Update app.py

app.py

@@ -7,6 +7,7 @@ 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")
@@ -38,6 +39,16 @@ dynamodb = boto3.resource(
 )
 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", "pieces"]
+
 # Streamlit Layout - Authentication Section
 st.sidebar.title("🔑 User Authentication")
 auth_option = st.sidebar.radio("Select an option", ["Login", "Sign Up", "Logout"])
@@ -74,7 +85,7 @@ if "user_id" not in st.session_state:
 
 # Streamlit Layout - Three Panel Design
 st.title("🍽️ Food Image Review & Annotation")
-col1, col2, col3 = st.columns([1, 1, 1])
+col1, col2 = st.columns([1, 1])
 
 # Compliance & Disclaimer Section
 st.markdown("### **Terms & Conditions**")
@@ -101,49 +112,57 @@ def resize_image(image, new_width=512):
 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=f"Uploaded ({original_img.width}x{original_img.height} pixels)", use_container_width=True)
+        st.image(original_img, caption=f"Uploaded ({original_img.width}x{original_img.height} pixels)", use_column_width=True)
 
     # 🖼️ Panel 2: Resized Image (512x512 Maintaining Aspect Ratio)
     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)
-
-    # ✏️ Panel 3: Food Annotations with Structured Input
-    with col3:
-        st.subheader("📝 Add Annotations")
-        food_items = []
-
-        # User enters food items one at a time
-        new_food = st.text_input("Enter food item")
-        if st.button("+ Add Food Item"):
-            if new_food:
-                food_items.append({"name": new_food, "ingredients": [], "weight": None})
-
-        # Display added food items
-        for idx, food in enumerate(food_items):
-            st.write(f"{idx+1}. {food['name']}")
-            sub_ingredient = st.text_input(f"Add ingredient for {food['name']}", key=f"sub_{idx}")
-            if st.button(f"+ Add Ingredient to {food['name']}", key=f"btn_{idx}"):
-                food["ingredients"].append(sub_ingredient)
-
-            weight = st.text_input(f"Enter weight for {food['name']} (grams)", key=f"wt_{idx}")
-            if weight:
-                food["weight"] = weight
-
-        if st.button("Save Annotations"):
-            metadata_table.update_item(
-                Key={"image_id": uploaded_file.name},
-                UpdateExpression="SET user_id = :u, annotations = :a, processing_status = :p, s3_url = :s, tokens_earned = :t",
-                ExpressionAttributeValues={
-                    ":u": st.session_state["user_id"],
-                    ":a": food_items,
-                    ":p": "processed",
-                    ":s": f"s3://{S3_BUCKET_NAME}/raw-uploads/{uploaded_file.name}",
-                    ":t": len(food_items)
-                },
-            )
-            st.success("✅ Annotations saved successfully!")
+        st.image(processed_img, caption=f"Processed ({processed_img.width}x{processed_img.height} pixels)", use_column_width=True)
+
+# 📝 Annotation Table
+st.subheader("📝 Add Annotations")
+
+if "annotations" not in st.session_state:
+    st.session_state["annotations"] = []
+
+# Create dataframe for annotations
+annotation_df = pd.DataFrame(st.session_state["annotations"], columns=["Food Item", "Unit", "Quantity", "Ingredients"])
+
+# Food input with intellisense
+food_item = st.selectbox("Select food item or type custom:", FOOD_SUGGESTIONS + ["Other"])
+if food_item == "Other":
+    food_item = st.text_input("Enter food item manually:")
+
+unit = st.selectbox("Select unit:", UNIT_OPTIONS)
+quantity = st.number_input("Enter quantity:", min_value=0.1, step=0.1)
+
+# Dynamic ingredient input
+ingredients = st.text_area("Enter ingredients (one per line):")
+ingredient_list = [i.strip() for i in ingredients.split("\n") if i.strip()]
+
+if st.button("Add Food Item"):
+    if food_item and unit and quantity:
+        st.session_state["annotations"].append([food_item, unit, quantity, ingredient_list])
+        st.success(f"✅ Added {food_item} successfully!")
+
+# Display annotation table
+st.dataframe(pd.DataFrame(st.session_state["annotations"], columns=["Food Item", "Unit", "Quantity", "Ingredients"]))
+
+# Save Annotations to DynamoDB
+if st.button("Save Annotations"):
+    metadata_table.update_item(
+        Key={"image_id": uploaded_file.name},
+        UpdateExpression="SET user_id = :u, annotations = :a, processing_status = :p, s3_url = :s, tokens_earned = :t",
+        ExpressionAttributeValues={
+            ":u": st.session_state["user_id"],
+            ":a": st.session_state["annotations"],
+            ":p": "processed",
+            ":s": f"s3://{S3_BUCKET_NAME}/raw-uploads/{uploaded_file.name}",
+            ":t": len(st.session_state["annotations"])
+        },
+    )
+    st.success("✅ Annotations saved successfully!")