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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() |