put comments

pages/Model_Evaluation.py

@@ -161,64 +161,76 @@ with st.expander("ℹ️ **What is Model Evaluation?**", expanded=True):
 
 
 # ---- Evaluation Logic ----
+# Check if evaluation should be triggered
 if st.session_state.trigger_eval:
     st.markdown("### ⏱️ Evaluation Results")
 
+    # Start timing the evaluation
     start_time = time.time()
-    y_true = []
-    y_pred = []
-    y_score = []
-    misclassified_images = []
+    y_true = []  # Ground truth labels
+    y_pred = []  # Predicted labels
+    y_score = []  # Raw model outputs
+    misclassified_images = []  # List to store misclassified samples
 
-    total_batches = len(test_loader)
-    progress_bar = st.progress(0)
-    status_text = st.empty()
-    stop_info = st.empty()
+    total_batches = len(test_loader)  # Total number of batches
+    progress_bar = st.progress(0)  # Initialize progress bar
+    status_text = st.empty()  # Placeholder for status updates
+    stop_info = st.empty()  # Placeholder for stop message
 
+    # Disable gradient calculation for faster evaluation
     with torch.no_grad():
         for i, (images, labels) in enumerate(test_loader):
+            # Allow user to stop the evaluation
             if st.session_state.stop_eval:
                 stop_info.warning("🚩 Evaluation stopped by user.")
                 break
 
+            # Run model on input images
             outputs = model(images)
-            _, predicted = torch.max(outputs, 1)
+            _, predicted = torch.max(outputs, 1)  # Get predicted class
             y_true.extend(labels.numpy())
             y_pred.extend(predicted.numpy())
             y_score.extend(outputs.detach().numpy())
 
+            # Store misclassified samples
             for j in range(len(labels)):
                 if predicted[j] != labels[j]:
                     misclassified_images.append((images[j], predicted[j].item(), labels[j].item()))
 
+            # Update progress bar and status text
             percent_complete = (i + 1) / total_batches
             progress_bar.progress(min(percent_complete, 1.0))
             status_text.text(f"Evaluating on Test Set: {int(percent_complete * 100)}% | Batch {i+1}/{total_batches}")
-            time.sleep(0.1)
+            time.sleep(0.1)  # Add delay for UI responsiveness
 
     end_time = time.time()
-    eval_time = end_time - start_time
+    eval_time = end_time - start_time  # Total evaluation time
 
+    # Finalize evaluation if not stopped
     if not st.session_state.stop_eval:
         st.session_state.evaluation_done = True
         st.session_state.trigger_eval = False  # ✅ Reset the trigger
         st.success(f"✅ Evaluation completed in **{eval_time:.2f} seconds**")
 
+        # Generate classification report and display as a DataFrame
         report = classification_report(y_true, y_pred, target_names=class_names, output_dict=True)
         report_df = pd.DataFrame(report).transpose()
         st.dataframe(report_df.style.format("{:.2f}"))
 
+        # Initialize PDF report
         pdf = FPDF()
         pdf.add_page()
         pdf.set_font("Arial", size=12)
         pdf.cell(200, 10, txt=clean_text("Classification Report"), ln=True, align='C')
 
+        # Add table headers
         col_widths = [40, 40, 40, 40]
         headers = ["Class", "Precision", "Recall", "F1-Score"]
         for i, header in enumerate(headers):
             pdf.cell(col_widths[i], 10, header, border=1)
         pdf.ln()
 
+        # Add metrics for each class
         for idx, row in report_df.iterrows():
             if idx in ['accuracy', 'macro avg', 'weighted avg']:
                 continue
@@ -228,6 +240,7 @@ if st.session_state.trigger_eval:
             pdf.cell(col_widths[3], 10, f"{row['f1-score']:.2f}", border=1)
             pdf.ln()
 
+        # Create and display confusion matrix
         cm = confusion_matrix(y_true, y_pred)
         fig_cm, ax = plt.subplots()
         sns.heatmap(cm, annot=True, fmt='d', xticklabels=class_names, yticklabels=class_names, cmap="Blues", ax=ax)
@@ -235,12 +248,15 @@ if st.session_state.trigger_eval:
         ax.set_ylabel('True')
         ax.set_title("Confusion Matrix")
         st.pyplot(fig_cm)
+
+        # Save confusion matrix to PDF
         cm_path = "confusion_matrix.png"
         fig_cm.savefig(cm_path, format='png', dpi=300, bbox_inches='tight')
         plt.close(fig_cm)
         if os.path.exists(cm_path):
             pdf.image(cm_path, x=10, y=None, w=180)
 
+        # Create and display ROC curve for each class
         y_true_bin = label_binarize(y_true, classes=list(range(len(class_names))))
         y_score_np = np.array(y_score)
         fig_roc, ax = plt.subplots()
@@ -249,26 +265,30 @@ if st.session_state.trigger_eval:
             roc_auc = auc(fpr, tpr)
             ax.plot(fpr, tpr, label=f'{class_names[i]} (AUC = {roc_auc:.2f})')
 
-        ax.plot([0, 1], [0, 1], 'k--')
+        ax.plot([0, 1], [0, 1], 'k--')  # Diagonal reference line
         ax.set_xlabel('False Positive Rate')
         ax.set_ylabel('True Positive Rate')
         ax.set_title('Multi-class ROC Curve')
         ax.legend(loc='lower right')
         st.pyplot(fig_roc)
+
+        # Save ROC curve to PDF
         roc_path = "roc_curve.png"
         fig_roc.savefig(roc_path, format='png', dpi=300, bbox_inches='tight')
         plt.close(fig_roc)
         if os.path.exists(roc_path):
             pdf.image(roc_path, x=10, y=None, w=180)
 
+        # Show misclassified samples (up to 5)
         st.markdown("### ❌ Misclassified Samples")
         fig_mis, axs = plt.subplots(1, min(5, len(misclassified_images)), figsize=(15, 4))
         for idx, (img, pred, true) in enumerate(misclassified_images[:5]):
-            axs[idx].imshow(img.permute(1, 2, 0))
+            axs[idx].imshow(img.permute(1, 2, 0))  # Convert tensor to image format
             axs[idx].set_title(f"True: {class_names[true]}\nPred: {class_names[pred]}")
             axs[idx].axis('off')
         st.pyplot(fig_mis)
 
+        # Save PDF and provide download button
         output_pdf = "evaluation_report.pdf"
         pdf.output(output_pdf)
         with open(output_pdf, "rb") as f:

training/training.ipynb

@@ -1017,7 +1017,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "id": "560a2a1b",
    "metadata": {},
    "outputs": [
@@ -1035,38 +1035,70 @@
    "source": [
     "from sklearn.metrics import roc_curve, auc\n",
     "from sklearn.preprocessing import label_binarize\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import torch\n",
+    "\n",
+    "# Number of classes for Diabetic Retinopathy (DR) classification\n",
+    "n_classes = 5  \n",
     "\n",
-    "n_classes = 5  # for DR classification\n",
+    "# Convert class labels to one-hot encoded format (needed for multi-class ROC)\n",
+    "# Example: label 2 becomes [0, 0, 1, 0, 0]\n",
     "y_true_bin = label_binarize(all_labels, classes=[0, 1, 2, 3, 4])\n",
+    "\n",
+    "# Will hold predicted probabilities for each class\n",
     "y_scores = []\n",
     "\n",
+    "# Set model to evaluation mode\n",
     "model.eval()\n",
+    "\n",
+    "# Disable gradient calculation for faster inference\n",
     "with torch.no_grad():\n",
     "    for inputs, labels in test_loader:\n",
     "        inputs = inputs.to(device)\n",
+    "        \n",
+    "        # Forward pass through the model\n",
     "        outputs = model(inputs)\n",
+    "        \n",
+    "        # Apply softmax to get class probabilities\n",
     "        probs = torch.softmax(outputs, dim=1)\n",
+    "        \n",
+    "        # Append the probabilities to y_scores list\n",
     "        y_scores.extend(probs.cpu().numpy())\n",
     "\n",
-    "# Compute ROC curve and AUC for each class\n",
-    "fpr, tpr, roc_auc = dict(), dict(), dict()\n",
+    "# Convert the list of predictions to a NumPy array\n",
     "y_scores = np.array(y_scores)\n",
     "\n",
+    "# Initialize dictionaries to store False Positive Rate (FPR), True Positive Rate (TPR), and AUC\n",
+    "fpr, tpr, roc_auc = dict(), dict(), dict()\n",
+    "\n",
+    "# Compute ROC curve and AUC for each class\n",
     "for i in range(n_classes):\n",
+    "    # Calculate FPR and TPR for class `i`\n",
     "    fpr[i], tpr[i], _ = roc_curve(y_true_bin[:, i], y_scores[:, i])\n",
+    "    \n",
+    "    # Calculate Area Under Curve (AUC) for class `i`\n",
     "    roc_auc[i] = auc(fpr[i], tpr[i])\n",
     "\n",
-    "# Plot all ROC curves\n",
+    "# Plot ROC curves for all classes\n",
     "plt.figure(figsize=(10, 7))\n",
+    "\n",
     "for i in range(n_classes):\n",
     "    plt.plot(fpr[i], tpr[i], label=f'{class_names[i]} (AUC = {roc_auc[i]:.2f})')\n",
     "\n",
+    "# Plot the diagonal line (chance level)\n",
     "plt.plot([0, 1], [0, 1], 'k--')\n",
+    "\n",
+    "# Set plot title and axis labels\n",
     "plt.title('Multi-class ROC Curve')\n",
     "plt.xlabel('False Positive Rate')\n",
     "plt.ylabel('True Positive Rate')\n",
+    "\n",
+    "# Add legend and grid\n",
     "plt.legend(loc='lower right')\n",
     "plt.grid(True)\n",
+    "\n",
+    "# Show the plot\n",
     "plt.show()\n"
    ]
   },
@@ -1474,52 +1506,6 @@
     "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": {