Update xray_classifier.py

xray_classifier.py

@@ -1,106 +1,106 @@
-
-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)
-
-
+
+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="0.0.0.0", port=7860)
+
+