diff --git a/analysis.ipynb b/analysis.ipynb
index 44146ef..c3984d0 100644
--- a/analysis.ipynb
+++ b/analysis.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "id": "b18c1027",
    "metadata": {},
    "outputs": [
@@ -10,7 +10,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'id': 'gen-1758708788-9UUhU8KfktBmyteT4BUC', 'provider': 'Google', 'model': 'google/gemini-2.5-flash-lite', 'object': 'chat.completion', 'created': 1758708788, 'choices': [{'logprobs': None, 'finish_reason': 'stop', 'native_finish_reason': 'STOP', 'index': 0, 'message': {'role': 'assistant', 'content': 'Parameters,Best,LLN,Pred.,%Pred.,ZScore,PRE#1,PRE#2,PRE#3\\nFVC,4.24,3.03,3.79,112.0,0.95,4.24,4.17,4.15\\nFEV1,3.26,2.53,3.16,103.3,0.28,3.26,3.21,3.14\\nFEV1/FVC%,76.89,72.47,83.78,91.8,-1.05,76.9,77.0,75.7\\nPEF,684,222,384,178.7,-,444,438,684\\nFEF2575,2.74,2.15,3.42,80.2,-0.84,2.74,2.68,2.48\\nFEF25,6.08,,,0.0,-,6.08,6.0,5.53\\nFEF50,3.06,,,0.0,-,3.06,3.1,2.77\\nFEF75,1.06,0.71,1.41,75.1,-0.72,1.06,1.12,0.94\\nPEFTime,79,,,49,-,79,40,39\\nEVol,78.0,,,77.0,-,78.0,77.0,197.0\\nFEV6,4.22,3.03,3.79,111.4,-,4.22,4.17,4.13', 'refusal': None, 'reasoning': None}}], 'usage': {'prompt_tokens': 1348, 'completion_tokens': 434, 'total_tokens': 1782, 'prompt_tokens_details': {'cached_tokens': 0}, 'completion_tokens_details': {'reasoning_tokens': 0, 'image_tokens': 0}}}\n",
+      "{'id': 'gen-1759135172-DIhs7TMuaaVY0h3T2ibV', 'provider': 'Google', 'model': 'google/gemini-2.5-flash-lite', 'object': 'chat.completion', 'created': 1759135172, 'choices': [{'logprobs': None, 'finish_reason': 'stop', 'native_finish_reason': 'STOP', 'index': 0, 'message': {'role': 'assistant', 'content': 'Parameters,Best,LLN,Pred.,%Pred.,ZScore,PRE#1,PRE#2,PRE#3\\nFVC,L,4.24,3.03,3.79,112.0,0.95,4.24,4.17,4.15\\nFEV1,L,3.26,2.53,3.16,103.3,0.28,3.26,3.21,3.14\\nFEV1/FVC%,76.89,72.47,83.78,91.8,-1.05,76.9,77.0,75.7\\nPEF,L/m,684,222,384,178.7,-,444,438,684\\nFEF2575,L/s,2.74,2.15,3.42,80.2,-0.84,2.74,2.68,2.48\\nFEF25,L/s,6.08,-,-,-,6.08,6.0,5.53\\nFEF50,L/s,3.06,-,-,-,3.06,3.1,2.77\\nFEF75,L/s,1.06,0.71,1.41,75.1,-0.72,1.06,1.12,0.94\\nPEFTime,ms,-,-,79,-,79,49,39\\nEvol,mL,-,-,78.0,-,78.0,77.0,197.0\\nFEV6,L,4.22,3.03,3.79,111.4,-,4.22,4.17,4.13', 'refusal': None, 'reasoning': None}}], 'usage': {'prompt_tokens': 1350, 'completion_tokens': 454, 'total_tokens': 1804, 'prompt_tokens_details': {'cached_tokens': 0}, 'completion_tokens_details': {'reasoning_tokens': 0, 'image_tokens': 0}}}\n",
       "Content saved to extracted_table.csv\n"
      ]
     }
@@ -44,7 +44,7 @@
     "        \"content\": [\n",
     "            {\n",
     "                \"type\": \"text\",\n",
-    "                \"text\": \"Please extract the table from the pdf and return the values in csv format, \"\n",
+    "                \"text\": \"Please extract the Spirometry table from the pdf and return the values in csv format, \"\n",
     "                \"note that it is the unit of parameter that is beside it and it should not be a column. \"\n",
     "                \"The '-' Should be treated as empty values.\"\n",
     "                \"do not add 'csv' at the start or end of the response\"\n",
diff --git a/context_generator.py b/context_generator.py
new file mode 100644
index 0000000..a98d562
--- /dev/null
+++ b/context_generator.py
@@ -0,0 +1,5 @@
+import pandas as pd
+
+pnoe_df = pd.read_csv('data/pnoe_data.csv')
+patient_df = pd.read_csv('data/patient_data.csv')
+spirometry_df = pd.read_csv('data/spirometry_data.csv')
\ No newline at end of file
diff --git a/extracted_table.csv b/extracted_table.csv
new file mode 100644
index 0000000..7482e84
--- /dev/null
+++ b/extracted_table.csv
@@ -0,0 +1,12 @@
+Parameters,Best,LLN,Pred.,%Pred.,ZScore,PRE#1,PRE#2,PRE#3
+FVC,L,4.24,3.03,3.79,112.0,0.95,4.24,4.17,4.15
+FEV1,L,3.26,2.53,3.16,103.3,0.28,3.26,3.21,3.14
+FEV1/FVC%,76.89,72.47,83.78,91.8,-1.05,76.9,77.0,75.7
+PEF,L/m,684,222,384,178.7,-,444,438,684
+FEF2575,L/s,2.74,2.15,3.42,80.2,-0.84,2.74,2.68,2.48
+FEF25,L/s,6.08,-,-,-,6.08,6.0,5.53
+FEF50,L/s,3.06,-,-,-,3.06,3.1,2.77
+FEF75,L/s,1.06,0.71,1.41,75.1,-0.72,1.06,1.12,0.94
+PEFTime,ms,-,-,79,-,79,49,39
+Evol,mL,-,-,78.0,-,78.0,77.0,197.0
+FEV6,L,4.22,3.03,3.79,111.4,-,4.22,4.17,4.13
\ No newline at end of file
diff --git a/graphs/body_composition_chart.png b/graphs/body_composition_chart.png
index 51afe2c..337db2f 100644
Binary files a/graphs/body_composition_chart.png and b/graphs/body_composition_chart.png differ
diff --git a/graphs/spirometry_chart.png b/graphs/spirometry_chart.png
new file mode 100644
index 0000000..af5cd9e
Binary files /dev/null and b/graphs/spirometry_chart.png differ
diff --git a/notebook.ipynb b/notebook.ipynb
index 9397027..cd35ace 100644
--- a/notebook.ipynb
+++ b/notebook.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 1,
    "id": "63f43af5",
    "metadata": {},
    "outputs": [],
@@ -15,7 +15,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 2,
    "id": "b0ee2af1",
    "metadata": {},
    "outputs": [
@@ -31,7 +31,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/tmp/ipykernel_161470/622539462.py:3: FutureWarning: errors='ignore' is deprecated and will raise in a future version. Use to_numeric without passing `errors` and catch exceptions explicitly instead\n",
+      "/tmp/ipykernel_226264/622539462.py:3: FutureWarning: errors='ignore' is deprecated and will raise in a future version. Use to_numeric without passing `errors` and catch exceptions explicitly instead\n",
       "  df = df.apply(pd.to_numeric, errors='ignore')\n"
      ]
     }
@@ -61,7 +61,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 3,
    "id": "fbd292c3",
    "metadata": {},
    "outputs": [
@@ -79,7 +79,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 4,
    "id": "ef8bc7ac",
    "metadata": {},
    "outputs": [
@@ -138,7 +138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 5,
    "id": "06244aa2",
    "metadata": {},
    "outputs": [
@@ -253,7 +253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 6,
    "id": "8a1878a0",
    "metadata": {},
    "outputs": [
@@ -331,7 +331,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 7,
    "id": "7361fb05",
    "metadata": {},
    "outputs": [
@@ -390,7 +390,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 8,
    "id": "c89478ff",
    "metadata": {},
    "outputs": [
@@ -449,7 +449,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 9,
    "id": "1db16040",
    "metadata": {},
    "outputs": [
@@ -522,7 +522,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 10,
    "id": "52642f49",
    "metadata": {},
    "outputs": [
@@ -909,7 +909,7 @@
        "[63 rows x 147 columns]"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -921,7 +921,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 11,
    "id": "2056096d",
    "metadata": {},
    "outputs": [
@@ -1046,13 +1046,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 12,
    "id": "bf55717b",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x800 with 1 Axes>"
       ]
@@ -1111,7 +1111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 13,
    "id": "21c1c0a5",
    "metadata": {},
    "outputs": [
@@ -1178,6 +1178,348 @@
     "plt.savefig('graphs/body_fat_percent_chart.png', bbox_inches='tight', dpi=300)\n",
     "plt.show() # This is where the file is saved and displayed above."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "10687f82",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Parameters</th>\n",
+       "      <th>Best</th>\n",
+       "      <th>LLN</th>\n",
+       "      <th>Pred.</th>\n",
+       "      <th>%Pred.</th>\n",
+       "      <th>ZScore</th>\n",
+       "      <th>PRE#1</th>\n",
+       "      <th>PRE#2</th>\n",
+       "      <th>PRE#3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>FVC</td>\n",
+       "      <td>4.24</td>\n",
+       "      <td>3.03</td>\n",
+       "      <td>3.79</td>\n",
+       "      <td>112.0</td>\n",
+       "      <td>0.95</td>\n",
+       "      <td>4.24</td>\n",
+       "      <td>4.17</td>\n",
+       "      <td>4.15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>FEV1</td>\n",
+       "      <td>3.26</td>\n",
+       "      <td>2.53</td>\n",
+       "      <td>3.16</td>\n",
+       "      <td>103.3</td>\n",
+       "      <td>0.28</td>\n",
+       "      <td>3.26</td>\n",
+       "      <td>3.21</td>\n",
+       "      <td>3.14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>FEV1/FVC%</td>\n",
+       "      <td>76.89</td>\n",
+       "      <td>72.47</td>\n",
+       "      <td>83.78</td>\n",
+       "      <td>91.8</td>\n",
+       "      <td>-1.05</td>\n",
+       "      <td>76.90</td>\n",
+       "      <td>77.00</td>\n",
+       "      <td>75.70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>PEF</td>\n",
+       "      <td>684.00</td>\n",
+       "      <td>222.00</td>\n",
+       "      <td>384.00</td>\n",
+       "      <td>178.7</td>\n",
+       "      <td>-</td>\n",
+       "      <td>444.00</td>\n",
+       "      <td>438.00</td>\n",
+       "      <td>684.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>FEF2575</td>\n",
+       "      <td>2.74</td>\n",
+       "      <td>2.15</td>\n",
+       "      <td>3.42</td>\n",
+       "      <td>80.2</td>\n",
+       "      <td>-0.84</td>\n",
+       "      <td>2.74</td>\n",
+       "      <td>2.68</td>\n",
+       "      <td>2.48</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>FEF25</td>\n",
+       "      <td>6.08</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>6.08</td>\n",
+       "      <td>6.00</td>\n",
+       "      <td>5.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>FEF50</td>\n",
+       "      <td>3.06</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>3.06</td>\n",
+       "      <td>3.10</td>\n",
+       "      <td>2.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>FEF75</td>\n",
+       "      <td>1.06</td>\n",
+       "      <td>0.71</td>\n",
+       "      <td>1.41</td>\n",
+       "      <td>75.1</td>\n",
+       "      <td>-0.72</td>\n",
+       "      <td>1.06</td>\n",
+       "      <td>1.12</td>\n",
+       "      <td>0.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>PEFTime</td>\n",
+       "      <td>79.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>49.0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>79.00</td>\n",
+       "      <td>40.00</td>\n",
+       "      <td>39.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>EVol</td>\n",
+       "      <td>78.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>78.00</td>\n",
+       "      <td>77.00</td>\n",
+       "      <td>197.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>FEV6</td>\n",
+       "      <td>4.22</td>\n",
+       "      <td>3.03</td>\n",
+       "      <td>3.79</td>\n",
+       "      <td>111.4</td>\n",
+       "      <td>-</td>\n",
+       "      <td>4.22</td>\n",
+       "      <td>4.17</td>\n",
+       "      <td>4.13</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Parameters    Best     LLN   Pred.  %Pred. ZScore   PRE#1   PRE#2   PRE#3\n",
+       "0         FVC    4.24    3.03    3.79   112.0   0.95    4.24    4.17    4.15\n",
+       "1        FEV1    3.26    2.53    3.16   103.3   0.28    3.26    3.21    3.14\n",
+       "2   FEV1/FVC%   76.89   72.47   83.78    91.8  -1.05   76.90   77.00   75.70\n",
+       "3         PEF  684.00  222.00  384.00   178.7      -  444.00  438.00  684.00\n",
+       "4     FEF2575    2.74    2.15    3.42    80.2  -0.84    2.74    2.68    2.48\n",
+       "5       FEF25    6.08     NaN     NaN     0.0      -    6.08    6.00    5.53\n",
+       "6       FEF50    3.06     NaN     NaN     0.0      -    3.06    3.10    2.77\n",
+       "7       FEF75    1.06    0.71    1.41    75.1  -0.72    1.06    1.12    0.94\n",
+       "8     PEFTime   79.00     NaN     NaN    49.0      -   79.00   40.00   39.00\n",
+       "9        EVol   78.00     NaN     NaN    77.0      -   78.00   77.00  197.00\n",
+       "10       FEV6    4.22    3.03    3.79   111.4      -    4.22    4.17    4.13"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "spirometry_data = pd.read_csv('data/spirometry_data.csv')\n",
+    "spirometry_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "5d7588d8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1150x360 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Ensure data is loaded\n",
+    "try:\n",
+    "    spirometry_df = spirometry_data.copy()\n",
+    "except NameError:\n",
+    "    spirometry_df = pd.read_csv('data/spirometry_data.csv')\n",
+    "\n",
+    "# Coerce numeric columns\n",
+    "for col in ['Best', 'LLN', 'Pred.', '%Pred.', 'ZScore']:\n",
+    "    if col in spirometry_df.columns:\n",
+    "        spirometry_df[col] = pd.to_numeric(spirometry_df[col], errors='coerce')\n",
+    "\n",
+    "# Select rows of interest and prepare display values\n",
+    "rows_map = {\n",
+    "    'Lung Volume': 'FVC',\n",
+    "    'Lung Power': 'FEV1',\n",
+    "    'Power/Volume': 'FEV1/FVC%'\n",
+    "}\n",
+    "\n",
+    "records = []\n",
+    "for label, param in rows_map.items():\n",
+    "    row = spirometry_df.loc[spirometry_df['Parameters'].str.strip() == param]\n",
+    "    if row.empty:\n",
+    "        continue\n",
+    "    row = row.iloc[0]\n",
+    "    records.append({\n",
+    "        'label': label,\n",
+    "        'param': param,\n",
+    "        'best': row['Best'],\n",
+    "        'pct': row['%Pred.'],\n",
+    "        'z': row['ZScore']\n",
+    "    })\n",
+    "\n",
+    "# Figure setup\n",
+    "os.makedirs('graphs', exist_ok=True)\n",
+    "fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(11.5, 3.6), sharex=True,\n",
+    "                        gridspec_kw={'hspace': 0.65})\n",
+    "\n",
+    "x_min, x_max = -5, 3\n",
+    "# Segment colors: red -> orange -> yellow -> green\n",
+    "segments = [\n",
+    "    (-5, -3, '#f4a7a7'),   # red-ish\n",
+    "    (-3, -2, '#f7c49a'),   # orange-ish\n",
+    "    (-2, -1, '#f6e3a3'),   # yellow-ish\n",
+    "    (-1,  3, '#c9f0cc'),   # green-ish\n",
+    "]\n",
+    "\n",
+    "# Plot each row\n",
+    "for ax, rec in zip(axes, records):\n",
+    "    # Background segments\n",
+    "    for a, b, color in segments:\n",
+    "        ax.barh(0, width=b-a, left=a, height=0.6, color=color, edgecolor='none')\n",
+    "\n",
+    "    # LLN (-1) and Predicted (0) markers\n",
+    "    ax.axvline(-1, color='black', lw=1)\n",
+    "    ax.axvline(0, color='black', lw=1)\n",
+    "\n",
+    "    # Z-score pointer (downward triangle)\n",
+    "    if pd.notna(rec['z']):\n",
+    "        ax.plot(float(rec['z']), 0, marker=(3, 0, 180), markersize=12, color='dimgray')\n",
+    "\n",
+    "    # Labels and styling\n",
+    "    ax.set_title(rec['label'], loc='left', fontsize=11, fontweight='bold', pad=2)\n",
+    "    ax.set_xlim(x_min, x_max)\n",
+    "    ax.set_yticks([])\n",
+    "    ax.set_xlabel('')\n",
+    "\n",
+    "# Axis ticks only on bottom\n",
+    "axes[-1].set_xticks(np.arange(x_min, x_max + 1, 1))\n",
+    "axes[-1].set_xticklabels([str(i) for i in range(x_min, x_max + 1)])\n",
+    "for ax in axes[:-1]:\n",
+    "    ax.set_xticks([])\n",
+    "\n",
+    "# Top annotations\n",
+    "axes[0].text(-1, 0.45, 'LLN', ha='center', va='bottom', fontsize=9)\n",
+    "axes[0].text(0, 0.45, 'Predicted', ha='center', va='bottom', fontsize=9)\n",
+    "\n",
+    "# Right-side summary boxes\n",
+    "fig.subplots_adjust(right=0.78)\n",
+    "box_ax = fig.add_axes([0.805, 0.06, 0.18, 0.90])  # [left, bottom, width, height]\n",
+    "box_ax.axis('off')\n",
+    "\n",
+    "# Helper to draw a pill-shaped text box\n",
+    "from matplotlib.patches import FancyBboxPatch\n",
+    "\n",
+    "def pill(ax, xy, text):\n",
+    "    x, y = xy\n",
+    "    # Draw rounded rectangle background\n",
+    "    bbox = FancyBboxPatch((x-0.48, y-0.09), 0.96, 0.18,\n",
+    "                          boxstyle='round,pad=0.02,rounding_size=0.08',\n",
+    "                          ec='#dddddd', fc='#f3f3f3', linewidth=1.0)\n",
+    "    ax.add_patch(bbox)\n",
+    "    ax.text(x, y+0.025, text, ha='center', va='center', fontsize=11, fontweight='bold')\n",
+    "    ax.text(x, y-0.055, 'of predicted', ha='center', va='center', fontsize=9, color='#555555')\n",
+    "\n",
+    "box_ax.set_xlim(0, 1)\n",
+    "box_ax.set_ylim(0, 1)\n",
+    "\n",
+    "# Prepare display strings and positions (top to bottom)\n",
+    "right_items = []\n",
+    "for rec in records:\n",
+    "    name = 'FVC' if rec['param'] == 'FVC' else ('FEV1' if rec['param'] == 'FEV1' else 'FEV1/FVC')\n",
+    "    unit = 'L' if rec['param'] in ('FVC', 'FEV1') else '%'\n",
+    "    value_fmt = f\"{rec['best']:.2f}{unit}\"\n",
+    "    pct_fmt = f\"{rec['pct']:.1f}%\"\n",
+    "    right_items.append((name, value_fmt, pct_fmt))\n",
+    "\n",
+    "# Sort to match image order on the right (FVC, FEV1, FEV1/FVC)\n",
+    "order = ['FVC', 'FEV1', 'FEV1/FVC']\n",
+    "right_items_sorted = [next(item for item in right_items if item[0] == k) for k in order]\n",
+    "\n",
+    "ys = [0.78, 0.48, 0.18]\n",
+    "for (name, value_fmt, pct_fmt), y in zip(right_items_sorted, ys):\n",
+    "    main_line = f\"{name}\\n{value_fmt} → {pct_fmt}\"\n",
+    "    pill(box_ax, (0.5, y), main_line)\n",
+    "\n",
+    "plt.savefig('graphs/spirometry_chart.png', dpi=300)\n",
+    "plt.show()"
+   ]
   }
  ],
  "metadata": {