+    os.path.join('chest-xray-pneumonia', 'chest_xray', 'test', 'PNEUMONIA'),
+    os.path.join('chest-xray-pneumonia', 'chest_xray', 'train', 'PNEUMONIA'),
+    os.path.join('chest-xray-pneumonia', 'chest_xray', 'val', 'PNEUMONIA'),
+]
+
+normal_images = load_images_from_folders(normal_folders)
+pneumonia_images = load_images_from_folders(pneumonia_folders)
+
+# Print the number of images loaded for each category
+print(f"Loaded {len(normal_images)} normal images.")
+print(f"Loaded {len(pneumonia_images)} pneumonia images.")
+
+# Prepare dataset for machine learning
+images = np.concatenate([normal_images, pneumonia_images])  # Combine normal and pneumonia images
+labels = np.array([0] * len(normal_images) + [1] * len(pneumonia_images))  # Create labels
+
+# Split the dataset into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(images, labels, test_size=0.2, random_state=42)
+
+# Convert to RGB format
+X_train_rgb = np.stack([cv2.cvtColor((img * 255).astype(np.uint8), cv2.COLOR_GRAY2RGB) for img in X_train])
+X_test_rgb = np.stack([cv2.cvtColor((img * 255).astype(np.uint8), cv2.COLOR_GRAY2RGB) for img in X_test])
+
+print(f'Training data shape: {X_train_rgb.shape}, Training labels shape: {y_train.shape}')
+print(f'Testing data shape: {X_test_rgb.shape}, Testing labels shape: {y_test.shape}')
+
+# Build a simple CNN model
+model = Sequential([
+    Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)),
+    MaxPooling2D(pool_size=(2, 2)),
+    Conv2D(64, (3, 3), activation='relu'),
+    MaxPooling2D(pool_size=(2, 2)),
+    Flatten(),
+    Dense(128, activation='relu'),
+    Dense(1, activation='sigmoid')  # Binary classification
+])
+
+model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+
+# Set up model checkpointing with the new file extension
+checkpoint = ModelCheckpoint('pneumonia_model.keras', save_best_only=True, monitor='val_loss', mode='min')
+
+# Train the model
+model.fit(X_train_rgb, y_train, validation_data=(X_test_rgb, y_test), epochs=10, callbacks=[checkpoint])
+
+# Save the model in the new format
+model.save('pneumonia_model_final.keras')
+print("Model saved as 'pneumonia_model_final.keras'")

pneumonia_model.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7063c5dcfc8c5fe59992f6ffeca3e0946b7b7ecbac960e2fb51e8d55a5e288d7
+size 286927607

requirements.txt

@@ -0,0 +1,5 @@
+Flask
+numpy
+opencv-python
+tensorflow
+streamlit

streamlit_app.py

@@ -0,0 +1,5 @@
+# streamlit_app.py
+import streamlit as st
+
+x = st.slider('Select a value')
+st.write(x, 'squared is', x * x)