--- a
+++ b/main.py
@@ -0,0 +1,297 @@
+<<<<<<< HEAD
+import os
+import cv2
+import numpy as np
+from flask import Flask, render_template, request, jsonify, send_from_directory
+import torch
+from torchvision import transforms, models
+from PIL import Image
+from pytorch_grad_cam import GradCAM
+from pytorch_grad_cam.utils.image import show_cam_on_image, preprocess_image
+import uuid
+
+
+app = Flask(__name__)
+
+# Class names
+class_names = ['Covid-19', 'Emphysema', 'Healthy', 'Pneumonia', 'Tuberculosis', 'Random']
+
+# Load the pre-trained ResNet101 model
+model = models.resnet101(pretrained=False)
+model.fc = torch.nn.Linear(in_features=2048, out_features=len(class_names))
+checkpoint_path = "resnet101_state_dict.pth"  
+model.load_state_dict(torch.load(checkpoint_path, map_location=torch.device('cpu')))
+model.eval()
+
+# Image transformations
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+
+# Function to preprocess image
+def preprocess_image(image_path):
+    image = Image.open(image_path).convert('RGB')
+    return transform(image).unsqueeze(0)
+
+# Generate visualizations for Grad-CAM and other images
+def generate_visualizations(image_path):
+    original_image = cv2.imread(image_path)
+    original_image_rgb = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)
+
+    # Grayscale image
+    grayscale_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2GRAY)
+
+    # Histogram equalized image
+    equalized_image = cv2.equalizeHist(grayscale_image)
+
+    # Edge detection result
+    edges_image = cv2.Canny(grayscale_image, 50, 150)
+
+    # Segmented image
+    _, segmented_image = cv2.threshold(grayscale_image, 127, 255, cv2.THRESH_BINARY)
+
+    # Grad-CAM Visualization
+    input_tensor = preprocess_image(image_path)
+    target_layer = model.layer4[-1]  # Last layer of ResNet101
+    cam = GradCAM(model=model, target_layers=[target_layer])
+    grayscale_cam = cam(input_tensor=input_tensor)[0]
+
+    # Normalize the Grad-CAM output to range [0, 1]
+    grayscale_cam = np.maximum(grayscale_cam, 0)
+    grayscale_cam = grayscale_cam / np.max(grayscale_cam)
+
+    # Apply the Grad-CAM heatmap on the image with a red color map
+    input_image_rgb_resized = cv2.resize(original_image_rgb, (224, 224))
+
+    # Using a custom colormap (ensure red regions are highlighted)
+    heatmap = cv2.applyColorMap(np.uint8(255 * grayscale_cam), cv2.COLORMAP_JET)
+    grad_cam_image = cv2.addWeighted(input_image_rgb_resized, 0.7, heatmap, 0.3, 0)
+
+    # Mask for ROI extraction (high-confidence areas are those with grayscale_cam > 0.5)
+    roi_mask = (grayscale_cam > 0.5).astype(np.uint8)
+    roi = cv2.bitwise_and(input_image_rgb_resized, input_image_rgb_resized, mask=roi_mask)
+
+    # Generate unique file name
+    file_id = str(uuid.uuid4())
+
+    # Save images to server and return file paths
+    visualization_paths = {
+        "original": f'/uploads/{file_id}_original.png',
+        "grayscale": f'/uploads/{file_id}_grayscale.png',
+        "equalized": f'/uploads/{file_id}_equalized.png',
+        "edges": f'/uploads/{file_id}_edges.png',
+        "segmented": f'/uploads/{file_id}_segmented.png',
+        "grad_cam": f'/uploads/{file_id}_grad_cam.png',
+        "roi": f'/uploads/{file_id}_roi.png'
+    }
+
+    # Create uploads directory if it doesn't exist
+    os.makedirs('uploads', exist_ok=True)
+
+    # Save the visualizations as images
+    cv2.imwrite(f'uploads/{file_id}_original.png', original_image)
+    cv2.imwrite(f'uploads/{file_id}_grayscale.png', grayscale_image)
+    cv2.imwrite(f'uploads/{file_id}_equalized.png', equalized_image)
+    cv2.imwrite(f'uploads/{file_id}_edges.png', edges_image)
+    cv2.imwrite(f'uploads/{file_id}_segmented.png', segmented_image)
+    cv2.imwrite(f'uploads/{file_id}_grad_cam.png', grad_cam_image)
+    cv2.imwrite(f'uploads/{file_id}_roi.png', roi)
+
+    return visualization_paths
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    if 'file' not in request.files:
+        return jsonify({"error": "No file part"}), 400
+
+    file = request.files['file']
+    if file.filename == '':
+        return jsonify({"error": "No selected file"}), 400
+
+    image_id = str(uuid.uuid4())
+    file_path = os.path.join('uploads', f'{image_id}.png')
+    os.makedirs('uploads', exist_ok=True)
+    file.save(file_path)
+
+    input_tensor = preprocess_image(file_path)
+    with torch.no_grad():
+        output = model(input_tensor)
+        predicted_class = output.argmax(dim=1).item()
+        confidence_score = torch.nn.functional.softmax(output, dim=1)[0, predicted_class].item() * 100
+
+    result = {
+        "predicted_class": class_names[predicted_class],
+        "confidence_score": f"{confidence_score:.2f}%",
+    }
+
+    if class_names[predicted_class] == "Random":
+        result["message"] = "Sorry... You inserted a Non X-ray image. Please try again with a chest x-ray image. Thank you."
+        result["visualizations"] = None
+    else:
+        visualizations = generate_visualizations(file_path)
+        result["visualizations"] = visualizations
+
+    return jsonify(result)
+
+# To serve the images from the 'uploads' folder correctly
+@app.route('/uploads/<filename>')
+def uploaded_file(filename):
+    return send_from_directory('uploads', filename)
+
+if __name__ == '__main__':
+    app.run(debug=True)
+=======
+import os
+import cv2
+import numpy as np
+from flask import Flask, render_template, request, jsonify, send_from_directory
+import torch
+from torchvision import transforms, models
+from PIL import Image
+from pytorch_grad_cam import GradCAM
+from pytorch_grad_cam.utils.image import show_cam_on_image, preprocess_image
+import uuid
+
+
+app = Flask(__name__)
+
+# Class names
+class_names = ['Covid-19', 'Emphysema', 'Healthy', 'Pneumonia', 'Tuberculosis', 'Random']
+
+# Load the pre-trained ResNet101 model
+model = models.resnet101(pretrained=False)
+model.fc = torch.nn.Linear(in_features=2048, out_features=len(class_names))
+checkpoint_path = "resnet101_state_dict.pth"  
+model.load_state_dict(torch.load(checkpoint_path, map_location=torch.device('cpu')))
+model.eval()
+
+# Image transformations
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+
+# Function to preprocess image
+def preprocess_image(image_path):
+    image = Image.open(image_path).convert('RGB')
+    return transform(image).unsqueeze(0)
+
+# Generate visualizations for Grad-CAM and other images
+def generate_visualizations(image_path):
+    original_image = cv2.imread(image_path)
+    original_image_rgb = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)
+
+    # Grayscale image
+    grayscale_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2GRAY)
+
+    # Histogram equalized image
+    equalized_image = cv2.equalizeHist(grayscale_image)
+
+    # Edge detection result
+    edges_image = cv2.Canny(grayscale_image, 50, 150)
+
+    # Segmented image
+    _, segmented_image = cv2.threshold(grayscale_image, 127, 255, cv2.THRESH_BINARY)
+
+    # Grad-CAM Visualization
+    input_tensor = preprocess_image(image_path)
+    target_layer = model.layer4[-1]  # Last layer of ResNet101
+    cam = GradCAM(model=model, target_layers=[target_layer])
+    grayscale_cam = cam(input_tensor=input_tensor)[0]
+
+    # Normalize the Grad-CAM output to range [0, 1]
+    grayscale_cam = np.maximum(grayscale_cam, 0)
+    grayscale_cam = grayscale_cam / np.max(grayscale_cam)
+
+    # Apply the Grad-CAM heatmap on the image with a red color map
+    input_image_rgb_resized = cv2.resize(original_image_rgb, (224, 224))
+
+    # Using a custom colormap (ensure red regions are highlighted)
+    heatmap = cv2.applyColorMap(np.uint8(255 * grayscale_cam), cv2.COLORMAP_JET)
+    grad_cam_image = cv2.addWeighted(input_image_rgb_resized, 0.7, heatmap, 0.3, 0)
+
+    # Mask for ROI extraction (high-confidence areas are those with grayscale_cam > 0.5)
+    roi_mask = (grayscale_cam > 0.5).astype(np.uint8)
+    roi = cv2.bitwise_and(input_image_rgb_resized, input_image_rgb_resized, mask=roi_mask)
+
+    # Generate unique file name
+    file_id = str(uuid.uuid4())
+
+    # Save images to server and return file paths
+    visualization_paths = {
+        "original": f'/uploads/{file_id}_original.png',
+        "grayscale": f'/uploads/{file_id}_grayscale.png',
+        "equalized": f'/uploads/{file_id}_equalized.png',
+        "edges": f'/uploads/{file_id}_edges.png',
+        "segmented": f'/uploads/{file_id}_segmented.png',
+        "grad_cam": f'/uploads/{file_id}_grad_cam.png',
+        "roi": f'/uploads/{file_id}_roi.png'
+    }
+
+    # Create uploads directory if it doesn't exist
+    os.makedirs('uploads', exist_ok=True)
+
+    # Save the visualizations as images
+    cv2.imwrite(f'uploads/{file_id}_original.png', original_image)
+    cv2.imwrite(f'uploads/{file_id}_grayscale.png', grayscale_image)
+    cv2.imwrite(f'uploads/{file_id}_equalized.png', equalized_image)
+    cv2.imwrite(f'uploads/{file_id}_edges.png', edges_image)
+    cv2.imwrite(f'uploads/{file_id}_segmented.png', segmented_image)
+    cv2.imwrite(f'uploads/{file_id}_grad_cam.png', grad_cam_image)
+    cv2.imwrite(f'uploads/{file_id}_roi.png', roi)
+
+    return visualization_paths
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    if 'file' not in request.files:
+        return jsonify({"error": "No file part"}), 400
+
+    file = request.files['file']
+    if file.filename == '':
+        return jsonify({"error": "No selected file"}), 400
+
+    image_id = str(uuid.uuid4())
+    file_path = os.path.join('uploads', f'{image_id}.png')
+    os.makedirs('uploads', exist_ok=True)
+    file.save(file_path)
+
+    input_tensor = preprocess_image(file_path)
+    with torch.no_grad():
+        output = model(input_tensor)
+        predicted_class = output.argmax(dim=1).item()
+        confidence_score = torch.nn.functional.softmax(output, dim=1)[0, predicted_class].item() * 100
+
+    result = {
+        "predicted_class": class_names[predicted_class],
+        "confidence_score": f"{confidence_score:.2f}%",
+    }
+
+    if class_names[predicted_class] == "Random":
+        result["message"] = "Sorry... You inserted a Non X-ray image. Please try again with a chest x-ray image. Thank you."
+        result["visualizations"] = None
+    else:
+        visualizations = generate_visualizations(file_path)
+        result["visualizations"] = visualizations
+
+    return jsonify(result)
+
+# To serve the images from the 'uploads' folder correctly
+@app.route('/uploads/<filename>')
+def uploaded_file(filename):
+    return send_from_directory('uploads', filename)
+
+if __name__ == '__main__':
+    app.run(debug=True)
+>>>>>>> 5676c55 (Add ResNet101 model using Git LFS)