Code Editor for app.py

import streamlit as st
import pandas as pd
import requests
import json
from datetime import datetime, timedelta
import time

# NOTE: Using corsproxy.io because we're in a WASM environment. If running locally,
# you can remove the corsproxy.io prefix. Some websites don't work with the proxy,
# in those cases try removing the proxy prefix.

st.set_page_config(
    page_title="Industry Trend Monitor",
    page_icon="📊",
    layout="wide"
)

st.title("📊 Industry Trend Monitor Newsletter")
st.markdown("*Powered by Perplexity & TrendHunter insights*")

# Sidebar for configuration
with st.sidebar:
    st.header("🔧 Configuration")
    
    # Industry selection
    industries = [
        "Technology", "Healthcare", "Finance", "Retail", "Manufacturing",
        "Energy", "Transportation", "Education", "Entertainment", "Real Estate"
    ]
    selected_industry = st.selectbox("Select Industry", industries)
    
    # Time range
    time_ranges = ["Last 7 days", "Last 30 days", "Last 90 days"]
    selected_range = st.selectbox("Time Range", time_ranges)
    
    # Newsletter frequency
    frequency = st.selectbox("Newsletter Frequency", ["Daily", "Weekly", "Monthly"])
    
    st.divider()
    
    # Mock API configuration
    st.subheader("API Configuration")
    perplexity_enabled = st.checkbox("Perplexity API", value=True)
    trendhunter_enabled = st.checkbox("TrendHunter API", value=True)

# Mock data generators
def generate_perplexity_trends(industry, days=7):
    """Generate mock Perplexity trend data"""
    trends = [
        f"AI adoption in {industry.lower()} increases by 45%",
        f"Sustainability initiatives reshape {industry.lower()} landscape",
        f"Remote work technologies transform {industry.lower()} operations",
        f"Data privacy regulations impact {industry.lower()} companies",
        f"Digital transformation accelerates in {industry.lower()} sector"
    ]
    
    data = []
    for i, trend in enumerate(trends[:3]):  # Limit to 3 trends
        data.append({
            "trend": trend,
            "relevance_score": 85 - i*5,
            "source": "Perplexity AI",
            "date": (datetime.now() - timedelta(days=i*2)).strftime("%Y-%m-%d"),
            "category": "Market Analysis"
        })
    return data

def generate_trendhunter_data(industry, days=7):
    """Generate mock TrendHunter data"""
    innovations = [
        f"Emerging {industry.lower()} startups focus on blockchain integration",
        f"Consumer behavior shifts drive {industry.lower()} innovation",
        f"Micro-trends in {industry.lower()} point to future opportunities",
        f"Cross-industry collaborations redefine {industry.lower()} standards",
        f"Gen Z preferences influence {industry.lower()} product development"
    ]
    
    data = []
    for i, innovation in enumerate(innovations[:3]):  # Limit to 3 innovations
        data.append({
            "trend": innovation,
            "innovation_score": 90 - i*3,
            "source": "TrendHunter",
            "date": (datetime.now() - timedelta(days=i*3+1)).strftime("%Y-%m-%d"),
            "category": "Innovation Spotlight"
        })
    return data

def generate_newsletter_content(industry, perplexity_data, trendhunter_data):
    """Generate newsletter content"""
    content = f"""
## 📈 {industry} Industry Trends Newsletter
*Generated on {datetime.now().strftime("%B %d, %Y")}*

### 🔍 Market Intelligence (Perplexity AI)
"""
    
    for item in perplexity_data:
        content += f"""
**{item['trend']}**
- Relevance Score: {item['relevance_score']}/100
- Date: {item['date']}
- Category: {item['category']}
---
"""
    
    content += """
### 💡 Innovation Insights (TrendHunter)
"""
    
    for item in trendhunter_data:
        content += f"""
**{item['trend']}**
- Innovation Score: {item['innovation_score']}/100
- Date: {item['date']}
- Category: {item['category']}
---
"""
    
    return content

# Main content area
col1, col2 = st.columns([2, 1])

with col1:
    st.header(f"📊 {selected_industry} Industry Trends")
    
    if st.button("🔄 Generate Newsletter", type="primary"):
        with st.spinner("Fetching latest trends..."):
            # Simulate API calls with progress
            progress_bar = st.progress(0)
            
            # Mock Perplexity data
            if perplexity_enabled:
                time.sleep(1)
                progress_bar.progress(33)
                perplexity_trends = generate_perplexity_trends(selected_industry)
            else:
                perplexity_trends = []
            
            # Mock TrendHunter data
            if trendhunter_enabled:
                time.sleep(1)
                progress_bar.progress(66)
                trendhunter_trends = generate_trendhunter_data(selected_industry)
            else:
                trendhunter_trends = []
            
            # Generate newsletter
            time.sleep(0.5)
            progress_bar.progress(100)
            
            newsletter_content = generate_newsletter_content(
                selected_industry, perplexity_trends, trendhunter_trends
            )
            
            st.success("Newsletter generated successfully!")
            
            # Display newsletter
            st.markdown(newsletter_content)
            
            # Download button
            st.download_button(
                label="📥 Download Newsletter",
                data=newsletter_content,
                file_name=f"{selected_industry}_trends_{datetime.now().strftime('%Y%m%d')}.md",
                mime="text/markdown"
            )

with col2:
    st.header("📋 Quick Stats")
    
    # Mock statistics
    st.metric("Active Trends", "127", "↑ 12%")
    st.metric("Industry Score", "8.4/10", "↑ 0.3")
    st.metric("Data Sources", "15", "↑ 2")
    
    st.divider()
    
    st.subheader("🎯 Trend Categories")
    categories = ["Technology", "Sustainability", "Consumer Behavior", "Regulation", "Innovation"]
    for cat in categories:
        st.write(f"• {cat}")
    
    st.divider()
    
    st.subheader("⚡ Recent Updates")
    st.write("• New data source integrated")
    st.write("• Algorithm improved")
    st.write("• Mobile app launched")

# Footer
st.divider()
col1, col2, col3 = st.columns(3)

with col1:
    st.write("**Data Sources:**")
    st.write("• Perplexity AI")
    st.write("• TrendHunter")

with col2:
    st.write("**Update Frequency:**")
    st.write(f"• {frequency}")
    st.write(f"• {selected_range}")

with col3:
    st.write("**Export Options:**")
    st.write("• Markdown")
    st.write("• Email (Premium)")

# Sample data table
if st.checkbox("Show Raw Data"):
    st.subheader("📊 Raw Trend Data")
    
    # Combine data for table display
    sample_data = []
    for i in range(10):
        sample_data.append({
            "Date": (datetime.now() - timedelta(days=i)).strftime("%Y-%m-%d"),
            "Industry": selected_industry,
            "Trend": f"Sample trend #{i+1} for {selected_industry.lower()}",
            "Source": "Perplexity" if i % 2 == 0 else "TrendHunter",
            "Score": 95 - i*2,
            "Category": ["Technology", "Market", "Innovation", "Consumer"][i % 4]
        })
    
    df = pd.DataFrame(sample_data)
    st.table(df)