app.py

import streamlit as st
import json
import os
import boto3
from PIL import Image
import firebase_admin
from firebase_admin import credentials, auth
import pandas as pd

# Load AWS credentials using correct HF Secrets
AWS_ACCESS_KEY = os.getenv("AWS_ACCESS_KEY")
AWS_SECRET_KEY = os.getenv("AWS_SECRET_KEY")
AWS_REGION = os.getenv("AWS_REGION", "us-east-1")
S3_BUCKET_NAME = os.getenv("S3_BUCKET_NAME", "food-image-crowdsourcing")
DYNAMODB_TABLE = os.getenv("DYNAMODB_TABLE", "image_metadata")

# Load Firebase credentials
FIREBASE_CONFIG = json.loads(os.getenv("FIREBASE_CONFIG", "{}"))

# Initialize Firebase Admin SDK (Prevent multiple initialization)
if not firebase_admin._apps:
    cred = credentials.Certificate(FIREBASE_CONFIG)
    firebase_admin.initialize_app(cred)

# Initialize AWS Services (S3 & DynamoDB)
s3 = boto3.client(
    "s3",
    aws_access_key_id=AWS_ACCESS_KEY,
    aws_secret_access_key=AWS_SECRET_KEY,
)

dynamodb = boto3.resource(
    "dynamodb", 
    region_name=AWS_REGION, 
    aws_access_key_id=AWS_ACCESS_KEY, 
    aws_secret_access_key=AWS_SECRET_KEY,
)
metadata_table = dynamodb.Table(DYNAMODB_TABLE)

# Food Intellisense List
FOOD_SUGGESTIONS = [
    "Apple", "Banana", "Pizza", "Burger", "Pasta", "Sushi", "Tacos", "Salad",
    "Chicken (Baked)", "Chicken (Roasted)", "Chicken (Grilled)", "Chicken (Boiled)",
    "Fish (Grilled)", "Fish (Fried)", "Fish (Steamed)", "Beef Steak", "Pork Chops",
    "Spaghetti Carbonara", "Lasagna", "Pad Thai", "Dim Sum", "Kimchi Fried Rice",
    "Biryani", "Croissant", "Baguette", "Miso Soup", "Ramen", "Pierogi", "Gyoza",
    "Schnitzel", "Creole Gumbo", "Jambalaya", "Tandoori Chicken", "Falafel", "Shawarma"
]  # Extended with diverse cuisines

# Unit options for food weight/volume
UNIT_OPTIONS = ["grams", "ounces", "teaspoons", "tablespoons", "cups", "slices", "pieces"]

# Streamlit Layout - Authentication Section
st.sidebar.title("🔑 User Authentication")
auth_option = st.sidebar.radio("Select an option", ["Login", "Sign Up", "Logout"])

if auth_option == "Sign Up":
    email = st.sidebar.text_input("Email")
    password = st.sidebar.text_input("Password", type="password")
    if st.sidebar.button("Sign Up"):
        try:
            user = auth.create_user(email=email, password=password)
            st.sidebar.success("✅ User created successfully! Please log in.")
        except Exception as e:
            st.sidebar.error(f"Error: {e}")

if auth_option == "Login":
    email = st.sidebar.text_input("Email")
    password = st.sidebar.text_input("Password", type="password")
    if st.sidebar.button("Login"):
        try:
            user = auth.get_user_by_email(email)
            st.session_state["user_id"] = user.uid
            st.session_state["tokens"] = 0  # Initialize token count
            st.sidebar.success("✅ Logged in successfully!")
        except Exception as e:
            st.sidebar.error(f"Login failed: {e}")

if auth_option == "Logout" and "user_id" in st.session_state:
    del st.session_state["user_id"]
    st.sidebar.success("✅ Logged out successfully!")

# Ensure user is logged in before uploading
if "user_id" not in st.session_state:
    st.warning("⚠️ Please log in to upload images.")
    st.stop()

# Streamlit Layout - Three Panel Design
st.title("🍽️ Food Image Review & Annotation")
col1, col2 = st.columns([1, 1])

# Compliance & Disclaimer Section
st.markdown("### **Terms & Conditions**")
st.write(
    "By uploading an image, you agree to transfer full copyright to the research team for AI training purposes."
    " You are responsible for ensuring you own the image and it does not violate any copyright laws."
    " We do not guarantee when tokens will be redeemable. Keep track of your user ID.")
terms_accepted = st.checkbox("I agree to the terms and conditions", key="terms_accepted")
if not terms_accepted:
    st.warning("⚠️ You must agree to the terms before proceeding.")
    st.stop()

# Upload Image
uploaded_file = st.file_uploader("Upload an image of your food", type=["jpg", "png", "jpeg"])
if uploaded_file:
    original_img = Image.open(uploaded_file)
    st.session_state["original_image"] = original_img
    st.session_state["tokens"] += 1  # Earn 1 token for upload

# If an image has been uploaded, process and display it
if "original_image" in st.session_state:
    original_img = st.session_state["original_image"]
    
    def resize_image(image, max_size=512):
        aspect_ratio = image.width / image.height
        if image.width > image.height:
            new_width = max_size
            new_height = int(max_size / aspect_ratio)
        else:
            new_height = max_size
            new_width = int(max_size * aspect_ratio)
        return image.resize((new_width, new_height))

    processed_img = resize_image(original_img)

    col1, col2 = st.columns(2)
    with col1:
        st.subheader("📷 Original Image")
        st.image(original_img, caption=f"Original ({original_img.width}x{original_img.height} pixels)", use_container_width=True)
    with col2:
        st.subheader("🖼️ Processed Image")
        st.image(processed_img, caption=f"Processed ({processed_img.width}x{processed_img.height} pixels)", use_container_width=True)
    st.session_state["processed_image"] = processed_img

# Display earned tokens
st.success(f"🎉 Tokens Earned: {st.session_state['tokens']}")