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+FROM python:3.9
+
+WORKDIR /app
+
+COPY . /app
+
+RUN pip install --upgrade pip
+RUN pip install -r requirements.txt
+
+EXPOSE 7860
+
+CMD ["python", "app.py"]

lung.py

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+
+import torch
+import torch.nn as nn
+from torchvision import models, transforms
+from flask import Flask, jsonify, request, render_template
+from PIL import Image
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+from PIL import Image
+import numpy as np
+import cv2
+import cv2
+import torch
+
+from pytorch_grad_cam import GradCAM
+from pytorch_grad_cam.utils.image import show_cam_on_image
+from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
+
+from flask_cors import CORS
+
+
+
+def get_edge_for_visualization(pil_img):
+  
+    w, h = pil_img.size
+    scale = 256 / min(w, h)
+    new_w, new_h = int(w * scale), int(h * scale)
+    resized = pil_img.resize((new_w, new_h), Image.BILINEAR)
+
+   
+    left = (resized.width - 224) // 2
+    top = (resized.height - 224) // 2
+    cropped = resized.crop((left, top, left + 224, top + 224))
+
+   
+    gray = cv2.cvtColor(np.array(cropped), cv2.COLOR_RGB2GRAY)
+    blurred = cv2.GaussianBlur(gray, (5, 5), 0)
+    edges = 255 - cv2.Canny(blurred, 30, 80)
+
+    
+    edge_rgb = np.stack([edges]*3, axis=-1).astype(np.float32) / 255.0
+
+    return edge_rgb
+
+app = Flask(__name__)
+CORS(app)
+os.makedirs("static", exist_ok=True)
+
+# Device setup
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+# Transform setup (same as training)
+data_transforms = transforms.Compose([
+    transforms.Resize(256),
+    transforms.CenterCrop(224),
+    transforms.ToTensor(),
+    transforms.Normalize(
+        mean=[0.485, 0.456, 0.406],
+        std=[0.229, 0.224, 0.225]
+    )
+])
+
+
+model = models.resnet18(pretrained=False)
+model.fc = nn.Linear(model.fc.in_features, 3) 
+
+model.load_state_dict(torch.load("lung_xray_classify.pth", map_location=device))
+model.to(device)
+model.eval()
+
+
+class_names = ['COVID', 'HEALTHY', 'PNEUMONIA']
+
+# @app.route("/")
+# def home():
+#     return render_template("index.html")
+
+@app.route("/predict_lung", methods=["POST"])
+def predict():
+    if "file" not in request.files:
+        return jsonify({"error": "No file provided"}), 400
+
+    file = request.files["file"]
+    filepath = os.path.join("static", file.filename)
+    file.save(filepath)
+
+    try:
+        image = Image.open(filepath).convert("RGB")
+        input_tensor = data_transforms(image).unsqueeze(0).to(device)  
+
+        with torch.no_grad():
+            output = model(input_tensor)
+            pred_idx = torch.argmax(output, dim=1).item()
+            pred_label = class_names[pred_idx]
+            classes = [ClassifierOutputTarget(pred_idx)];
+            target_layer = [model.layer4[-1]] 
+
+        cam = GradCAM(model = model, target_layers  = target_layer)
+
+        heatmap  = cam(input_tensor  = input_tensor, targets = classes);
+        edge_img = get_edge_for_visualization(image)
+        cam_image = show_cam_on_image(edge_img, heatmap[0], use_rgb=True)
+        input_img  = np.array(image);
+        input_img = input_img.astype(np.float32)/255
+        input_img = cv2.resize(input_img, (224,224))
+        cam_image_real =  show_cam_on_image(input_img, heatmap[0], use_rgb=True)    
+        cam_image_path = os.path.join("static", f"cam_{file.filename}")
+        cv2.imwrite(cam_image_path, cv2.cvtColor(cam_image_real, cv2.COLOR_RGB2BGR))
+        file={
+            "prediction": pred_label,
+            "cam_image_url": f"/static/lung_cam_{file.filename}"
+        }
+        print(file)
+        return jsonify(file)
+
+             
+
+
+
+        
+       
+    
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+
+if __name__ == '__main__':
+    app.run(debug=True)
+
+

lung_xray_classify.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3704b0e4825e0d049f13227d73e472fed43a8cd06d89dbb109a8d63084cc104c
+size 44792424

requirements.txt

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+flask
+flask-cors
+torch==2.2.0
+torchvision==0.17.0
+pillow
+numpy
+matplotlib
+opencv-python-headless
+git+https://github.com/jacobgil/pytorch-grad-cam.git