file added

app.py

@@ -15,6 +15,7 @@ import pathlib
 import cv2  # Import OpenCV
 import numpy as np
 
+
 # Pathlib adjustment for Windows compatibility
 # temp = pathlib.PosixPath
 # pathlib.PosixPath = pathlib.WindowsPath

pred.py

@@ -21,10 +21,11 @@ transform = transforms.Compose([
 ])
 im = transform(image).unsqueeze(0)  # Add batch dimension (BCHW)
 
-   
-output = model(im)
-print(output)
-
+try:   
+    output = model(im)
+    print(output)
+except Exception as e:
+        logger.error(f"Error in image prediction: {e}")
         
 
 # Get predictions
@@ -48,3 +49,63 @@ if hasattr(model, 'names'):
     class_name = model.names[predicted_class_id]
     print(f"Predicted Class Name: {class_name}")
 
+# import torch
+# import cv2  # Import OpenCV
+# from torchvision import transforms
+# import pathlib
+
+# # Pathlib adjustment for Windows compatibility
+# temp = pathlib.PosixPath
+# pathlib.PosixPath = pathlib.WindowsPath
+
+# # Load pre-trained YOLOv5 model
+# model = torch.hub.load(
+#     r'C:\Users\RESHMA R B\OneDrive\Documents\Desktop\project_without_malayalam\chatbot2\yolov5',
+#     'custom',
+#     path=r"C:\Users\RESHMA R B\OneDrive\Documents\Desktop\project_without_malayalam\chatbot2\models\best.pt",
+#     source="local"
+# )
+
+# # Set model to evaluation mode
+# model.eval()
+
+# # Define image transformations (for PyTorch)
+# transform = transforms.Compose([
+#     transforms.ToTensor(),  # Convert image to tensor
+#     transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),  # Normalize
+# ])
+
+# # Load and preprocess the image using OpenCV
+# img_path = r"C:\Users\RESHMA R B\OneDrive\Documents\Desktop\project_without_malayalam\chatbot2\ACNE.jpg"
+# image = cv2.imread(img_path)  # Load image in BGR format
+# image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)  # Convert BGR to RGB
+# image_resized = cv2.resize(image, (224, 224))  # Resize to match model's expected input size
+
+# # Transform the image for the model
+# im = transform(image_resized).unsqueeze(0)  # Add batch dimension (BCHW)
+
+# # Get predictions
+# with torch.no_grad():
+#     output = model(im)  # Raw model output (logits)
+
+# # Apply softmax to get confidence scores
+# softmax = torch.nn.Softmax(dim=1)
+# probs = softmax(output)
+
+# # Get the predicted class and its confidence score
+# predicted_class_id = torch.argmax(probs, dim=1).item()
+# confidence_score = probs[0, predicted_class_id].item()
+
+# # Print predicted class and confidence score
+# print(f"Predicted Class ID: {predicted_class_id}")
+# print(f"Confidence Score: {confidence_score:.4f}")
+
+# # Print predicted class name if available
+# if hasattr(model, 'names'):
+#     class_name = model.names[predicted_class_id]
+#     print(f"Predicted Class Name: {class_name}")
+
+
+# cv2.imshow("Input Image", image)  
+# cv2.waitKey(0)  
+# cv2.destroyAllWindows()