feat: Enhance medical report generation with new features and improved data handling

- Added body fat percentage input and optional muscle oxygenation CSV upload in the upload form.
- Implemented TSI chart generation based on muscle oxygenation data.
- Updated report generation to include metabolism and fuel source charts.
- Refactored context generation to eliminate reliance on SECA data, using patient info directly instead.
- Improved error handling and logging for graph generation processes.
- Enhanced HTML templates for better user experience and functionality.

notebooks/graphs.ipynb

@@ -72,7 +72,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "id": "99116a35",
    "metadata": {},
    "outputs": [],
@@ -237,7 +237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
    "id": "470e871e",
    "metadata": {},
    "outputs": [
@@ -290,7 +290,14 @@
     "    # --- Chart data and positions ---\n",
     "    categories = ['Very Slow', 'Slow', 'Average', 'Fast', 'Very Fast']\n",
     "    positions = [1500, 3000, 4500, 6000, 7500]\n",
-    "    kcal_value = 1386\n",
+    "    # Step 1: Filter resting phase (usually lowest VO2 or MET values)\n",
+    "    rest_phase = df[df['MET'] <= 1.1]  # assuming <1.1 MET means rest\n",
+    "\n",
+    "    # Step 2: Compute resting metabolic rate\n",
+    "    rmr = rest_phase['EE(kcal/day)'].mean()\n",
+    "\n",
+    "    print(f\"Estimated RMR from data: {rmr:.0f} kcal/day\")\n",
+    "    kcal_value = rmr\n",
     "    # Position the indicator and highlight based on the kcal value\n",
     "    # For this example, we'll place it in the 'Very Slow' section.\n",
     "    indicator_pos = kcal_value\n",
@@ -349,7 +356,13 @@
     "    fig, ax = plt.subplots(figsize=(10, 2.5))\n",
     "\n",
     "    # --- Chart data and positions ---\n",
-    "    fat_percentage = 33\n",
+    "    rest_phase = df[df['RER'] == 0.9]  # filter rest data\n",
+    "    fat_rest = rest_phase['FAT(%)'].mean()\n",
+    "    carb_rest = rest_phase['CARBS(%)'].mean()\n",
+    "\n",
+    "    print(f\"Resting phase fuel mix: Fats {fat_rest:.1f}%, Carbs {carb_rest:.1f}%\")\n",
+    "\n",
+    "    fat_percentage = fat_rest\n",
     "    carb_percentage = 100 - fat_percentage\n",
     "    optimal_point = 75\n",
     "\n",

notebooks/page_context_gen.ipynb

@@ -18,13 +18,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "da5ac3c1",
    "metadata": {},
    "outputs": [],
    "source": [
     "pnoe_df = pd.read_csv('data/pnoe_data.csv', delimiter=';')\n",
-    "patients_info = pd.read_excel('data/patients_data.xlsx')\n",
     "spirometry_df = pd.read_csv('data/spirometry_data.csv')"
    ]
   },
@@ -254,7 +253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "2fa8ff13",
    "metadata": {},
    "outputs": [
@@ -270,15 +269,10 @@
     }
    ],
    "source": [
-    "def body_composition_chart(first_name='Keirstyn', last_name='Moran'):\n",
+    "def body_composition_chart(fat_percentage=22.4, weight_kg=70):\n",
     "\n",
     "    \n",
     "    #=========================== Body Composition Donut Chart ========================#\n",
-    "    patient_data = patients_info[(patients_info['FirstName'].str.contains(first_name, case=False, na=False)) & \n",
-    "                                  (patients_info['LastName'].str.contains(last_name, case=False, na=False))]\n",
-    "# Get the fat mass percentage for Keirstyn\n",
-    "    fat_percentage = patient_data['Adult_FMP'].iloc[0]\n",
-    "    weight_kg = patient_data['Weight'].iloc[0]\n",
     "    lean_percentage = 100 - fat_percentage\n",
     "\n",
     "# Create donut chart\n",