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 Curry", "Steak", "Fish & Chips", "Dumplings", "Kimchi", "Pancakes",
    "Biryani", "Croissant", "Baguette", "Miso Soup", "Ramen", "Pierogi"
]  # Extend this list 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']}")

# Annotation Table
st.subheader("📝 Add Annotations")
if "annotations" not in st.session_state:
    st.session_state["annotations"] = []

new_food_item = st.selectbox("Food Item", FOOD_SUGGESTIONS + ["Other..."])
if new_food_item == "Other...":
    new_food_item = st.text_input("Enter custom food item")
new_quantity = st.number_input("Quantity", min_value=0, step=1)
new_unit = st.selectbox("Unit", UNIT_OPTIONS)

if st.button("Add Annotation"):
    st.session_state["annotations"].append({
        "name": new_food_item,
        "quantity": new_quantity,
        "unit": new_unit
    })
    st.session_state["tokens"] += 1  # Earn 1 token per annotation

st.table(st.session_state["annotations"])