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_DISH"))
client1 = InferenceClient(api_key=os.getenv("HF_API_TOKEN_DIET"))
# 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 highly specialized food identification AI with extensive knowledge of global cuisines. Your sole task is to accurately identify dishes from images. Adhere strictly to these guidelines:\n1. Analyze the image thoroughly, focusing on ingredients, presentation, and cultural context.\n2. Provide ONLY the name of the main dish or dishes visible. Do not list individual ingredients or components.\n3. Use the most specific and widely recognized name for the dish.\n4. If multiple distinct dishes are present, list them separated by commas.\n5. If you cannot identify a dish with high confidence (>90%), respond with 'Unidentified dish'.\n6. Do not provide any explanations, descriptions, or additional commentary.\n7. Respond in a concise, list-like format.\nYour response should contain nothing but the dish name(s) or 'Unidentified dish'.",
                "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):
    bmi = weight_kg / ((height_cm / 100) ** 2)

    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

    activity_multipliers = {
        "sedentary": 1.2,
        "light": 1.375,
        "moderate": 1.55,
        "active": 1.725,
        "very active": 1.9
    }
    tdee = bmr * activity_multipliers[activity_level]

    if gender == "male":
        ibw = 50 + (0.91 * (height_cm - 152.4))
    else:
        ibw = 45.5 + (0.91 * (height_cm - 152.4))

    if weight_goal == "loss":
        daily_caloric_needs = tdee - 500
    elif weight_goal == "gain":
        daily_caloric_needs = tdee + 500
    else:
        daily_caloric_needs = tdee

    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} (e.g., weight loss, weight gain)

    Provide the response in the following format:
    1. Dish assessment (e.g., "The dish {dish_name} is suitable for your goal" or "The dish {dish_name} is not suitable for your goal, as it is too high in calories for weight goal").
    2. Suggest an Indian diet plan that stays near to the {calorie_intake_per_day}. For each meal (morning, lunch, evening), list the dish name, calorie count, and ingredients required to make it.And Make it with in 700 Tokens.
    """
    response = client1.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=700
    )

    return response.choices[0].message.content

# Streamlit App Title
st.title("AI Diet Planner")

# 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)

# Submit button
submit = st.sidebar.button("Submit")

# Process the image and calculate metrics upon submission
if submit:
    if image_file:
        st.write("### Results")
        image_bytes = image_file.read()

        # Step 1: Identify the dish
        dish_name = identify_dish(image_bytes)
        st.markdown("<hr>", unsafe_allow_html=True)
        st.write("#### Dish Name Identified:")
        st.markdown(f"<div style='background-color: #d4edda; color: #155724; padding: 10px; border-radius: 10px;'>{dish_name}</div>", unsafe_allow_html=True)

        # Step 2: Perform Calculations
        metrics = calculate_metrics(age, gender, height_cm, weight_kg, weight_goal, activity_level, time_frame)
        st.markdown("<hr>", unsafe_allow_html=True)
        st.write("#### Metrics Calculated:")
        st.markdown(f"""
            <div style='background-color: #f8d7da; color: #721c24; padding: 10px; border-radius: 10px;'>
                <p><b>Your BMI:</b> {metrics['BMI']:.2f}</p>
                <p><b>Your BMR(Basal metabolic rate):</b> {metrics['BMR']:.2f} calories</p>
                <p><b>Your TDEE(Total Daily Energy Expenditure):</b> {metrics['TDEE']:.2f} calories</p>
                <p><b>Ideal Body Weight (IBW):</b> {metrics['IBW']:.2f} kg</p>
                <p><b>Daily Caloric Needs:</b> {metrics['Daily Caloric Needs']:.2f} calories</p>
            </div>
        """, unsafe_allow_html=True)

        # Step 3: Generate diet plan
        diet_plan = generate_diet_plan(dish_name, metrics["Daily Caloric Needs"], weight_goal)
        st.markdown("<hr>", unsafe_allow_html=True)
        st.write("#### Diet Plan Based on Dish & Goal:")
        st.markdown(f"<div style='background-color: #d1ecf1; color: #0c5460; padding: 10px; border-radius: 10px;'>{diet_plan}</div>", unsafe_allow_html=True)

    else:
        st.error("Please upload a valid image in JPEG or PNG format.")

# CSS styling
st.markdown("""
    <style>
    .stButton button { background-color: #4CAF50; color: white; }
    .stContainer { border: 1px solid #ddd; padding: 20px; margin-bottom: 20px; }
    hr { border: 1px solid #e9ecef; margin: 20px 0; }
    </style>
""", unsafe_allow_html=True)