diff --git "a/data_for_ghazi/sd3.5_scrapped/graph.ipynb" "b/data_for_ghazi/sd3.5_scrapped/graph.ipynb"
new file mode 100644--- /dev/null
+++ "b/data_for_ghazi/sd3.5_scrapped/graph.ipynb"
@@ -0,0 +1,293 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Error plotting Protest And Activism: local variable 'stats' referenced before assignment\n",
+      "Error plotting People Faces: local variable 'stats' referenced before assignment\n",
+      "Error plotting Misinformation Graphics: local variable 'stats' referenced before assignment\n",
+      "Error plotting Ai Art: local variable 'stats' referenced before assignment\n",
+      "Error plotting Conspiracy Imagery: local variable 'stats' referenced before assignment\n",
+      "Error plotting Propaganda Images: local variable 'stats' referenced before assignment\n",
+      "Error plotting Hateful Memes: local variable 'stats' referenced before assignment\n",
+      "Error plotting Clickbait Thumbnails: local variable 'stats' referenced before assignment\n",
+      "Error plotting Fake Product Ads: local variable 'stats' referenced before assignment\n",
+      "\n",
+      "Category Statistics:\n",
+      "------------------------------------------------------------\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import json\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy import stats  # Fixed: Added the missing import\n",
+    "import os\n",
+    "\n",
+    "def process_metrics_file(file_path):\n",
+    "    try:\n",
+    "        with open(file_path, 'r') as f:\n",
+    "            data = json.load(f)\n",
+    "            # Extract combined_scores from the results array\n",
+    "            metrics = [result['metrics']['combined_score'] for result in data['results']]\n",
+    "        return np.array(metrics)\n",
+    "    except Exception as e:\n",
+    "        print(f\"Error processing {file_path}: {e}\")\n",
+    "        return np.array([])\n",
+    "\n",
+    "def create_multi_comparison_plot(directory_path):\n",
+    "    # Define a color palette for multiple datasets\n",
+    "    colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', \n",
+    "              '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']\n",
+    "    \n",
+    "    plt.figure(figsize=(15, 8))\n",
+    "    \n",
+    "    # Get all JSON files in the directory\n",
+    "    json_files = [f for f in os.listdir(directory_path) if f.endswith('.json')]\n",
+    "    \n",
+    "    # Store statistics for later printing\n",
+    "    all_stats = {}\n",
+    "    \n",
+    "    # Process each file\n",
+    "    for idx, file_name in enumerate(json_files):\n",
+    "        if 'category_averages' in file_name or 'visual_evidence' in file_name:\n",
+    "            continue  # Skip these files\n",
+    "            \n",
+    "        file_path = os.path.join(directory_path, file_name)\n",
+    "        \n",
+    "        # Get label from filename (remove _metrics and extension)\n",
+    "        label = file_name.replace('_metrics_', '_').replace('.json', '')\n",
+    "        label = label.split('_20')[0].replace('_', ' ').title()\n",
+    "        \n",
+    "        # Process metrics\n",
+    "        metrics = process_metrics_file(file_path)\n",
+    "        \n",
+    "        if len(metrics) > 0:\n",
+    "            try:\n",
+    "                # Plot histogram\n",
+    "                plt.hist(metrics, bins=30, density=True, alpha=0.3, \n",
+    "                        color=colors[idx % len(colors)], label=f'{label} Hist')\n",
+    "                \n",
+    "                # Plot KDE\n",
+    "                kde = stats.gaussian_kde(metrics)\n",
+    "                x_range = np.linspace(min(metrics), max(metrics), 100)\n",
+    "                plt.plot(x_range, kde(x_range), '-', color=colors[idx % len(colors)], \n",
+    "                        lw=2, label=f'{label} Density')\n",
+    "                \n",
+    "                # Add mean line\n",
+    "                mean_value = np.mean(metrics)\n",
+    "                plt.axvline(mean_value, color=colors[idx % len(colors)], \n",
+    "                           linestyle='dashed', linewidth=1, alpha=0.5)\n",
+    "                \n",
+    "                # Store statistics\n",
+    "                all_stats[label] = {\n",
+    "                    'mean': mean_value,\n",
+    "                    'median': np.median(metrics),\n",
+    "                    'std': np.std(metrics),\n",
+    "                    'count': len(metrics)\n",
+    "                }\n",
+    "            except Exception as e:\n",
+    "                print(f\"Error plotting {label}: {e}\")\n",
+    "                continue\n",
+    "    \n",
+    "    # Customize plot\n",
+    "    plt.grid(True, linestyle='--', alpha=0.3)\n",
+    "    plt.title('Distribution of Combined Scores Across Categories', fontsize=14, pad=20)\n",
+    "    plt.xlabel('Combined Score', fontsize=12)\n",
+    "    plt.ylabel('Density', fontsize=12)\n",
+    "    \n",
+    "    # Adjust legend\n",
+    "    plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0.)\n",
+    "    \n",
+    "    # Adjust layout to prevent label cutoff\n",
+    "    plt.tight_layout()\n",
+    "    \n",
+    "    # Print statistics\n",
+    "    print(\"\\nCategory Statistics:\")\n",
+    "    print(\"-\" * 60)\n",
+    "    for category, stats in sorted(all_stats.items()):\n",
+    "        print(f\"\\n{category}:\")\n",
+    "        print(f\"Mean: {stats['mean']:.4f}\")\n",
+    "        print(f\"Median: {stats['median']:.4f}\")\n",
+    "        print(f\"Standard Deviation: {stats['std']:.4f}\")\n",
+    "        print(f\"Number of samples: {stats['count']}\")\n",
+    "    \n",
+    "    return plt\n",
+    "\n",
+    "# Usage\n",
+    "directory_path = '/shared/shashmi/inversion/new_metric/sd3.5_scrapped/metrics_output'\n",
+    "plt = create_multi_comparison_plot(directory_path)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import matplotlib.pyplot as plt\n",
+    "import os\n",
+    "from PIL import Image\n",
+    "import matplotlib.gridspec as gridspec\n",
+    "\n",
+    "def load_and_sort_category(file_path):\n",
+    "    \"\"\"Load JSON file and sort results by combined score.\"\"\"\n",
+    "    with open(file_path, 'r') as f:\n",
+    "        data = json.load(f)\n",
+    "    \n",
+    "    # Sort results by combined score\n",
+    "    sorted_results = sorted(data['results'], \n",
+    "                          key=lambda x: x['metrics']['combined_score'],\n",
+    "                          reverse=True)\n",
+    "    return sorted_results\n",
+    "\n",
+    "def plot_image_comparison(original_path, inversion_path, score, fig, gs_pos, title):\n",
+    "    \"\"\"Plot original and inverted images side by side.\"\"\"\n",
+    "    try:\n",
+    "        # Create a sub-gridspec for this comparison\n",
+    "        gs_sub = gridspec.GridSpecFromSubplotSpec(1, 2, subplot_spec=gs_pos, wspace=0.1)\n",
+    "        \n",
+    "        # Load images\n",
+    "        img_orig = Image.open(original_path)\n",
+    "        img_inv = Image.open(inversion_path)\n",
+    "        \n",
+    "        # Convert RGBA to RGB if necessary\n",
+    "        if img_orig.mode == 'RGBA':\n",
+    "            img_orig = img_orig.convert('RGB')\n",
+    "        if img_inv.mode == 'RGBA':\n",
+    "            img_inv = img_inv.convert('RGB')\n",
+    "        \n",
+    "        # Create subplots\n",
+    "        ax1 = fig.add_subplot(gs_sub[0])\n",
+    "        ax2 = fig.add_subplot(gs_sub[1])\n",
+    "        \n",
+    "        # Plot images\n",
+    "        ax1.imshow(img_orig)\n",
+    "        ax1.set_title('Original')\n",
+    "        ax1.axis('off')\n",
+    "        \n",
+    "        ax2.imshow(img_inv)\n",
+    "        ax2.set_title('Inverted')\n",
+    "        ax2.axis('off')\n",
+    "        \n",
+    "        # Add title above the pair\n",
+    "        plt.suptitle(f'{title}\\nCombined Score: {score:.4f}', y=1.1)\n",
+    "        \n",
+    "    except Exception as e:\n",
+    "        print(f\"Error loading images: {e}\")\n",
+    "        print(f\"Original path: {original_path}\")\n",
+    "        print(f\"Inversion path: {inversion_path}\")\n",
+    "\n",
+    "def visualize_category_extremes(directory_path):\n",
+    "    \"\"\"Visualize top 2 and bottom 2 images from each category.\"\"\"\n",
+    "    # Get all JSON files\n",
+    "    json_files = [f for f in os.listdir(directory_path) \n",
+    "                 if f.endswith('.json') \n",
+    "                 and 'category_averages' not in f \n",
+    "                 and 'visual_evidence' not in f]\n",
+    "    \n",
+    "    for file_name in json_files:\n",
+    "        try:\n",
+    "            file_path = os.path.join(directory_path, file_name)\n",
+    "            category = file_name.split('_metrics_')[0].replace('_', ' ').title()\n",
+    "            print(f\"Processing {category}...\")\n",
+    "            \n",
+    "            # Load and sort results\n",
+    "            sorted_results = load_and_sort_category(file_path)\n",
+    "            \n",
+    "            if not sorted_results:\n",
+    "                print(f\"No results found for {category}\")\n",
+    "                continue\n",
+    "            \n",
+    "            # Get top 2 and bottom 2\n",
+    "            top_2 = sorted_results[:2]\n",
+    "            bottom_2 = sorted_results[-2:]\n",
+    "            \n",
+    "            # Create figure and gridspec\n",
+    "            fig = plt.figure(figsize=(20, 15))\n",
+    "            gs = gridspec.GridSpec(2, 2, figure=fig, hspace=0.4, wspace=0.3)\n",
+    "            \n",
+    "            # Plot top 2\n",
+    "            for idx, result in enumerate(top_2):\n",
+    "                plot_image_comparison(\n",
+    "                    result['original_path'],\n",
+    "                    result['inversion_path'],\n",
+    "                    result['metrics']['combined_score'],\n",
+    "                    fig,\n",
+    "                    gs[0, idx],\n",
+    "                    f'Top {idx + 1}'\n",
+    "                )\n",
+    "            \n",
+    "            # Plot bottom 2\n",
+    "            for idx, result in enumerate(bottom_2):\n",
+    "                plot_image_comparison(\n",
+    "                    result['original_path'],\n",
+    "                    result['inversion_path'],\n",
+    "                    result['metrics']['combined_score'],\n",
+    "                    fig,\n",
+    "                    gs[1, idx],\n",
+    "                    f'Bottom {idx + 1}'\n",
+    "                )\n",
+    "            \n",
+    "            # Add category title\n",
+    "            fig.suptitle(f'{category} - Top and Bottom Images', fontsize=16, y=1.02)\n",
+    "            plt.show()\n",
+    "            \n",
+    "        except Exception as e:\n",
+    "            print(f\"Error processing category {category}: {e}\")\n",
+    "            continue\n",
+    "\n",
+    "# Usage\n",
+    "directory_path = '/shared/shashmi/inversion/new_metric/sd3.5_scrapped/metrics_output'\n",
+    "visualize_category_extremes(directory_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "inverter",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}