app.py

"""
Indian/Pakistani Food Classifier
A deep learning model to identify 80+ Indian and Pakistani dishes
"""

import streamlit as st
import tensorflow as tf
import numpy as np
from PIL import Image
import json
import os
import plotly.graph_objects as go
import pandas as pd
from datetime import datetime
import random

# Page configuration
st.set_page_config(
    page_title="Pakistani & Indian Food Classifier",
    page_icon="🍛",
    layout="wide",
    initial_sidebar_state="expanded"
)

# Custom CSS for beautiful UI
st.markdown("""
<style>
    /* Main container */
    .main {
        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
    }
    
    /* Header styling */
    .header-container {
        background: linear-gradient(135deg, #006400 0%, #008000 50%, #ffffff 100%);
        padding: 2rem;
        border-radius: 20px;
        margin-bottom: 2rem;
        text-align: center;
        box-shadow: 0 10px 30px rgba(0,0,0,0.1);
    }
    
    .header-title {
        font-size: 3rem;
        font-weight: bold;
        color: white;
        margin-bottom: 0.5rem;
    }
    
    .header-subtitle {
        font-size: 1.2rem;
        color: rgba(255,255,255,0.9);
    }
    
    .pakistan-flag {
        font-size: 2rem;
        margin-bottom: 1rem;
    }
    
    /* Card styling */
    .prediction-card {
        background: white;
        border-radius: 15px;
        padding: 1.5rem;
        margin: 1rem 0;
        box-shadow: 0 4px 6px rgba(0,0,0,0.1);
        transition: transform 0.3s;
    }
    
    .prediction-card:hover {
        transform: translateY(-5px);
        box-shadow: 0 6px 12px rgba(0,0,0,0.15);
    }
    
    /* Top prediction styling */
    .top-prediction {
        background: linear-gradient(135deg, #006400 0%, #008000 100%);
        color: white;
        border-radius: 15px;
        padding: 1.5rem;
        margin: 1rem 0;
        text-align: center;
    }
    
    .top-prediction h2 {
        font-size: 2.5rem;
        margin: 0;
    }
    
    .confidence-score {
        font-size: 1.2rem;
        margin-top: 0.5rem;
    }
    
    /* Other predictions */
    .other-prediction {
        background: #f8f9fa;
        border-left: 5px solid #006400;
        border-radius: 10px;
        padding: 1rem;
        margin: 0.8rem 0;
    }
    
    /* Sidebar styling */
    .sidebar-content {
        background: #f0f2f6;
        border-radius: 10px;
        padding: 1rem;
    }
    
    /* Button styling */
    .stButton > button {
        background: linear-gradient(135deg, #006400 0%, #008000 100%);
        color: white;
        border: none;
        padding: 0.5rem 2rem;
        border-radius: 25px;
        font-weight: bold;
        transition: all 0.3s;
    }
    
    .stButton > button:hover {
        transform: scale(1.05);
        box-shadow: 0 4px 8px rgba(0,0,0,0.2);
    }
    
    /* Footer */
    .footer {
        text-align: center;
        padding: 2rem;
        color: #666;
        font-size: 0.8rem;
        margin-top: 3rem;
    }
    
    /* Success/Error messages */
    .success-message {
        background: #d4edda;
        color: #155724;
        padding: 1rem;
        border-radius: 10px;
        margin: 1rem 0;
    }
    
    .info-message {
        background: #d1ecf1;
        color: #0c5460;
        padding: 1rem;
        border-radius: 10px;
        margin: 1rem 0;
    }
</style>
""", unsafe_allow_html=True)

# ============================================================
# LOAD MODEL AND CLASSES
# ============================================================
@st.cache_resource
def load_model():
    """Load the trained model"""
    try:
        model = tf.keras.models.load_model('indian_food_classifier.keras')
        return model
    except:
        try:
            model = tf.keras.models.load_model('/kaggle/working/indian_food_classifier.keras')
            return model
        except:
            st.error("⚠️ Model file not found. Please upload 'indian_food_classifier.keras'")
            return None

@st.cache_data
def load_class_names():
    """Load class names"""
    try:
        with open('class_names.json', 'r') as f:
            class_names = json.load(f)
        return class_names
    except:
        try:
            with open('/kaggle/working/class_names.json', 'r') as f:
                class_names = json.load(f)
            return class_names
        except:
            st.error("⚠️ class_names.json not found. Please upload the file.")
            return None

def preprocess_image(image, target_size=(224, 224)):
    """Preprocess image for model prediction"""
    if image.mode != 'RGB':
        image = image.convert('RGB')
    image = image.resize(target_size)
    img_array = np.array(image) / 255.0
    img_array = np.expand_dims(img_array, axis=0)
    return img_array

def format_food_name(name):
    """Format food name for display"""
    return name.replace('_', ' ').title()

def create_confidence_chart(confidences, labels, top_n=5):
    """Create an interactive confidence chart"""
    fig = go.Figure(data=[
        go.Bar(
            x=confidences[:top_n],
            y=[format_food_name(l) for l in labels[:top_n]],
            orientation='h',
            marker=dict(
                color=confidences[:top_n],
                colorscale='Greens',
                showscale=True,
                colorbar=dict(title="Confidence (%)")
            ),
            text=[f"{c:.1f}%" for c in confidences[:top_n]],
            textposition='outside'
        )
    ])
    
    fig.update_layout(
        title="Top Predictions Confidence Score",
        xaxis_title="Confidence (%)",
        yaxis_title="Food Item",
        height=400,
        margin=dict(l=0, r=0, t=40, b=0),
        paper_bgcolor='rgba(0,0,0,0)',
        plot_bgcolor='rgba(0,0,0,0)'
    )
    
    return fig

# ============================================================
# MAIN APP
# ============================================================
def main():
    # Header
    st.markdown("""
    <div class="header-container">
        <div class="pakistan-flag">
            🇵🇰 🇮🇳 🇵🇰
        </div>
        <div class="header-title">
            🍛 Pakistani & Indian Food Classifier
        </div>
        <div class="header-subtitle">
            AI-powered dish recognition for 80+ South Asian delicacies
        </div>
    </div>
    """, unsafe_allow_html=True)
    
    # Sidebar
    with st.sidebar:
        st.markdown("### 🏆 Model Information")
        st.info("""
        - **Architecture:** EfficientNetV2S
        - **Classes:** 80 Indian/Pakistani Dishes
        - **Accuracy:** 59.25%
        - **Input Size:** 224x224 pixels
        """)
        
        st.markdown("---")
        st.markdown("### 🍽️ Popular Dishes")
        
        # Random popular dishes
        popular_dishes = [
            "Biryani", "Nihari", "Butter Chicken", "Aloo Gobi",
            "Samosa", "Gulab Jamun", "Naan", "Haleem",
            "Karahi", "Seekh Kebab", "Dal Makhani", "Ras Malai"
        ]
        
        for dish in random.sample(popular_dishes, min(6, len(popular_dishes))):
            st.markdown(f"• {dish}")
        
        st.markdown("---")
        st.markdown("### 📊 How It Works")
        st.markdown("""
        1. 📸 Upload a clear photo of food
        2. 🤖 AI analyzes the image
        3. 🎯 Get top 5 predictions with confidence scores
        4. 📈 View detailed confidence chart
        """)
        
        st.markdown("---")
        st.markdown("### 💡 Tips for Best Results")
        st.markdown("""
        - Use well-lit photos
        - Focus on the main dish
        - Avoid cluttered backgrounds
        - Single dish per photo works best
        """)
        
        st.markdown("---")
        st.markdown("Made with ❤️ for South Asian Cuisine")
    
    # Main content area
    col1, col2 = st.columns([1, 1])
    
    with col1:
        st.markdown("### 📤 Upload Food Image")
        uploaded_file = st.file_uploader(
            "Choose an image...",
            type=['jpg', 'jpeg', 'png', 'webp', 'gif'],
            help="Upload a clear image of Pakistani or Indian food"
        )
        
        if uploaded_file is not None:
            image = Image.open(uploaded_file)
            
            # Display image with styling
            st.markdown("#### Preview")
            st.image(image, caption="Uploaded Image", use_container_width=True)
            
            # Image info
            st.caption(f"📐 Image size: {image.size[0]} x {image.size[1]} pixels")
    
    with col2:
        if uploaded_file is not None:
            st.markdown("### 🔍 Analysis Results")
            
            with st.spinner("🍛 Analyzing your food image..."):
                # Load model and classes
                model = load_model()
                class_names = load_class_names()
                
                if model is not None and class_names is not None:
                    # Preprocess and predict
                    processed_img = preprocess_image(image)
                    predictions = model.predict(processed_img, verbose=0)[0]
                    
                    # Get top 5 predictions
                    top_5_idx = np.argsort(predictions)[-5:][::-1]
                    top_5_names = [class_names[idx] for idx in top_5_idx]
                    top_5_confidences = [predictions[idx] * 100 for idx in top_5_idx]
                    
                    # Display top prediction (highlighted)
                    st.markdown(f"""
                    <div class="top-prediction">
                        <div style="font-size: 1.2rem;">🥇 Top Prediction</div>
                        <h2>{format_food_name(top_5_names[0])}</h2>
                        <div class="confidence-score">Confidence: {top_5_confidences[0]:.2f}%</div>
                    </div>
                    """, unsafe_allow_html=True)
                    
                    # Display other predictions
                    st.markdown("#### Other Possibilities")
                    
                    for i in range(1, min(5, len(top_5_names))):
                        confidence_percent = top_5_confidences[i]
                        
                        # Determine emoji based on rank
                        if i == 1:
                            emoji = "🥈"
                        elif i == 2:
                            emoji = "🥉"
                        else:
                            emoji = f"{i+1}️⃣"
                        
                        st.markdown(f"""
                        <div class="other-prediction">
                            <strong>{emoji} {format_food_name(top_5_names[i])}</strong><br>
                            <span style="color: #666;">Confidence: {confidence_percent:.2f}%</span>
                        </div>
                        """, unsafe_allow_html=True)
                    
                    # Confidence chart
                    st.markdown("---")
                    st.markdown("### 📊 Confidence Analysis")
                    
                    fig = create_confidence_chart(top_5_confidences, top_5_names, top_n=5)
                    st.plotly_chart(fig, use_container_width=True)
                    
                    # Confidence meter for top prediction
                    st.markdown("#### Confidence Meter")
                    confidence_level = top_5_confidences[0]
                    
                    if confidence_level > 70:
                        st.success(f"🎯 High confidence! The AI is very sure this is {format_food_name(top_5_names[0])}")
                    elif confidence_level > 50:
                        st.warning(f"🤔 Medium confidence. The AI thinks it's {format_food_name(top_5_names[0])}")
                    else:
                        st.info(f"💡 Low confidence. Try uploading a clearer photo for better results")
    
    # Footer with additional information
    st.markdown("---")
    
    col1, col2, col3 = st.columns(3)
    
    with col1:
        st.markdown("""
        ### 🎯 Supported Cuisines
        - Punjabi
        - Mughlai
        - South Indian
        - Sindhi
        - Kashmiri
        - Hyderabadi
        """)
    
    with col2:
        st.markdown("""
        ### 🍜 Dish Categories
        - Curries & Gravies
        - Rice Dishes (Biryani)
        - Breads (Naan, Roti)
        - Desserts & Sweets
        - Snacks & Appetizers
        - Beverages
        """)
    
    with col3:
        st.markdown("""
        ### 📈 Model Performance
        - 59.25% Top-1 Accuracy
        - 80+ Food Classes
        - 3,200 Training Images
        - EfficientNetV2S Backbone
        - Real-time Predictions
        """)
    
    # Footer
    st.markdown("""
    <div class="footer">
        <p>🇵🇰 Celebrating the rich culinary heritage of Pakistan and India 🇮🇳</p>
        <p>⚠️ Note: For best results, use clear, well-lit images of individual dishes. The model works best on traditional South Asian cuisine.</p>
        <p>Made with Streamlit & TensorFlow | Model trained on 80+ dishes</p>
    </div>
    """, unsafe_allow_html=True)

if __name__ == "__main__":
    main()