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| import streamlit as st | |
| import json | |
| import os | |
| import io | |
| import base64 | |
| import boto3 | |
| from PIL import Image | |
| from streamlit_tags import st_tags | |
| # 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 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) | |
| # Streamlit Layout - Three Panel Design | |
| st.title("🍽️ Food Image Review & Annotation") | |
| col1, col2, col3 = st.columns([1, 1, 1]) # Equal size columns | |
| # Upload Image | |
| uploaded_file = st.file_uploader("Upload an image of your food", type=["jpg", "png", "jpeg"]) | |
| if uploaded_file: | |
| image = Image.open(uploaded_file) | |
| st.session_state["original_image"] = image | |
| # Resize image in Streamlit before sending to S3 | |
| def resize_image(image): | |
| aspect_ratio = image.width / image.height | |
| new_width = 512 | |
| new_height = int(new_width / aspect_ratio) | |
| resized_image = image.resize((new_width, new_height)) | |
| return resized_image | |
| if "original_image" in st.session_state: | |
| original_img = st.session_state["original_image"] | |
| processed_img = resize_image(original_img) | |
| # 🖼️ Panel 1: Original Image | |
| with col1: | |
| st.subheader("📷 Original Image") | |
| st.image(original_img, caption="Uploaded", use_container_width=True) | |
| # 🖼️ Panel 2: Resized Image (512x512 Maintaining Aspect Ratio) | |
| with col2: | |
| st.subheader("🖼️ Processed Image") | |
| st.image(processed_img, caption="512x512 Maintained Aspect", use_container_width=True) | |
| # ✏️ Panel 3: Food Annotations with Intellisense | |
| with col3: | |
| st.subheader("📝 Add Annotations") | |
| # Expanded food suggestions | |
| suggested_foods = [ | |
| "Pizza", "Pasta", "Tacos", "Sushi", "Ramen", "Kimchi", "Bratwurst", "Jambalaya", | |
| "Chicken", "Rice", "Steak", "Bread", "Cheese", "Salmon", "Avocado", "Eggs", | |
| "Carrots", "Tomatoes", "Cucumber", "Yogurt", "Peanuts", "Lettuce", "Pierogies", "Mongolian Beef", "Spaghetti Bolognese", | |
| "Bigos", "Stuffed Cabbage", "Zurek", "Schnitzel", "Tomato Soup", "Potato Pancakes", "Blintzes", "Broccoli Chicken" | |
| ] | |
| # User selects food items from suggestions | |
| food_items = st_tags( | |
| label="Enter food items", | |
| text="Add items...", | |
| value=[], | |
| suggestions=suggested_foods, | |
| maxtags=10, | |
| ) | |
| if st.button("Save Annotations"): | |
| metadata_table.update_item( | |
| Key={"image_id": uploaded_file.name}, | |
| UpdateExpression="SET annotations = :a, processing_status = :p, s3_url = :s, tokens_earned = :t", | |
| ExpressionAttributeValues={ | |
| ":a": food_items, | |
| ":p": "processed", | |
| ":s": f"s3://{S3_BUCKET_NAME}/raw-uploads/{uploaded_file.name}", | |
| ":t": 1 if food_items else 0 | |
| }, | |
| ) | |
| st.success("✅ Annotations saved successfully!") | |