file added

app.py

@@ -198,7 +198,7 @@ def predict_image(image):
 
         if hasattr(model, 'names'):
             class_name = model.names[predicted_class_id]
-            prediction_result = f"Predicted Class: {class_name}\nConfidence: {confidence_score:.4f}"
+            prediction_result = f"Predicted Class: {class_name}\nConfidence Score: {confidence_score:.4f}"
             description = get_description(class_name)
         else:
             prediction_result = f"Predicted Class ID: {predicted_class_id}\nConfidence: {confidence_score:.4f}"

pred.py

@@ -48,63 +48,3 @@ 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()