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89d3c48 | 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 | import os
import gradio as gr
from dotenv import load_dotenv
from google import genai
# Load environment variables
load_dotenv()
api_key = os.getenv("GEMINI_API_KEY")
# Initialize Gemini client
client = genai.Client(api_key=api_key)
# Function to calculate daily calorie requirements
def calculate_calorie_requirements(age, gender, weight, height, fitness_goal):
if gender == "Male":
bmr = 10 * weight + 6.25 * height - 5 * age + 5
else:
bmr = 10 * weight + 6.25 * height - 5 * age - 161
if fitness_goal == "Weight Loss":
return bmr * 1.2
elif fitness_goal == "Weight Gain":
return bmr * 1.5
else:
return bmr * 1.375
# Generate personalized plan
def generate_plan(name, age, gender, weight, height, fitness_goal, dietary_preference,
food_allergies, local_cuisine, month, include_ayurveda):
bmi = round(weight / (height / 100) ** 2, 2)
health_status = "Underweight" if bmi < 18.5 else "Normal weight" if bmi <= 24.9 else "Overweight"
daily_calories = calculate_calorie_requirements(age, gender, weight, height, fitness_goal)
metrics = {
"name": name,
"age": age,
"gender": gender,
"bmi": bmi,
"health_status": health_status,
"fitness_goal": fitness_goal,
"dietary_preference": dietary_preference,
"food_allergies": food_allergies,
"daily_calories": int(daily_calories),
"local_cuisine": local_cuisine,
"month": month,
}
if include_ayurveda:
prompt = f"""
You are a health expert specializing in both modern medicine and Ayurveda.
Generate a personalized weekly diet and exercise plan for {name}, a {age}-year-old {gender}
with a BMI of {bmi} ({health_status}).
Goal: {fitness_goal}. Daily Calorie Requirement: {int(daily_calories)} kcal.
Dietary Preference: {dietary_preference}. Allergies: {food_allergies}.
Local Cuisine: {local_cuisine}. Month: {month}.
Include Ayurvedic insights and dosha-based recommendations.
"""
else:
prompt = f"""
You are a health expert.
Generate a personalized weekly diet and exercise plan for {name}, a {age}-year-old {gender}
with a BMI of {bmi} ({health_status}).
Goal: {fitness_goal}. Daily Calorie Requirement: {int(daily_calories)} kcal.
Dietary Preference: {dietary_preference}. Allergies: {food_allergies}.
Local Cuisine: {local_cuisine}. Month: {month}.
"""
try:
response = client.models.generate_content(
model="gemini-2.5-flash",
contents=prompt
)
return f"**Your BMI:** {bmi} ({health_status})\n\n" + response.text
except Exception as e:
return f"Error calling Gemini API: {e}"
# Gradio UI
iface = gr.Interface(
fn=generate_plan,
inputs=[
gr.Textbox(label="Name"),
gr.Number(label="Age", value=25),
gr.Radio(["Male", "Female", "Other"], label="Gender"),
gr.Number(label="Weight (kg)", value=70),
gr.Number(label="Height (cm)", value=170),
gr.Radio(["Weight Loss", "Weight Gain", "Maintenance"], label="Fitness Goal"),
gr.Dropdown(["Vegetarian", "Vegan", "Keto", "Halal", "None"], label="Dietary Preference"),
gr.Textbox(label="Food Allergies (if any)"),
gr.Textbox(label="Preferred Local Cuisine"),
gr.Dropdown(
["January", "February", "March", "April", "May", "June",
"July", "August", "September", "October", "November", "December"],
label="Month"
),
gr.Checkbox(label="Include Ayurvedic insights", value=True),
],
outputs=gr.Markdown(label="Personalized Health Plan"),
title="AI-Based Personalized Weekly Diet and Exercise Planner (Gemini 2.5 Flash Pro)",
description="Uses Google Gemini AI to generate a custom health and fitness plan integrating Ayurvedic insights."
)
if __name__ == "__main__":
iface.launch()
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