Update app.py

app.py

@@ -1,83 +1,46 @@
-import numpy as np
 import pandas as pd
 import tensorflow as tf
 import gradio as gr
 from sklearn.preprocessing import StandardScaler
 import json
 
-# Comprehensive food database with detailed nutrition information
+# Indian food database with detailed nutrition information
 FOOD_DATABASE = {
     'foods': {
         'high_protein': {
-            'Salmon': {'protein': 22, 'carbs': 0, 'fats': 13, 'calories': 208, 'benefits': ['omega-3', 'vitamin D', 'B12']},
+            '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']},
-            'Lentils': {'protein': 18, 'carbs': 40, 'fats': 1, 'calories': 230, 'benefits': ['fiber', 'iron']}
+            'Tofu': {'protein': 8, 'carbs': 1, 'fats': 5, 'calories': 70, 'benefits': ['protein', 'iron']}
         },
         'low_glycemic': {
-            'Quinoa': {'protein': 4, 'carbs': 21, 'fats': 2, 'calories': 120, 'benefits': ['complete protein', 'fiber']},
+            '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']},
-            'Berries': {'protein': 1, 'carbs': 14, 'fats': 0, 'calories': 57, 'benefits': ['antioxidants', 'vitamin C']},
-            'Turmeric': {'protein': 0, 'carbs': 1, 'fats': 0, 'calories': 9, 'benefits': ['curcumin', 'anti-inflammatory']}
+            '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 Berries', 'Quinoa Porridge', 'Vegetable Omelet'],
-            'lunch': ['Grilled Chicken Salad', 'Lentil Soup', 'Quinoa Bowl'],
-            'dinner': ['Baked Salmon', 'Turkey Stir-fry', 'Vegetarian Curry'],
-            'snacks': ['Almonds', 'Hummus with Vegetables', 'Apple with Peanut Butter']
+            '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 Berries', 'Whole Grain Toast with Avocado', 'Smoothie Bowl'],
+            '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', 'Bean and Quinoa Bowl'],
+            'dinner': ['Grilled Salmon', 'Lean Chicken Breast', 'Vegetable Biryani'],
             'snacks': ['Walnuts', 'Fresh Fruit', 'Greek Yogurt']
         }
     }
 }
 
-# Genetic risk profiles and dietary implications
-GENETIC_PROFILES = {
-    'FTO': {
-        'Risk Variant': {
-            'implications': 'Increased risk of obesity and diabetes',
-            'dietary_recommendations': [
-                'Focus on high-protein meals to increase satiety',
-                'Choose low glycemic index foods',
-                'Increase fiber intake',
-                'Regular meal timing'
-            ]
-        }
-    },
-    'ADRB3': {
-        'Risk Variant': {
-            'implications': 'Reduced metabolic rate',
-            'dietary_recommendations': [
-                'Include thermogenic foods',
-                'Higher protein intake',
-                'Controlled portion sizes',
-                'Green tea consumption'
-            ]
-        }
-    },
-    'PPARG': {
-        'Risk Variant': {
-            'implications': 'Reduced insulin sensitivity',
-            'dietary_recommendations': [
-                'Choose low glycemic index foods',
-                'Include omega-3 rich foods',
-                'Increase fiber intake',
-                'Include anti-inflammatory foods'
-            ]
-        }
-    }
-}
-
-def generate_personalized_recommendations(genetic_data, health_metrics):
+# 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 = {
@@ -108,6 +71,14 @@ def generate_personalized_recommendations(genetic_data, health_metrics):
         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):
@@ -134,22 +105,10 @@ def format_recommendations(recommendations):
             for option in options:
                 output += f"• {option}\n"
     
-    # Foods to Focus On
-    if recommendations['foods_to_focus']:
-        output += "\n✅ FOODS TO FOCUS ON:\n"
-        for food in recommendations['foods_to_focus']:
-            output += f"• {food}\n"
-    
-    # Foods to Avoid
-    if recommendations['foods_to_avoid']:
-        output += "\n❌ FOODS TO AVOID:\n"
-        for food in recommendations['foods_to_avoid']:
-            output += f"• {food}\n"
-    
     return output
 
 def predict_diet(fto_variant, adrb3_variant, pparg_variant, apob_variant,
-                blood_glucose, blood_pressure, bmi, age, activity_level):
+                blood_glucose, blood_pressure, bmi, age, activity_level, diet_type):
     
     # Process genetic data
     genetic_data = {
@@ -175,7 +134,7 @@ def predict_diet(fto_variant, adrb3_variant, pparg_variant, apob_variant,
     }
     
     # Generate recommendations
-    recommendations = generate_personalized_recommendations(genetic_data, health_metrics)
+    recommendations = generate_personalized_recommendations(genetic_data, health_metrics, diet_type)
     
     # Format and return recommendations
     return format_recommendations(recommendations)
@@ -192,13 +151,14 @@ iface = gr.Interface(
         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(["Sedentary", "Active"], label="Activity Level"),
+        gr.Radio(["Vegetarian", "Non-Vegetarian", "Vegan"], label="Diet Preference")
     ],
     outputs="text",
-    title="Nutrigenomics-Based Diet Recommendation System",
+    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)
+iface.launch(share=True)