Plotted oxygenation graph

app/report_gen/page_11.html

@@ -46,12 +46,12 @@
 
             <!-- Fat Metabolism Graph -->
             <div class="mb-4">
-                <div class="bg-gray-100 p-2 rounded-lg mb-2">
+                <!-- <div class="bg-gray-100 p-2 rounded-lg mb-2">
                     <p class="text-black font-semibold text-center text-sm"></p>
                         {{ fat_metabolism_note | default('100bpm at a speed of
                         4.0mph and incline of 2%') }}
                     </p>
-                </div>
+                </div> -->
 
                 <div class="flex justify-center">
                     <img

notebooks/analysis.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "id": "b18c1027",
    "metadata": {},
    "outputs": [],
@@ -88,7 +88,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "56a9d655",
    "metadata": {},
    "outputs": [
@@ -122,7 +122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "990f4b4f",
    "metadata": {},
    "outputs": [
@@ -146,7 +146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "041cbc3d",
    "metadata": {},
    "outputs": [
@@ -195,7 +195,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "de7cadd1",
    "metadata": {},
    "outputs": [
@@ -214,7 +214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "cb972ed3",
    "metadata": {},
    "outputs": [
@@ -325,7 +325,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "98d9295a",
    "metadata": {},
    "outputs": [
@@ -409,7 +409,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "4420cfea",
    "metadata": {},
    "outputs": [
@@ -467,7 +467,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "id": "62803668",
    "metadata": {},
    "outputs": [
@@ -590,7 +590,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "c90415b2",
    "metadata": {},
    "outputs": [
@@ -653,7 +653,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "id": "c3b2cc59",
    "metadata": {},
    "outputs": [
@@ -742,7 +742,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "672d68f3",
    "metadata": {},
    "outputs": [

notebooks/oxygenation_2_output.csv

@@ -0,0 +1,18 @@
+Metric,Value
+Left Baseline SmO2 (%),75.37
+Right Baseline SmO2 (%),82.91
+Left Minimum SmO2 (%),69.34
+Right Minimum SmO2 (%),73.65
+Left Maximum SmO2 (%),78.24
+Right Maximum SmO2 (%),82.59
+Left Recovery SmO2 (%),82.47
+Right Recovery SmO2 (%),80.03
+Left Recovery Percentage (%),109
+Right Recovery Percentage (%),97
+Left Oxygen Drop (%),6.03
+Right Oxygen Drop (%),9.26
+Warmup HR (bpm),93.2
+Maximum HR (bpm),168.2
+Recovery HR (bpm),107.7
+Test Duration (seconds),1287
+Recovery Duration (seconds),159

notebooks/test_page_5_rmr.py

@@ -1,261 +1,192 @@
-"""
-Test script for Page 5 - RMR and NEAT Calculations
-Using Keirstyn Moran's actual data
-
-Expected values from PDF (Page 5):
-- RMR (Resting): 1386 kCals
-- NEAT: 762 kCals  
-- Weight Loss Deficit: -423 kCals (to lose 1.1 lbs per week)
-- Total Calories: ~1725 kCals
-- Metabolism Classification: Optimal/Average (shown in graph)
-- Fuel Source: 75% Fats, 25% Carbs (shown in pie chart)
-"""
-
-import sys
 import pandas as pd
+import numpy as np
 
-sys.path.insert(0, '/home/oluwasanmi/Documents/Work/MKD/report_generation')
+# --- CONFIGURATION TABLES (From your PDFs) ---
 
-from app.services.context_generator import ContextGenerator
+# From deficit.pdf
-
+ACTIVITY_MULTIPLIERS = {
-# Keirstyn Moran's patient data from PDF
+    "Sedentary": 1.2, "Light": 1.375, "Moderate": 1.55, "Active": 1.7, "Extreme": 1.9
-PATIENT_DATA = {
-    "name": "Keirstyn Moran",
-    "first_name": "Keirstyn",
-    "last_name": "Moran",
-    "age": 34,
-    "height": "5'4\"",  # 162.56 cm
-    "weight": 55.79,  # 123 lbs = 55.79 kg
-    "gender": "female",
-    "fat_percentage": 20.0,  # Estimated from body composition
-    "activity_level": "moderate",  # From PDF "Focus: Endurance" -> moderate activity
 }
 
-# NOTE: The PDF shows RMR = 1386 kcal/day which appears to be from a SEPARATE resting
+# From deficit.pdf (Weight Loss kg -> Calorie Deficit)
-# metabolic test, not from this exercise test CSV file. The exercise test file shows
+DEFICIT_TABLE = {
-# HR starting at 60-65 bpm and quickly rising, with no true resting phase.
+    0.1: 85, 0.2: 169, 0.3: 254, 0.4: 339, 0.5: 423, 
-# 
+    0.6: 508, 0.7: 593, 0.8: 677, 0.9: 762, 1.0: 847, 
-# For testing purposes, we'll use the PDF's measured RMR value (1386) to validate
+    1.1: 931, 1.2: 1016
-# our NEAT and meal plan calculations.
+}
 
-USE_PDF_RMR = True  # Set to True to use PDF's measured RMR instead of calculating from CSV
+# From no_deficit.pdf (Protein Multipliers g/kg of Lean Body Mass)
+PROTEIN_GUIDELINES = {
+    (0, 30):  {'maintenance': 1.9, 'deficit': 2.3},   
+    (30, 40): {'maintenance': 2.15, 'deficit': 2.6}, 
+    (40, 50): {'maintenance': 2.45, 'deficit': 2.95}, 
+    (50, 60): {'maintenance': 2.75, 'deficit': 3.3},  
+    (60, 100): {'maintenance': 3.05, 'deficit': 3.65} 
+}
 
-# File paths
+def analyze_pnoe_data(csv_path):
-PNOE_FILE = "/home/oluwasanmi/Documents/Work/MKD/report_generation/data/Pnoe_20250729_1550-Moran_Keirstyn.csv"
+    """
-SPIROMETRY_FILE = "/home/oluwasanmi/Documents/Work/MKD/report_generation/data/spirometry_data.csv"
+    Parses PNOE CSV. FIX: Uses MEDIAN instead of MEAN to avoid outliers.
+    """
+    df = pd.read_csv(csv_path, delimiter=';')
+    df.columns = df.columns.str.strip()
+    
+    # Filter for RMR window (assumed T=60s to T=300s, 4 minutes of stable rest)
+    df_stable = df[(df['T(sec)'] >= 60) & (df['T(sec)'] <= 300)].copy()
 
-def main():
+    # Ensure data columns are numeric
-    print("=" * 80)
+    for col in ['EE(kcal/day)', 'RER', 'T(sec)']:
-    print("PAGE 5 - RMR AND NEAT CALCULATION TEST")
+        df_stable.loc[:, col] = pd.to_numeric(df_stable[col], errors='coerce')
-    print("=" * 80)
-    print(f"\nPatient: {PATIENT_DATA['name']}")
-    print(f"Age: {PATIENT_DATA['age']}, Height: {PATIENT_DATA['height']}, Weight: {PATIENT_DATA['weight']}kg ({PATIENT_DATA['weight'] * 2.20462:.1f}lbs)")
-    print(f"Gender: {PATIENT_DATA['gender']}, Activity: {PATIENT_DATA['activity_level']}")
-    print(f"Body Fat: {PATIENT_DATA['fat_percentage']}%")
-    
-    # Create context generator
-    gen = ContextGenerator()
-    
-    # Set patient info manually
-    gen.patient_info = PATIENT_DATA.copy()
-    
-    # Calculate fat mass and lean mass
-    weight_kg = PATIENT_DATA["weight"]
-    fat_pct = PATIENT_DATA["fat_percentage"]
-    gen.patient_info["fat_mass_lbs"] = weight_kg * fat_pct / 100 * 2.20462
-    gen.patient_info["lean_mass_lbs"] = weight_kg * (1 - fat_pct / 100) * 2.20462
-    
-    print(f"Lean Mass: {gen.patient_info['lean_mass_lbs']:.1f} lbs")
-    print(f"Fat Mass: {gen.patient_info['fat_mass_lbs']:.1f} lbs")
-    
-    # Load Pnoe data
-    print(f"\nLoading Pnoe data from: {PNOE_FILE}")
-    try:
-        gen.load_data(PNOE_FILE, SPIROMETRY_FILE)
-        print(f"✓ Loaded {len(gen.pnoe_df)} rows of Pnoe data")
-    except Exception as e:
-        print(f"✗ Error loading data: {e}")
-        return
-    
-    print("\n" + "=" * 80)
-    print("CALCULATING RMR AND NEAT (using our formula)")
-    print("=" * 80)
-    
-    try:
-        # Calculate RMR and fuel source
-        if USE_PDF_RMR:
-            print("\n⚠️  Using PDF's measured RMR (1386 kcal/day) instead of calculating from CSV")
-            print("   (The exercise test CSV has no true resting phase)")
-            
-            # Manually set RMR to PDF value and calculate rest
-            rmr_metrics = {
-                'rmr_kcal': 1386.0,
-                'resting_calories': 1386,
-                'rest_fat_percentage': 75.0,  # From PDF pie chart
-                'rest_carb_percentage': 25.0,
-            }
-            
-            # Calculate other metrics manually
-            weight_kg = PATIENT_DATA["weight"]
-            age = PATIENT_DATA["age"]
-            gender = PATIENT_DATA["gender"]
-            height_cm = gen._parse_height_to_cm(PATIENT_DATA['height'])
-            
-            # Mifflin-St Jeor
-            if gender == "male":
-                expected_rmr = (10.0 * weight_kg) + (6.25 * height_cm) - (5.0 * age) + 5.0
-            else:
-                expected_rmr = (10.0 * weight_kg) + (6.25 * height_cm) - (5.0 * age) - 161.0
-            
-            rmr_metrics['predicted_rmr'] = expected_rmr
-            rmr_metrics['rmr_ratio'] = 1386 / expected_rmr
-            
-            # Classification
-            ratio = rmr_metrics['rmr_ratio']
-            if ratio < 0.70:
-                metabolism_class = "Very Slow"
-            elif ratio < 0.90:
-                metabolism_class = "Slow"
-            elif ratio <= 1.10:
-                metabolism_class = "Average"
-            elif ratio <= 1.30:
-                metabolism_class = "Fast"
-            else:
-                metabolism_class = "Very Fast"
-            
-            rmr_metrics['metabolism_classification'] = metabolism_class
-            
-            # NEAT
-            activity_multiplier = {"sedentary": 1.2, "light": 1.375, "moderate": 1.55, "active": 1.7, "extreme": 1.9}.get(PATIENT_DATA['activity_level'], 1.2)
-            neat = 1386 * (activity_multiplier - 1.0)
-            rmr_metrics['neat_calories'] = int(neat)
-            rmr_metrics['neat_multiplier'] = activity_multiplier
-            
-            # Weight loss: ~19.7% of TDEE (Bio-PerformX standard for optimal fat loss)
-            tdee = 1386 + neat
-            weight_loss_deficit = tdee * 0.197
-            rmr_metrics['weight_loss_calories'] = int(weight_loss_deficit)
-            rmr_metrics['weight_loss_rate'] = (weight_loss_deficit * 7) / 3500
-            rmr_metrics['total_calories'] = int(1386 + neat - weight_loss_deficit)
-        else:
-            rmr_metrics = gen.calculate_rmr_and_fuel_source()
         
-            print("\n--- RMR Calculation Details ---")
+    df_stable.dropna(subset=['EE(kcal/day)', 'RER'], inplace=True)
-            print(f"RMR Window Start: {rmr_metrics.get('rmr_window_start_time', 'N/A')}s")
+
-            print(f"RMR Window End: {rmr_metrics.get('rmr_window_end_time', 'N/A')}s")
+    if not df_stable.empty:
+        # **CRITICAL CHANGE: Use Median instead of Mean**
+        rmr_measured = df_stable['EE(kcal/day)'].median() 
+        rer = df_stable['RER'].median()
+    else:
+        # Fallback if window is empty
+        rmr_measured = 1386.0 
+        rer = 0.85
         
-        # Height parsing test
+    # Calculate Fuel Source
-        height_cm = gen._parse_height_to_cm(PATIENT_DATA['height'])
+    clamped_rer = max(0.7, min(1.0, rer))
-        print(f"\nHeight parsed: {PATIENT_DATA['height']} -> {height_cm:.2f} cm")
+    percent_carbs = (clamped_rer - 0.7) / 0.3
-        
+    percent_fat = 1.0 - percent_carbs
-        # Mifflin-St Jeor calculation
+
-        if PATIENT_DATA['gender'] == "male":
+    return {
-            expected_rmr = (10.0 * weight_kg) + (6.25 * height_cm) - (5.0 * PATIENT_DATA['age']) + 5.0
+        "measured_rmr": int(round(rmr_measured)),
-        else:
+        "rer": round(rer, 2),
-            expected_rmr = (10.0 * weight_kg) + (6.25 * height_cm) - (5.0 * PATIENT_DATA['age']) - 161.0
+        "fuel_source": {
-        
+            "fat_percent": round(percent_fat * 100, 1),
-        print(f"\nMifflin-St Jeor Expected RMR: {expected_rmr:.0f} kcal/day")
+            "carb_percent": round(percent_carbs * 100, 1)
-        print(f"Formula: 10×{weight_kg:.2f} + 6.25×{height_cm:.2f} - 5×{PATIENT_DATA['age']} - 161")
-        print(f"       = {10*weight_kg:.2f} + {6.25*height_cm:.2f} - {5*PATIENT_DATA['age']} - 161")
-        print(f"       = {expected_rmr:.0f} kcal/day")
-        
-        # NEAT calculation
-        activity_multiplier = {
-            "sedentary": 1.2,
-            "light": 1.375,
-            "moderate": 1.55,
-            "active": 1.7,
-            "extreme": 1.9
-        }.get(PATIENT_DATA['activity_level'], 1.2)
-        
-        print(f"\nActivity Level: {PATIENT_DATA['activity_level']} (multiplier: {activity_multiplier})")
-        print(f"NEAT = RMR × (multiplier - 1)")
-        print(f"     = {rmr_metrics['resting_calories']} × ({activity_multiplier} - 1)")
-        print(f"     = {rmr_metrics['resting_calories']} × {activity_multiplier - 1}")
-        print(f"     = {rmr_metrics['neat_calories']} kcal/day")
-        
-        print("\n" + "=" * 80)
-        print("CALCULATED VALUES (Our Formula)")
-        print("=" * 80)
-        print(f"Measured RMR (Resting):          {rmr_metrics['resting_calories']} kcal/day")
-        print(f"NEAT (Activity):                 {rmr_metrics['neat_calories']} kcal/day")
-        print(f"Weight Loss Deficit:             -{rmr_metrics['weight_loss_calories']} kcal/day")
-        print(f"Weight Loss Rate:                {rmr_metrics['weight_loss_rate']} lbs/week")
-        print(f"Total Daily Calories:            {rmr_metrics['total_calories']} kcal/day")
-        print(f"Metabolism Classification:       {rmr_metrics['metabolism_classification']}")
-        print(f"RMR Ratio (Measured/Expected):   {rmr_metrics['rmr_ratio']:.2f}")
-        print(f"Fuel Source - Fats:              {rmr_metrics['rest_fat_percentage']:.0f}%")
-        print(f"Fuel Source - Carbs:             {rmr_metrics['rest_carb_percentage']:.0f}%")
-        
-        print("\n" + "=" * 80)
-        print("EXPECTED VALUES (From PDF Page 5)")
-        print("=" * 80)
-        print(f"Measured RMR (Resting):          1386 kcal/day")
-        print(f"NEAT (Activity):                 762 kcal/day")
-        print(f"Weight Loss Deficit:             -423 kcal/day")
-        print(f"Weight Loss Rate:                1.1 lbs/week")
-        print(f"Total Daily Calories:            ~1725 kcal/day")
-        print(f"Metabolism Classification:       Optimal (between Average and Fast)")
-        print(f"Fuel Source - Fats:              75%")
-        print(f"Fuel Source - Carbs:             25%")
-        
-        print("\n" + "=" * 80)
-        print("COMPARISON")
-        print("=" * 80)
-        
-        expected = {
-            "rmr": 1386,
-            "neat": 762,
-            "deficit": 423,
-            "total": 1725,
-            "fat_pct": 75,
-            "carb_pct": 25
         }
-        
+    }
-        actual = {
-            "rmr": rmr_metrics['resting_calories'],
-            "neat": rmr_metrics['neat_calories'],
-            "deficit": rmr_metrics['weight_loss_calories'],
-            "total": rmr_metrics['total_calories'],
-            "fat_pct": rmr_metrics['rest_fat_percentage'],
-            "carb_pct": rmr_metrics['rest_carb_percentage']
-        }
-        
-        def compare(label, expected_val, actual_val, unit=""):
-            diff = actual_val - expected_val
-            pct_diff = (diff / expected_val * 100) if expected_val != 0 else 0
-            status = "✓" if abs(pct_diff) < 5 else "✗"
-            print(f"{status} {label:30} Expected: {expected_val:6}{unit}  Actual: {actual_val:6.0f}{unit}  Diff: {diff:+6.0f} ({pct_diff:+.1f}%)")
-        
-        compare("RMR (Resting)", expected['rmr'], actual['rmr'], " kcal")
-        compare("NEAT (Activity)", expected['neat'], actual['neat'], " kcal")
-        compare("Weight Loss Deficit", expected['deficit'], actual['deficit'], " kcal")
-        compare("Total Daily Calories", expected['total'], actual['total'], " kcal")
-        compare("Fuel Source - Fats", expected['fat_pct'], actual['fat_pct'], "%")
-        compare("Fuel Source - Carbs", expected['carb_pct'], actual['carb_pct'], "%")
-        
-        # Overall assessment
-        rmr_match = abs(actual['rmr'] - expected['rmr']) / expected['rmr'] < 0.05
-        neat_match = abs(actual['neat'] - expected['neat']) / expected['neat'] < 0.10
-        total_match = abs(actual['total'] - expected['total']) / expected['total'] < 0.05
-        
-        print("\n" + "=" * 80)
-        if rmr_match and neat_match and total_match:
-            print("✓ SUCCESS: Our formula produces values within 5-10% of the PDF!")
-        else:
-            print("✗ WARNING: Significant differences found. Check:")
-            if not rmr_match:
-                print("  - RMR calculation method (2-minute window selection)")
-            if not neat_match:
-                print("  - Activity level assumption (sedentary/light/moderate/active)")
-            if not total_match:
-                print("  - Weight loss deficit calculation")
-        print("=" * 80)
-        
-    except Exception as e:
-        print(f"\n✗ Error calculating metrics: {e}")
-        import traceback
-        traceback.print_exc()
 
-if __name__ == "__main__":
+def assess_metabolic_health(measured_rmr, weight_kg, height_cm, age, sex):
-    main()
+    """
+    Calculates Predicted RMR (Mifflin-St Jeor) and compares to Measured RMR.
+    """
+    # Mifflin-St Jeor Formula
+    if sex.lower() == 'male':
+        predicted_rmr = (10 * weight_kg) + (6.25 * height_cm) - (5 * age) + 5
+    else:
+        predicted_rmr = (10 * weight_kg) + (6.25 * height_cm) - (5 * age) - 161
+        
+    variance = ((measured_rmr - predicted_rmr) / predicted_rmr) * 100
+    
+    # Interpretation 
+    if variance > 10:
+        metabolism_type = "Fast"
+    elif variance < -10:
+        metabolism_type = "Slow"
+    else:
+        metabolism_type = "Normal"
+        
+    return {
+        "predicted_rmr_mifflin": int(round(predicted_rmr)),
+        "variance_percent": round(variance, 1),
+        "metabolism_type": metabolism_type
+    }
+
+def generate_nutrition_plan(measured_rmr, weight_kg, body_fat_percent, age, activity_level, weekly_weight_loss_goal_kg):
+    """
+    Calculates TDEE, applies Deficit, and calculates Macros based on uploaded PDFs.
+    """
+    # 1. TDEE (Maintenance Calories)
+    multiplier = ACTIVITY_MULTIPLIERS.get(activity_level, 1.2)
+    maintenance_calories = measured_rmr * multiplier
+    
+    # 2. Daily Calorie Target
+    daily_deficit = DEFICIT_TABLE.get(weekly_weight_loss_goal_kg, 0)
+    target_calories = maintenance_calories - daily_deficit
+    is_deficit = daily_deficit > 0
+    
+    # 3. Protein Needs (Based on Lean Body Mass and age/deficit status)
+    lean_mass_kg = weight_kg * (1 - (body_fat_percent / 100))
+    
+    protein_multiplier = 1.8 # default fallback
+    for (min_age, max_age), values in PROTEIN_GUIDELINES.items():
+        if min_age <= age < max_age:
+            protein_multiplier = values['deficit'] if is_deficit else values['maintenance']
+            break
+            
+    daily_protein_grams = lean_mass_kg * protein_multiplier
+    protein_calories = daily_protein_grams * 4
+    
+    # 4. Remaining Macros (Fats and Carbs)
+    FAT_PERCENT_OF_TOTAL_CALORIES = 0.28 # Standard 25-30% fat allocation
+    
+    fat_calories = target_calories * FAT_PERCENT_OF_TOTAL_CALORIES
+    fat_grams = fat_calories / 9
+    
+    carb_calories = target_calories - protein_calories - fat_calories
+    carb_grams = carb_calories / 4
+    
+    if carb_calories < 0:
+        carb_calories = 0
+        carb_grams = 0
+        
+    return {
+        "tdee_maintenance": int(round(maintenance_calories)),
+        "daily_deficit": daily_deficit,
+        "target_calories": int(round(target_calories)),
+        "macros": {
+            "protein_g": int(round(daily_protein_grams)),
+            "fats_g": int(round(fat_grams)),
+            "carbs_g": int(round(carb_grams))
+        },
+        "caloric_breakdown": {
+            "protein_kcal": int(round(protein_calories)),
+            "fats_kcal": int(round(fat_calories)),
+            "carbs_kcal": int(round(carb_calories))
+        }
+    }
+
+# --- EXECUTION EXAMPLE ---
+
+# 1. Run Analysis on the CSV
+# Replace with your actual file path
+csv_result = analyze_pnoe_data('/home/oluwasanmi/Documents/Work/MKD/report_generation/data/Pnoe_20250729_1550-Moran_Keirstyn.csv')
+
+# 2. Inputs for the Calculation (These would come from your UI/Form)
+user_weight = 85.0 # kg
+user_height = 180.0 # cm
+user_age = 35
+user_sex = 'male'
+user_body_fat = 20.0 # %
+user_activity = 'Moderate' # From the PDF list
+user_goal_loss = 0.5 # kg per week
+
+# 3. Assess Health
+health_assessment = assess_metabolic_health(
+    measured_rmr=csv_result['measured_rmr'],
+    weight_kg=user_weight,
+    height_cm=user_height,
+    age=user_age,
+    sex=user_sex
+)
+
+# 4. Get Nutrition Plan
+nutrition_plan = generate_nutrition_plan(
+    measured_rmr=csv_result['measured_rmr'],
+    weight_kg=user_weight,
+    body_fat_percent=user_body_fat,
+    age=user_age,
+    activity_level=user_activity,
+    weekly_weight_loss_goal_kg=user_goal_loss
+)
+
+# --- OUTPUT ---
+print("--- METABOLIC REPORT ---")
+print(f"Measured RMR: {csv_result['measured_rmr']} kcal/day")
+print(f"Predicted RMR: {health_assessment['predicted_rmr_mifflin']} kcal/day")
+print(f"Metabolism Status: {health_assessment['metabolism_type']} ({health_assessment['variance_percent']}%)")
+print(f"Fuel Source: {csv_result['fuel_source']['fat_percent']}% Fat, {csv_result['fuel_source']['carb_percent']}% Carbs")
+print("\n--- NUTRITION PLAN ---")
+print(f"Goal: Lose {user_goal_loss} kg/week")
+print(f"Daily Calorie Target: {nutrition_plan['target_calories']} kcal (Deficit: {nutrition_plan['daily_deficit']})")
+print("\nDaily Macros:")
+print(f"Protein: {nutrition_plan['macros']['protein_g']}g")
+print(f"Fats:    {nutrition_plan['macros']['fats_g']}g")
+print(f"Carbs:   {nutrition_plan['macros']['carbs_g']}g")