app.py

import streamlit as st
import base64
from huggingface_hub import InferenceClient
import os

# Initialize Hugging Face Inference client using token from environment variables
client = InferenceClient(api_key=os.getenv("HF_API_TOKEN"))

# 1. Function to identify dish from image
def identify_dish(image_bytes):
    encoded_image = base64.b64encode(image_bytes).decode("utf-8")
    dish_name = ""

    for message in client.chat_completion(
        model="meta-llama/Llama-3.2-11B-Vision-Instruct",
        messages=[
            {
                "role": "You are a food identification expert who identifies dishes from images. Your task is to strictly return the names of the dishes present in the image. Only return the dish names if you have high Confidence Level and without additional explanation or description.",
                "content": [
                    {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{encoded_image}" }},
                    {"type": "text", "text": "Identify the dishes in the image and return only the names of the dishes."},
                ],
            }
        ],
        max_tokens=70,
        stream=True,
    ):
        if message.choices[0].delta.content:
            dish_name += message.choices[0].delta.content

    return dish_name.strip()

# 2. Function to get user inputs and calculate daily caloric needs
def calculate_metrics(age, gender, height_cm, weight_kg, weight_goal, activity_level, time_frame_months):
    # Calculate BMI
    bmi = weight_kg / ((height_cm / 100) ** 2)

    # Calculate BMR using Mifflin-St Jeor formula
    if gender == "male":
        bmr = 10 * weight_kg + 6.25 * height_cm - 5 * age + 5
    else:
        bmr = 10 * weight_kg + 6.25 * height_cm - 5 * age - 161

    # Calculate TDEE (Total Daily Energy Expenditure)
    activity_multipliers = {
        "sedentary": 1.2,
        "light": 1.375,
        "moderate": 1.55,
        "active": 1.725,
        "very active": 1.9
    }
    tdee = bmr * activity_multipliers[activity_level]

    # Calculate Ideal Body Weight (IBW) using Hamwi method
    if gender == "male":
        ibw = 50 + (0.91 * (height_cm - 152.4))
    else:
        ibw = 45.5 + (0.91 * (height_cm - 152.4))

    # Calculate Daily Caloric Needs
    if weight_goal == "loss":
        daily_caloric_needs = tdee - 500  # Deficit for weight loss
    elif weight_goal == "gain":
        daily_caloric_needs = tdee + 500  # Surplus for weight gain
    else:
        daily_caloric_needs = tdee

    # Calculate macronutrient calories
    protein_calories = daily_caloric_needs * 0.2
    fat_calories = daily_caloric_needs * 0.25
    carbohydrate_calories = daily_caloric_needs * 0.55

    return {
        "BMI": bmi,
        "BMR": bmr,
        "TDEE": tdee,
        "IBW": ibw,
        "Daily Caloric Needs": daily_caloric_needs,
        "Protein Calories": protein_calories,
        "Fat Calories": fat_calories,
        "Carbohydrate Calories": carbohydrate_calories
    }

# 3. Function to generate diet plan
def generate_diet_plan(dish_name, calorie_intake_per_day, goal):
    user_input = f"""
    You are a certified Dietitian with 20 years of experience. Based on the following input, create an Indian diet plan that fits within the calculated calorie intake and assesses if the given dish is suitable for the user's goal.

    Input:
    - Dish Name: {dish_name}
    - Caloric Intake per Day: {calorie_intake_per_day} calories
    - Goal: {goal}

    """
    # Request from the dietitian model
    response = client.chat_completion(
        model="meta-llama/Meta-Llama-3-8B-Instruct",
        messages=[{"role": "You are a certified Dietitian with 20 years of Experience", "content": user_input}],
        max_tokens=500
    )

    return response.choices[0].message.content

# Streamlit Sidebar for user input
st.sidebar.title("User Input")
image_file = st.sidebar.file_uploader("Upload an image of the dish", type=["jpeg", "png"])
age = st.sidebar.number_input("Enter your age", min_value=1)
gender = st.sidebar.selectbox("Select your gender", ["male", "female"])
height_cm = st.sidebar.number_input("Enter your height (cm)", min_value=1.0)
weight_kg = st.sidebar.number_input("Enter your weight (kg)", min_value=1.0)
weight_goal = st.sidebar.selectbox("Weight goal", ["loss", "gain", "maintain"])
activity_level = st.sidebar.selectbox("Activity level", ["sedentary", "light", "moderate", "active", "very active"])
time_frame = st.sidebar.number_input("Time frame to achieve goal (months)", min_value=1)

# Process the image and calculate metrics
if image_file:
    st.write("### Results")
    image_bytes = image_file.read()
    
    # Step 1: Identify the dish
    dish_name = identify_dish(image_bytes)
    st.success(f"Identified Dish: {dish_name}")
    
    # Step 2: Perform Calculations
    metrics = calculate_metrics(age, gender, height_cm, weight_kg, weight_goal, activity_level, time_frame)
    st.write(f"**Your BMI:** {metrics['BMI']:.2f}")
    st.write(f"**Your BMR:** {metrics['BMR']:.2f} calories")
    st.write(f"**Your TDEE:** {metrics['TDEE']:.2f} calories")
    st.write(f"**Ideal Body Weight (IBW):** {metrics['IBW']:.2f} kg")
    st.write(f"**Daily Caloric Needs:** {metrics['Daily Caloric Needs']:.2f} calories")
    
    # Step 3: Generate diet plan
    diet_plan = generate_diet_plan(dish_name, metrics["Daily Caloric Needs"], weight_goal)
    st.write(f"### Diet Plan\n {diet_plan}")

# CSS for styling
st.markdown("""
    <style>
    .stButton button { background-color: #4CAF50; color: white; }
    .stContainer { border: 1px solid #ddd; padding: 20px; margin-bottom: 20px; }
    </style>
""", unsafe_allow_html=True)