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import streamlit as st
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from PIL import Image
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import torch
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from transformers import AutoImageProcessor, AutoModelForImageClassification
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import pandas as pd
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st.set_page_config(
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page_title="CALIX",
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page_icon="apple",
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layout="centered",
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initial_sidebar_state="collapsed"
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)
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st.markdown("""
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<meta name="apple-mobile-web-app-capable" content="yes">
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<meta name="apple-mobile-web-app-status-bar-style" content="black">
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<meta name="apple-mobile-web-app-title" content="CALIX">
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<link rel="manifest" href="/manifest.json">
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""", unsafe_allow_html=True)
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st.markdown("""
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<style>
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body, .stApp {background:#121212 !important; color:#FFF !important;}
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.stTextInput input {background:#1E1E1E !important; color:#FFF !important; border:1px solid #444 !important;}
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.stFileUploader > div {background:#1E1E1E !important; border:2px dashed #444 !important;}
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.stButton>button {background:#FF9800; color:white; border-radius:20px; padding:10px 24px; font-weight:bold;}
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.stButton>button:hover {background:#F57C00;}
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.nav {background:#1E1E1E; padding:15px; box-shadow:0 2px 10px rgba(0,0,0,0.3); display:flex; justify-content:center; flex-wrap:wrap; margin-bottom:20px;}
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.logo {font-size:32px; color:#4CAF50; font-weight:bold; margin-right:30px;}
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.link {margin:0 15px; color:#BBB; text-decoration:none; padding:8px 16px; border-radius:8px;}
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.link:hover {background:#333; color:#4CAF50;}
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.card {background:#1E1E1E; padding:25px; border-radius:12px; border:1px solid #333; margin:15px 0;}
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.title {color:#4CAF50; font-size:42px; text-align:center; margin:30px 0;}
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.footer {text-align:center; color:#666; font-size:14px; margin-top:60px; padding:15px; border-top:1px solid #333;}
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.nav-btn {background:#333; color:#FFF; padding:8px 16px; border-radius:8px; border:none; margin:0 5px;}
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.nav-btn:hover {background:#555;}
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.stDataFrame td, .stDataFrame th {color:#FFF !important; background:#1E1E1E !important;}
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</style>
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""", unsafe_allow_html=True)
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def footer():
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st.markdown('<div class="footer">©2025 CALIX|AMC CSE-AIML|Open Source Powered</div>', unsafe_allow_html=True)
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def nav_bar():
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st.markdown("""
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<div class="nav">
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<div class="logo">CALIX</div>
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<a href="/?page=home" class="link">Home</a>
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<a href="/?page=estimate" class="link">Estimate</a>
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<a href="/?page=food_library" class="link">Food Library</a>
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<a href="/?page=health_tips" class="link">Health Tips</a>
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<a href="/?page=about" class="link">About</a>
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</div>
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""", unsafe_allow_html=True)
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page = st.query_params.get("page", "home")
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@st.cache_resource(show_spinner="Loading AI...")
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def load_model():
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processor = AutoImageProcessor.from_pretrained("eslamxm/vit-base-food101")
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model = AutoModelForImageClassification.from_pretrained("eslamxm/vit-base-food101")
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return processor, model
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processor, model = load_model()
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food_names = [model.config.id2label[i].replace("_", " ").title() for i in range(len(model.config.id2label))]
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nutrition_db = {
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"Pizza": {"cal": 285, "carb": "36g", "prot": "12g", "fat": "10g"},
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"Biryani": {"cal": 320, "carb": "45g", "prot": "15g", "fat": "12g"},
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"Appam": {"cal": 180, "carb": "32g", "prot": "3g", "fat": "5g"},
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"Samosa": {"cal": 250, "carb": "25g", "prot": "5g", "fat": "15g"},
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"Idli": {"cal": 60, "carb": "12g", "prot": "2g", "fat": "0.5g"},
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"Dosa": {"cal": 170, "carb": "28g", "prot": "4g", "fat": "6g"},
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"Burger": {"cal": 500, "carb": "45g", "prot": "25g", "fat": "28g"},
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"Pancakes": {"cal": 220, "carb": "32g", "prot": "6g", "fat": "8g"},
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"Chicken Curry": {"cal": 320, "carb": "15g", "prot": "25g", "fat": "18g"},
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"default": {"cal": 250, "carb": "30g", "prot": "10g", "fat": "10g"}
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}
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if "history" not in st.session_state:
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st.session_state.history = []
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health_tips = [
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"Drink 8 glasses of water daily.", "Eat 5 servings of fruits and vegetables.", "Walk 30 minutes every day.",
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"Avoid sugary drinks.", "Sleep 7-8 hours per night.", "Choose whole grains over refined.",
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"Limit processed foods.", "Eat protein with every meal.", "Reduce salt intake.",
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"Cook at home more often.", "Read food labels.", "Eat slowly and mindfully.",
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"Include healthy fats like avocado.", "Limit alcohol consumption.", "Practice portion control.",
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"Add spices instead of salt.", "Eat breakfast daily.", "Stay hydrated during exercise.",
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"Choose lean proteins.", "Include fiber-rich foods.", "Limit fried foods.",
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"Eat more plant-based meals.", "Avoid late-night snacking.", "Chew food thoroughly.",
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"Include omega-3 rich foods.", "Reduce caffeine after noon.", "Eat colorful foods.",
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"Plan meals ahead.", "Keep healthy snacks handy.", "Avoid emotional eating.",
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"Exercise in the morning.", "Stand more, sit less.", "Take stairs instead of elevator.",
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"Practice yoga or meditation.", "Get sunlight daily.", "Limit screen time before bed.",
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"Eat fermented foods for gut health.", "Include nuts and seeds.", "Drink green tea.",
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"Avoid trans fats.", "Eat fish twice a week.", "Include legumes in diet.",
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"Reduce red meat intake.", "Eat seasonal foods.", "Grow your own herbs.",
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"Share meals with family.", "Practice gratitude before eating.", "Try new healthy recipes.",
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"Keep a food journal.", "Celebrate small wins.", "Stay consistent, not perfect."
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]
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def nav_buttons(prev=None, next=None):
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col1, col2, col3 = st.columns([1,1,1])
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with col1:
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if prev and st.button("Back", key=f"back_{page}"):
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st.query_params["page"] = prev
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st.rerun()
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with col3:
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if next and st.button("Next", key=f"next_{page}"):
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st.query_params["page"] = next
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st.rerun()
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nav_bar()
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if page == "home":
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st.markdown('<h1 class="title">Know What You Eat - Instantly.</h1>', unsafe_allow_html=True)
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col1, col2 = st.columns(2)
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with col1:
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st.markdown('<div class="card">', unsafe_allow_html=True)
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uploaded = st.file_uploader("Upload food photo", type=["jpg","png","jpeg"])
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if uploaded:
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st.session_state.uploaded_image = uploaded
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st.image(uploaded, width=250)
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st.markdown('</div>', unsafe_allow_html=True)
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with col2:
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st.markdown('<div class="card"><p><b>Detects 101 Foods:</b><br>Pizza, Biryani, Appam, Samosa, Idli, Dosa, Burger, etc.</p></div>', unsafe_allow_html=True)
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nav_buttons(next="estimate")
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footer()
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elif page == "estimate":
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st.markdown('<h1 class="title">Estimate Calories</h1>', unsafe_allow_html=True)
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uploaded = st.session_state.get("uploaded_image") or st.file_uploader("Upload photo", type=["jpg","png","jpeg"])
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if uploaded:
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img = Image.open(uploaded).convert("RGB")
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st.image(img, width=300)
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with st.spinner("Detecting food..."):
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inputs = processor(images=img, return_tensors="pt")
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with torch.no_grad():
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outputs = model(**inputs)
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idx = outputs.logits.argmax(-1).item()
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name = food_names[idx]
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st.success(f"*Detected:* {name}")
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nut = nutrition_db.get(name, nutrition_db["default"])
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st.markdown(f"""
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<div class="card">
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<p><b>Calories:</b> {nut['cal']} kcal/100g</p>
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<p><b>Carbs:</b> {nut['carb']}</p>
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<p><b>Protein:</b> {nut['prot']}</p>
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<p><b>Fat:</b> {nut['fat']}</p>
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</div>
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""", unsafe_allow_html=True)
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if st.button("Add to Daily Log"):
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st.session_state.history.append({
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"Food": name,
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"Calories": nut['cal'],
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"Carbs": nut['carb'],
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"Protein": nut['prot'],
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"Fat": nut['fat']
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})
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st.success("Added!")
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nav_buttons(prev="home", next="food_library")
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else:
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nav_buttons(prev="home")
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footer()
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elif page == "food_library":
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st.markdown('<h1 class="title">Food Library (History)</h1>', unsafe_allow_html=True)
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if st.session_state.history:
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df = pd.DataFrame(st.session_state.history)
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st.dataframe(df, use_container_width=True)
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total_cal = sum([x for x in df["Calories"] if isinstance(x, (int, float))])
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st.info(f"*Total Calories Logged:* {total_cal} kcal")
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else:
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st.info("No food logged yet.")
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nav_buttons(prev="estimate", next="health_tips")
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footer()
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elif page == "health_tips":
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st.markdown('<h1 class="title">Health Tips</h1>', unsafe_allow_html=True)
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tip_idx = st.session_state.get("tip_idx", 0)
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st.markdown(f'<div class="card"><p>{health_tips[tip_idx]}</p></div>', unsafe_allow_html=True)
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col1, col2, col3 = st.columns([1,1,1])
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with col1:
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if st.button("Previous"):
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st.session_state.tip_idx = (tip_idx - 1) % len(health_tips)
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st.rerun()
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with col3:
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if st.button("Next"):
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st.session_state.tip_idx = (tip_idx + 1) % len(health_tips)
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st.rerun()
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nav_buttons(prev="food_library", next="about")
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footer()
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elif page == "about":
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st.markdown('<h1 class="title">About CALIX</h1>', unsafe_allow_html=True)
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st.markdown("""
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*CALIX* is an AI food detector that identifies *101 foods* from photos.
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### Features
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- *Detects exact names*: Pizza, Biryani, Appam, Samosa, Idli, Dosa, etc.
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- *Local nutrition*: Calories, Carbs, Protein, Fat (no internet needed)
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- *History log*: Saves all scans with full details
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- *50 Health Tips*
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- *Dark theme + navigation*
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### AI Model
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- eslamxm/vit-base-food101 (Public, 101 classes from Food-101 dataset)
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- Dataset: 101,000 images, 101 classes
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*Built by:*
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Rudhreshwaran, Shreyas, Tiya Singh, Shubham Prasad, Shubham Raj
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*AMC CSE-AIML | 2025*
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""")
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nav_buttons(prev="health_tips")
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footer() |
