app/frontend.py

import streamlit as st
import requests
import json
from PIL import Image
import io
import base64
from datetime import datetime
import pandas as pd
import plotly.express as px

# Configure the app
st.set_page_config(
    page_title="IndiScan - Product Health Analyzer",
    page_icon="🔍",
    layout="wide"
)

# API endpoint
API_URL = "http://localhost:8000"  # Change this when deploying

def main():
    # Sidebar
    st.sidebar.title("IndiScan 🔍")
    scan_option = st.sidebar.radio(
        "Choose scan method:",
        ["Barcode", "Image Upload", "Manual Entry"]
    )
    
    # Main content
    st.title("IndiScan - Product Health Analyzer")
    st.markdown("""
    Analyze food and cosmetic products for health risks and get detailed insights.
    Upload an image, enter a barcode, or manually input product details.
    """)
    
    # Admin section in sidebar
    with st.sidebar.expander("Admin Controls 🔐"):
        admin_username = st.text_input("Username")
        admin_password = st.text_input("Password", type="password")
        if st.button("Login"):
            try:
                auth = (admin_username, admin_password)
                response = requests.get(f"{API_URL}/export", auth=auth)
                if response.status_code == 200:
                    st.sidebar.success("Logged in as admin")
                    st.session_state['admin_auth'] = auth
                else:
                    st.sidebar.error("Invalid credentials")
            except Exception as e:
                st.sidebar.error(f"Login failed: {str(e)}")

    # Main content based on selected option
    if scan_option == "Barcode":
        barcode_scanner()
    elif scan_option == "Image Upload":
        image_scanner()
    else:
        manual_entry()

def barcode_scanner():
    st.header("Barcode Scanner 📱")
    barcode = st.text_input("Enter barcode number:")
    
    if barcode:
        try:
            response = requests.post(f"{API_URL}/scan/barcode", params={"barcode": barcode})
            if response.status_code == 200:
                display_results(response.json())
            else:
                st.error("Product not found")
        except Exception as e:
            st.error(f"Error: {str(e)}")

def image_scanner():
    st.header("Image Scanner 📸")
    uploaded_file = st.file_uploader("Upload product image", type=["jpg", "jpeg", "png"])
    
    if uploaded_file:
        try:
            # Display uploaded image
            image = Image.open(uploaded_file)
            st.image(image, caption="Uploaded Image", use_column_width=True)
            
            # Process image
            files = {"file": uploaded_file}
            response = requests.post(f"{API_URL}/scan/image", files=files)
            
            if response.status_code == 200:
                display_results(response.json())
            else:
                st.error("Failed to process image")
        except Exception as e:
            st.error(f"Error: {str(e)}")

def manual_entry():
    st.header("Manual Entry ✍️")
    
    col1, col2 = st.columns(2)
    
    with col1:
        product_type = st.selectbox("Product Type", ["Food", "Cosmetic"])
        ingredients_text = st.text_area("Enter ingredients list (comma-separated or as shown on package):")
    
    with col2:
        if ingredients_text:
            try:
                response = requests.post(
                    f"{API_URL}/analyze/text",
                    data={
                        "text": ingredients_text,
                        "product_type": product_type.lower()
                    }
                )
                
                if response.status_code == 200:
                    display_results(response.json())
                else:
                    st.error("Failed to analyze ingredients")
            except Exception as e:
                st.error(f"Error: {str(e)}")

def display_results(data):
    # Create three columns for different aspects of the analysis
    col1, col2, col3 = st.columns([2, 2, 1])
    
    with col1:
        st.subheader("Health Score")
        score = data.get('health_score', {}).get('score', 0)
        
        # Create a gauge chart using plotly
        fig = px.pie(
            values=[score, 1000-score],
            names=['Score', 'Remaining'],
            hole=0.7,
            color_discrete_sequence=['#00ff00' if score > 600 else '#ff0000', '#eee']
        )
        fig.update_layout(
            annotations=[dict(text=f"{score}/1000", x=0.5, y=0.5, font_size=20, showarrow=False)],
            showlegend=False,
            width=300,
            height=300
        )
        st.plotly_chart(fig)
        
        # Display explanation
        if 'explanation' in data.get('health_score', {}):
            st.markdown(data['health_score']['explanation'])
    
    with col2:
        st.subheader("Ingredients Analysis")
        if 'ingredients' in data:
            ingredients = data['ingredients']
            st.write(f"Found {len(ingredients)} ingredients:")
            for i, ingredient in enumerate(ingredients, 1):
                st.write(f"{i}. {ingredient}")
        
        # Display risks if available
        if 'risks' in data.get('health_score', {}):
            st.subheader("Risk Categories")
            risks = data['health_score']['risks']
            for category, risk_data in risks.items():
                with st.expander(f"{category.replace('_', ' ').title()}"):
                    st.write(f"Found in: {', '.join(risk_data['ingredients'])}")
    
    with col3:
        if 'nutrition_info' in data:
            st.subheader("Nutrition Info")
            nutrition = data['nutrition_info']
            for nutrient, value in nutrition.items():
                st.metric(nutrient.title(), f"{value}g")
        
        if 'nutrition_analysis' in data:
            analysis = data['nutrition_analysis']
            
            if analysis['concerns']:
                st.subheader("⚠️ Concerns")
                for concern in analysis['concerns']:
                    st.write(f"- {concern}")
            
            if analysis['positives']:
                st.subheader("✅ Positives")
                for positive in analysis['positives']:
                    st.write(f"- {positive}")
            
            if analysis['recommendations']:
                st.subheader("💡 Recommendations")
                for rec in analysis['recommendations']:
                    st.write(f"- {rec}")
    
    # Price comparison if available
    if 'prices' in data:
        st.subheader("Price Comparison")
        prices_df = pd.DataFrame(data['prices'])
        fig = px.bar(
            prices_df,
            x='platform',
            y='price',
            title="Price Comparison Across Platforms",
            color='platform'
        )
        st.plotly_chart(fig)
        
        # Display price table
        st.dataframe(
            prices_df[['platform', 'price', 'title', 'url']],
            column_config={
                "url": st.column_config.LinkColumn("Link")
            }
        )

if __name__ == "__main__":
    main()