File size: 7,707 Bytes
a566697 c2ea612 a566697 c2ea612 a566697 c2ea612 a566697 c2ea612 a566697 c2ea612 a566697 c2ea612 a566697 c2ea612 a566697 c2ea612 a566697 c2ea612 a566697 c2ea612 a566697 c2ea612 a566697 c2ea612 a566697 c2ea612 a566697 c2ea612 a566697 c2ea612 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 |
import pandas as pd
import tensorflow as tf
import gradio as gr
from sklearn.preprocessing import StandardScaler
import json
# Indian food database with detailed nutrition information
FOOD_DATABASE = {
'foods': {
'high_protein': {
'Paneer': {'protein': 18, 'carbs': 5, 'fats': 20, 'calories': 265, 'benefits': ['calcium', 'high protein']},
'Chicken Breast': {'protein': 31, 'carbs': 0, 'fats': 3.6, 'calories': 165, 'benefits': ['lean protein', 'B vitamins']},
'Tofu': {'protein': 8, 'carbs': 1, 'fats': 5, 'calories': 70, 'benefits': ['protein', 'iron']}
},
'low_glycemic': {
'Brown Rice': {'protein': 3, 'carbs': 45, 'fats': 1, 'calories': 216, 'benefits': ['fiber', 'minerals']},
'Sweet Potato': {'protein': 2, 'carbs': 27, 'fats': 0, 'calories': 103, 'benefits': ['vitamin A', 'fiber']},
'Greek Yogurt': {'protein': 10, 'carbs': 4, 'fats': 0, 'calories': 59, 'benefits': ['probiotics', 'calcium']}
},
'anti_inflammatory': {
'Spinach': {'protein': 3, 'carbs': 3, 'fats': 0, 'calories': 23, 'benefits': ['antioxidants', 'iron']},
'Turmeric': {'protein': 0, 'carbs': 1, 'fats': 0, 'calories': 9, 'benefits': ['curcumin', 'anti-inflammatory']},
'Mangoes': {'protein': 0.8, 'carbs': 15, 'fats': 0.6, 'calories': 60, 'benefits': ['vitamin C', 'fiber']}
}
},
'meal_plans': {
'diabetes_friendly': {
'breakfast': ['Greek Yogurt with Mangoes', 'Quinoa Porridge', 'Vegetable Upma'],
'lunch': ['Grilled Chicken Salad', 'Lentil Soup', 'Palak Paneer'],
'dinner': ['Grilled Salmon', 'Vegetable Stir-fry', 'Masoor Dal'],
'snacks': ['Almonds', 'Hummus with Vegetables', 'Cucumber Salad']
},
'heart_healthy': {
'breakfast': ['Oatmeal with Mangoes', 'Whole Grain Toast with Avocado', 'Smoothie Bowl'],
'lunch': ['Mediterranean Salad', 'Grilled Fish Tacos', 'Vegetable Soup'],
'dinner': ['Grilled Salmon', 'Lean Chicken Breast', 'Vegetable Biryani'],
'snacks': ['Walnuts', 'Fresh Fruit', 'Greek Yogurt']
}
}
}
# Modify the genetic profile analysis function as per Indian dietary trends
def generate_personalized_recommendations(genetic_data, health_metrics, diet_type):
"""Generate personalized diet recommendations based on genetic and health data"""
recommendations = {
'risk_profile': [],
'dietary_guidelines': [],
'meal_plan': {},
'foods_to_focus': [],
'foods_to_avoid': []
}
# Analyze genetic risks
if genetic_data['fto'] == 1: # Risk variant
recommendations['risk_profile'].append('FTO gene risk variant detected')
recommendations['dietary_guidelines'].extend(GENETIC_PROFILES['FTO']['Risk Variant']['dietary_recommendations'])
if genetic_data['pparg'] == 1: # Risk variant
recommendations['risk_profile'].append('PPARG gene risk variant detected')
recommendations['dietary_guidelines'].extend(GENETIC_PROFILES['PPARG']['Risk Variant']['dietary_recommendations'])
# Analyze health metrics
if health_metrics['blood_glucose'] > 100:
recommendations['dietary_guidelines'].append('Focus on low glycemic index foods')
recommendations['meal_plan'] = FOOD_DATABASE['meal_plans']['diabetes_friendly']
recommendations['foods_to_avoid'].extend(['White bread', 'Sugary drinks', 'Processed snacks'])
if health_metrics['systolic_bp'] > 130 or health_metrics['diastolic_bp'] > 80:
recommendations['dietary_guidelines'].append('Reduce sodium intake')
recommendations['meal_plan'] = FOOD_DATABASE['meal_plans']['heart_healthy']
recommendations['foods_to_focus'].extend(['Leafy greens', 'Berries', 'Whole grains'])
# Adjust meal plan based on dietary preference (veg, non-veg, vegan)
if diet_type == "Vegetarian":
recommendations['meal_plan'] = {meal: [food for food in options if food != 'Chicken Breast' and food != 'Grilled Salmon']
for meal, options in recommendations['meal_plan'].items()}
elif diet_type == "Vegan":
recommendations['meal_plan'] = {meal: [food for food in options if 'Paneer' not in food and 'Chicken Breast' not in food and 'Grilled Salmon' not in food]
for meal, options in recommendations['meal_plan'].items()}
return recommendations
def format_recommendations(recommendations):
"""Format recommendations into a readable output"""
output = "🧬 PERSONALIZED NUTRIGENOMIC DIET RECOMMENDATIONS 🧬\n\n"
# Risk Profile
output += "📊 GENETIC RISK PROFILE:\n"
for risk in recommendations['risk_profile']:
output += f"• {risk}\n"
output += "\n"
# Dietary Guidelines
output += "🥗 DIETARY GUIDELINES:\n"
for guideline in set(recommendations['dietary_guidelines']): # Remove duplicates
output += f"• {guideline}\n"
output += "\n"
# Meal Plan
if recommendations['meal_plan']:
output += "🍽️ RECOMMENDED MEAL PLAN:\n"
for meal, options in recommendations['meal_plan'].items():
output += f"\n{meal.upper()}:\n"
for option in options:
output += f"• {option}\n"
return output
def predict_diet(fto_variant, adrb3_variant, pparg_variant, apob_variant,
blood_glucose, blood_pressure, bmi, age, activity_level, diet_type):
# Process genetic data
genetic_data = {
'fto': float(fto_variant == "Risk Variant"),
'adrb3': float(adrb3_variant == "Risk Variant"),
'pparg': float(pparg_variant == "Risk Variant"),
'apob': float(apob_variant == "Risk Variant")
}
# Process health metrics
try:
systolic, diastolic = map(float, blood_pressure.split('/'))
except:
return "Please enter blood pressure in the format systolic/diastolic (e.g., 120/80)"
health_metrics = {
'blood_glucose': float(blood_glucose),
'systolic_bp': systolic,
'diastolic_bp': diastolic,
'bmi': float(bmi),
'age': float(age),
'activity': float(activity_level == "Active")
}
# Generate recommendations
recommendations = generate_personalized_recommendations(genetic_data, health_metrics, diet_type)
# Format and return recommendations
return format_recommendations(recommendations)
# Create Gradio interface
iface = gr.Interface(
fn=predict_diet,
inputs=[
gr.Radio(["Normal", "Risk Variant"], label="FTO Gene Variant (Obesity Risk)"),
gr.Radio(["Normal", "Risk Variant"], label="ADRB3 Gene Variant (Metabolism)"),
gr.Radio(["Normal", "Risk Variant"], label="PPARG Gene Variant (Insulin Sensitivity)"),
gr.Radio(["Normal", "Risk Variant"], label="APOB Gene Variant (Lipid Metabolism)"),
gr.Number(label="Fasting Blood Glucose (mg/dL)", value=90),
gr.Textbox(label="Blood Pressure (systolic/diastolic)", placeholder="120/80", value="120/80"),
gr.Number(label="BMI", value=25),
gr.Number(label="Age", value=30),
gr.Radio(["Sedentary", "Active"], label="Activity Level"),
gr.Radio(["Vegetarian", "Non-Vegetarian", "Vegan"], label="Diet Preference")
],
outputs="text",
title="Indian Diet-Based Nutrigenomics Recommendation System",
description="Get personalized dietary recommendations based on your genetic profile and health metrics.",
theme="huggingface"
)
# Launch the interface
iface.launch(share=True)
|