again

pages/Model_Evaluation.py

@@ -105,12 +105,12 @@ def load_test_data(csv_path):
 def load_model():
     model = models.densenet121(pretrained=False)
     model.classifier = nn.Linear(model.classifier.in_features, len(class_names))
-    model.load_state_dict(torch.load(r"D:\DR_Classification\Model\Pretrained_Densenet-121.pth", map_location=torch.device('cpu')))
+    model.load_state_dict(torch.load(r"Model/Pretrained_Densenet-121.pth", map_location=torch.device('cpu')))
     model.eval()
     return model
 
 # ---- Main UI Buttons ----
-csv_path = r"D:\DR_Classification\dataset\Splitted_data\splits\test_labels.csv"
+csv_path = r"D:\DR_Classification\splits\test_labels.csv"
 model = load_model()
 test_loader = load_test_data(csv_path)
 

pages/Upload_and_Predict.py

@@ -57,7 +57,7 @@ class_names = ['No DR', 'Mild', 'Moderate', 'Severe', 'Proliferative DR']
 
 # Load sample images from CSV with proper label mapping
 @st.cache_data
-def load_sample_images_from_csv(csv_path=r'D:\DR_Classification\dataset\Splitted_data\splits\test_labels.csv'):
+def load_sample_images_from_csv(csv_path=r'D:\DR_Classification\splits\test_labels.csv'):
     df = pd.read_csv(csv_path)
     samples = defaultdict(list)
 

training/training.ipynb

@@ -137,7 +137,7 @@
     "df['label'] = df['diagnosis']\n",
     "\n",
     "# Create output directories for storing train and test images\n",
-    "output_root = \"D:\\\\DR_Classification\\\\dataset\\\\Splitted_data\\\\splits\" #change path to your output folder\n",
+    "output_root = \"D:\\\\DR_Classification\\\\splits\" #change path to your output folder\n",
     "os.makedirs(os.path.join(output_root, \"train\"), exist_ok=True)  # Creates 'train' folder if not existing\n",
     "os.makedirs(os.path.join(output_root, \"test\"), exist_ok=True)   # Creates 'test' folder if not existing\n",
     "\n",
@@ -267,8 +267,8 @@
    "outputs": [],
    "source": [
     "# Load the split CSVs\n",
-    "train_df = pd.read_csv(\"D:/DR_Classification/dataset/Splitted_data/splits/train_labels.csv\") #change path to your csv file\n",
-    "test_df = pd.read_csv(\"D:/DR_Classification/dataset/Splitted_data/splits/test_labels.csv\") \n",
+    "train_df = pd.read_csv(\"D:/DR_Classification/splits/train_labels.csv\")\n",
+    "test_df = pd.read_csv(\"D:/DR_Classification/splits/test_labels.csv\")\n",
     "\n",
     "# Extract paths and labels\n",
     "train_paths = train_df['new_path'].tolist()\n",
@@ -1468,12 +1468,58 @@
     "\n",
     "# Example usage:\n",
     "class_names = ['No DR', 'Mild', 'Moderate', 'Severe', 'Proliferative DR']  # Modify as per your dataset\n",
-    "image_path = r'D:\\DR_Classification\\dataset\\Splitted_data\\splits\\train\\007-0025-000.jpg'  # Replace with your image path\n",
+    "image_path = r'D:\\DR_Classification\\splits\\train\\007-0025-000.jpg'  # Replace with your image path\n",
     "predicted_class, confidence_percentage = predict_image(model, image_path, class_names, device='cpu')\n",
     "\n",
     "print(f\"Predicted Class: {predicted_class}\")\n",
     "print(f\"Confidence: {confidence_percentage:.2f}%\")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "eb2308ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'D:\\\\DR_Classification\\\\dataset\\\\splits\\\\test_labels.csv'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mFileNotFoundError\u001b[39m                         Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 12\u001b[39m\n\u001b[32m      9\u001b[39m new_dir = \u001b[33mr\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mD:\u001b[39m\u001b[33m\\\u001b[39m\u001b[33mDR_Classification\u001b[39m\u001b[33m\\\u001b[39m\u001b[33mdataset\u001b[39m\u001b[33m\\\u001b[39m\u001b[33msplitted-data\u001b[39m\u001b[33m\\\u001b[39m\u001b[33mtest\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m     11\u001b[39m \u001b[38;5;66;03m# === Load the CSV ===\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m12\u001b[39m df = \u001b[43mpd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mread_csv\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcsv_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     14\u001b[39m \u001b[38;5;66;03m# === Replace old path with new path in 'new_path' column ===\u001b[39;00m\n\u001b[32m     15\u001b[39m df[\u001b[33m'\u001b[39m\u001b[33mnew_path\u001b[39m\u001b[33m'\u001b[39m] = df[\u001b[33m'\u001b[39m\u001b[33mnew_path\u001b[39m\u001b[33m'\u001b[39m].str.replace(old_dir, new_dir, regex=\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32md:\\DR_Classification\\.venv\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1026\u001b[39m, in \u001b[36mread_csv\u001b[39m\u001b[34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[39m\n\u001b[32m   1013\u001b[39m kwds_defaults = _refine_defaults_read(\n\u001b[32m   1014\u001b[39m     dialect,\n\u001b[32m   1015\u001b[39m     delimiter,\n\u001b[32m   (...)\u001b[39m\u001b[32m   1022\u001b[39m     dtype_backend=dtype_backend,\n\u001b[32m   1023\u001b[39m )\n\u001b[32m   1024\u001b[39m kwds.update(kwds_defaults)\n\u001b[32m-> \u001b[39m\u001b[32m1026\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_read\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32md:\\DR_Classification\\.venv\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:620\u001b[39m, in \u001b[36m_read\u001b[39m\u001b[34m(filepath_or_buffer, kwds)\u001b[39m\n\u001b[32m    617\u001b[39m _validate_names(kwds.get(\u001b[33m\"\u001b[39m\u001b[33mnames\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[32m    619\u001b[39m \u001b[38;5;66;03m# Create the parser.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m620\u001b[39m parser = \u001b[43mTextFileReader\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath_or_buffer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    622\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;129;01mor\u001b[39;00m iterator:\n\u001b[32m    623\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m parser\n",
+      "\u001b[36mFile \u001b[39m\u001b[32md:\\DR_Classification\\.venv\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1620\u001b[39m, in \u001b[36mTextFileReader.__init__\u001b[39m\u001b[34m(self, f, engine, **kwds)\u001b[39m\n\u001b[32m   1617\u001b[39m     \u001b[38;5;28mself\u001b[39m.options[\u001b[33m\"\u001b[39m\u001b[33mhas_index_names\u001b[39m\u001b[33m\"\u001b[39m] = kwds[\u001b[33m\"\u001b[39m\u001b[33mhas_index_names\u001b[39m\u001b[33m\"\u001b[39m]\n\u001b[32m   1619\u001b[39m \u001b[38;5;28mself\u001b[39m.handles: IOHandles | \u001b[38;5;28;01mNone\u001b[39;00m = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1620\u001b[39m \u001b[38;5;28mself\u001b[39m._engine = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_make_engine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mengine\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32md:\\DR_Classification\\.venv\\Lib\\site-packages\\pandas\\io\\parsers\\readers.py:1880\u001b[39m, in \u001b[36mTextFileReader._make_engine\u001b[39m\u001b[34m(self, f, engine)\u001b[39m\n\u001b[32m   1878\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[32m   1879\u001b[39m         mode += \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m-> \u001b[39m\u001b[32m1880\u001b[39m \u001b[38;5;28mself\u001b[39m.handles = \u001b[43mget_handle\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m   1881\u001b[39m \u001b[43m    \u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1882\u001b[39m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1883\u001b[39m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mencoding\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1884\u001b[39m \u001b[43m    \u001b[49m\u001b[43mcompression\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mcompression\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1885\u001b[39m \u001b[43m    \u001b[49m\u001b[43mmemory_map\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmemory_map\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1886\u001b[39m \u001b[43m    \u001b[49m\u001b[43mis_text\u001b[49m\u001b[43m=\u001b[49m\u001b[43mis_text\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1887\u001b[39m \u001b[43m    \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mencoding_errors\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstrict\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1888\u001b[39m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mstorage_options\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m   1889\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1890\u001b[39m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m.handles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m   1891\u001b[39m f = \u001b[38;5;28mself\u001b[39m.handles.handle\n",
+      "\u001b[36mFile \u001b[39m\u001b[32md:\\DR_Classification\\.venv\\Lib\\site-packages\\pandas\\io\\common.py:873\u001b[39m, in \u001b[36mget_handle\u001b[39m\u001b[34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[39m\n\u001b[32m    868\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(handle, \u001b[38;5;28mstr\u001b[39m):\n\u001b[32m    869\u001b[39m     \u001b[38;5;66;03m# Check whether the filename is to be opened in binary mode.\u001b[39;00m\n\u001b[32m    870\u001b[39m     \u001b[38;5;66;03m# Binary mode does not support 'encoding' and 'newline'.\u001b[39;00m\n\u001b[32m    871\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m ioargs.encoding \u001b[38;5;129;01mand\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33mb\u001b[39m\u001b[33m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m ioargs.mode:\n\u001b[32m    872\u001b[39m         \u001b[38;5;66;03m# Encoding\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m873\u001b[39m         handle = \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[32m    874\u001b[39m \u001b[43m            \u001b[49m\u001b[43mhandle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    875\u001b[39m \u001b[43m            \u001b[49m\u001b[43mioargs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    876\u001b[39m \u001b[43m            \u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m=\u001b[49m\u001b[43mioargs\u001b[49m\u001b[43m.\u001b[49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    877\u001b[39m \u001b[43m            \u001b[49m\u001b[43merrors\u001b[49m\u001b[43m=\u001b[49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m    878\u001b[39m \u001b[43m            \u001b[49m\u001b[43mnewline\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[32m    879\u001b[39m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    880\u001b[39m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    881\u001b[39m         \u001b[38;5;66;03m# Binary mode\u001b[39;00m\n\u001b[32m    882\u001b[39m         handle = \u001b[38;5;28mopen\u001b[39m(handle, ioargs.mode)\n",
+      "\u001b[31mFileNotFoundError\u001b[39m: [Errno 2] No such file or directory: 'D:\\\\DR_Classification\\\\dataset\\\\splits\\\\test_labels.csv'"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# === File paths ===\n",
+    "csv_path = r\"D:\\DR_Classification\\dataset\\splits\\test_labels.csv\"\n",
+    "output_csv_path = r\"D:\\DR_Classification\\dataset\\Splitted_data\\splits\\test_labels.csv\"\n",
+    "\n",
+    "# === Old and new base directory paths ===\n",
+    "old_dir = r\"D:\\DR_Classification\\splits\\test\"\n",
+    "new_dir = r\"D:\\DR_Classification\\dataset\\splitted-data\\test\"\n",
+    "\n",
+    "# === Load the CSV ===\n",
+    "df = pd.read_csv(csv_path)\n",
+    "\n",
+    "# === Replace old path with new path in 'new_path' column ===\n",
+    "df['new_path'] = df['new_path'].str.replace(old_dir, new_dir, regex=False)\n",
+    "\n",
+    "# === Save the updated CSV ===\n",
+    "df.to_csv(output_csv_path, index=False)\n",
+    "\n",
+    "print(\"✅ CSV updated and saved at:\", output_csv_path)\n"
+   ]
   }
  ],
  "metadata": {