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resnet18_brain_tumor.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d369161266ab606a0feb71d303704d4f2eb53d116c6473f42794e2d6c31d8841
+size 44792676

xray_classifier.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
+
+
+
+
+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("resnet18_brain_tumor.pth", map_location=device))
+
+model.to(device)
+model.eval()
+
+
+class_names = [
+    "wound",
+    "brain",
+    "lung"
+]
+
+# @app.route("/")
+# def home():
+#     return render_template("index.html")
+
+@app.route("/predict_classify", 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]
+     
+
+       
+
+       
+        file={
+            "prediction": pred_label,
+            
+        }
+        print(file)
+        return jsonify(file)
+
+             
+
+
+
+        
+       
+    
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+
+if __name__ == '__main__':
+    app.run(debug=True, host="127.0.0.1", port=5000)
+
+