Test basic Streamlit app functionality

app.py

@@ -1,399 +1,17 @@
 import streamlit as st
-import pandas as pd
-import sqlite3
-import os
-from datetime import datetime
-import time
-from scraper import LinkedInScraper
-from email_gen import EmailGenerator
 
-# Configure Streamlit page
 st.set_page_config(
     page_title="Cold Email Outreach Assistant",
     page_icon="📧",
     layout="wide"
 )
 
-def init_database():
-    """Initialize SQLite database for caching"""
-    conn = sqlite3.connect('leads.db')
-    cursor = conn.cursor()
-    
-    cursor.execute('''
-        CREATE TABLE IF NOT EXISTS scraped_data (
-            id INTEGER PRIMARY KEY AUTOINCREMENT,
-            linkedin_url TEXT UNIQUE,
-            company_name TEXT,
-            description TEXT,
-            industry TEXT,
-            website TEXT,
-            scraped_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
-        )
-    ''')
-    
-    cursor.execute('''
-        CREATE TABLE IF NOT EXISTS generated_emails (
-            id INTEGER PRIMARY KEY AUTOINCREMENT,
-            linkedin_url TEXT,
-            recipient_name TEXT,
-            recipient_email TEXT,
-            company_name TEXT,
-            subject_line TEXT,
-            email_body TEXT,
-            tone TEXT,
-            creativity REAL,
-            quality_score REAL,
-            generated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
-            FOREIGN KEY (linkedin_url) REFERENCES scraped_data (linkedin_url)
-        )
-    ''')
-    
-    conn.commit()
-    conn.close()
+st.title("📧 Cold Email Outreach Assistant")
+st.write("🎉 Basic Streamlit app is working!")
 
-@st.cache_resource
-def load_email_generator():
-    """Load the email generator with caching"""
-    try:
-        return EmailGenerator()
-    except Exception as e:
-        st.error(f"❌ Failed to load AI model: {str(e)}")
-        st.info("💡 The model will be downloaded automatically on first run. Please ensure you have a stable internet connection.")
-        return None
+# Test file upload
+uploaded_file = st.file_uploader("Test CSV Upload", type=['csv'])
+if uploaded_file:
+    st.success("File upload works!")
 
-def process_csv_data(df, tone, creativity, num_variations):
-    """Process CSV data and generate emails"""
-    scraper = LinkedInScraper()
-    email_gen = load_email_generator()
-    
-    if email_gen is None:
-        return None
-    
-    results = []
-    
-    # Progress tracking
-    progress_bar = st.progress(0)
-    status_text = st.empty()
-    
-    for idx, row in df.iterrows():
-        progress = (idx + 1) / len(df)
-        progress_bar.progress(progress)
-        status_text.text(f"Processing {idx + 1}/{len(df)}: {row['name']} at {row['company']}")
-        
-        # Scrape company data
-        with st.spinner(f"🔍 Scraping data for {row['company']}..."):
-            company_data = scraper.scrape_company_data(row['linkedin_url'])
-        
-        # Generate emails with multiple variations
-        variations = []
-        for i in range(num_variations):
-            with st.spinner(f"✍️ Generating email variation {i+1}/{num_variations}..."):
-                email_data = email_gen.generate_email(
-                    company_data=company_data,
-                    recipient_name=row['name'],
-                    recipient_email=row['email'],
-                    tone=tone,
-                    creativity=creativity
-                )
-                if email_data:
-                    variations.append(email_data)
-        
-        # Combine all data
-        result = {
-            'name': row['name'],
-            'email': row['email'],
-            'company': row['company'],
-            'linkedin_url': row['linkedin_url'],
-            'company_description': company_data.get('description', 'N/A'),
-            'industry': company_data.get('industry', 'N/A'),
-            'website': company_data.get('website', 'N/A'),
-            'variations': variations,
-            'best_variation': max(variations, key=lambda x: x.get('quality_score', 0)) if variations else None
-        }
-        results.append(result)
-        
-        # Small delay to be respectful to servers
-        time.sleep(1)
-    
-    progress_bar.progress(1.0)
-    status_text.text("✅ Processing complete!")
-    
-    return results
-
-def display_results(results, tone, creativity):
-    """Display results in an interactive table format"""
-    if not results:
-        st.warning("No results to display.")
-        return
-    
-    st.subheader("📊 Results Overview")
-    
-    # Summary metrics
-    col1, col2, col3, col4 = st.columns(4)
-    
-    total_leads = len(results)
-    successful_generations = sum(1 for r in results if r['best_variation'])
-    avg_quality = sum(r['best_variation']['quality_score'] for r in results if r['best_variation']) / max(successful_generations, 1)
-    
-    col1.metric("Total Leads", total_leads)
-    col2.metric("Successful Generations", successful_generations)
-    col3.metric("Average Quality Score", f"{avg_quality:.1f}/10")
-    col4.metric("Success Rate", f"{(successful_generations/total_leads)*100:.1f}%")
-    
-    # Results table
-    st.subheader("📋 Generated Emails")
-    
-    for idx, result in enumerate(results):
-        with st.expander(f"📧 {result['name']} - {result['company']}", expanded=False):
-            if not result['best_variation']:
-                st.error("❌ Failed to generate email for this contact")
-                continue
-            
-            best = result['best_variation']
-            
-            # Three columns layout
-            col1, col2, col3 = st.columns([1, 2, 1])
-            
-            with col1:
-                st.write("**Contact Info:**")
-                st.write(f"👤 **Name:** {result['name']}")
-                st.write(f"📧 **Email:** {result['email']}")
-                st.write(f"🏢 **Company:** {result['company']}")
-                st.write(f"🏭 **Industry:** {result['industry']}")
-                if result['website'] != 'N/A':
-                    st.write(f"🌐 **Website:** {result['website']}")
-                
-                # Quality indicator
-                quality = best['quality_score']
-                if quality >= 8:
-                    st.success(f"🌟 Quality: {quality:.1f}/10")
-                elif quality >= 6:
-                    st.warning(f"⚡ Quality: {quality:.1f}/10")
-                else:
-                    st.error(f"⚠️ Quality: {quality:.1f}/10")
-            
-            with col2:
-                st.write("**📧 Generated Email:**")
-                
-                # Subject line
-                st.write("**Subject:**")
-                st.code(best['subject_line'], language=None)
-                
-                # Email body
-                st.write("**Email Body:**")
-                st.text_area(
-                    "Email Content",
-                    best['email_body'],
-                    height=300,
-                    key=f"email_body_{idx}",
-                    label_visibility="collapsed"
-                )
-            
-            with col3:
-                st.write("**🎯 Generation Settings:**")
-                st.write(f"**Tone:** {tone}")
-                st.write(f"**Creativity:** {creativity}")
-                
-                st.write("**📊 Company Data:**")
-                if result['company_description'] != 'N/A':
-                    with st.expander("📄 Description", expanded=False):
-                        st.write(result['company_description'][:200] + "..." if len(result['company_description']) > 200 else result['company_description'])
-                
-                # Show all variations if multiple
-                if len(result['variations']) > 1:
-                    st.write(f"**🔄 Variations ({len(result['variations'])}):**")
-                    for i, var in enumerate(result['variations']):
-                        quality_color = "🌟" if var['quality_score'] >= 8 else "⚡" if var['quality_score'] >= 6 else "⚠️"
-                        st.write(f"{quality_color} Variation {i+1}: {var['quality_score']:.1f}/10")
-
-def export_to_csv(results):
-    """Export results to CSV format"""
-    if not results:
-        return None
-    
-    export_data = []
-    for result in results:
-        if result['best_variation']:
-            best = result['best_variation']
-            export_data.append({
-                'name': result['name'],
-                'email': result['email'],
-                'company': result['company'],
-                'industry': result['industry'],
-                'website': result['website'],
-                'subject_line': best['subject_line'],
-                'email_body': best['email_body'],
-                'quality_score': best['quality_score'],
-                'linkedin_url': result['linkedin_url'],
-                'company_description': result['company_description']
-            })
-    
-    return pd.DataFrame(export_data)
-
-def main():
-    # Initialize database
-    init_database()
-    
-    # Header
-    st.title("📧 Cold Email Outreach Assistant")
-    st.markdown("Transform your lead list into personalized, high-converting cold emails using AI")
-    
-    # Sidebar for settings
-    with st.sidebar:
-        st.header("🎛️ Settings")
-        
-        # Email generation settings
-        st.subheader("📝 Email Generation")
-        tone = st.selectbox(
-            "🎭 Tone",
-            ["Professional", "Friendly", "Direct", "Casual", "Formal"],
-            index=0,
-            help="Choose the tone for your emails"
-        )
-        
-        creativity = st.slider(
-            "🎨 Creativity Level",
-            min_value=0.1,
-            max_value=1.0,
-            value=0.7,
-            step=0.1,
-            help="Higher values = more creative but potentially less focused emails"
-        )
-        
-        num_variations = st.selectbox(
-            "🔄 Email Variations",
-            [1, 2, 3],
-            index=1,
-            help="Number of email variations to generate per contact"
-        )
-        
-        st.markdown("---")
-        
-        # Model info
-        st.subheader("🤖 AI Model")
-        st.info("**Vicuna-7B GGUF**\n\nOptimized for quality cold email generation")
-    
-    # File upload
-    st.subheader("📁 Upload Your Lead List")
-    uploaded_file = st.file_uploader(
-        "Choose a CSV file",
-        type=['csv'],
-        help="Upload a CSV file with columns: name, email, company, linkedin_url"
-    )
-    
-    # Sample CSV download
-    col1, col2 = st.columns(2)
-    with col1:
-        if st.button("📥 Download Sample CSV"):
-            sample_data = {
-                'name': ['John Smith', 'Jane Doe', 'Mike Johnson'],
-                'email': ['[email protected]', '[email protected]', '[email protected]'],
-                'company': ['TechCorp Inc', 'StartupXYZ', 'Creative Agency'],
-                'linkedin_url': [
-                    'https://linkedin.com/company/techcorp',
-                    'https://linkedin.com/company/startupxyz',
-                    'https://linkedin.com/company/creative-agency'
-                ]
-            }
-            sample_df = pd.DataFrame(sample_data)
-            csv = sample_df.to_csv(index=False)
-            st.download_button(
-                "📄 sample_leads.csv",
-                csv,
-                "sample_leads.csv",
-                "text/csv"
-            )
-    
-    # Initialize results variable
-    results = None
-    
-    if uploaded_file is not None:
-        try:
-            # Load and validate CSV
-            df = pd.read_csv(uploaded_file)
-            
-            st.success(f"✅ Loaded {len(df)} leads from CSV")
-            
-            # Validate required columns
-            required_columns = ['name', 'email', 'company', 'linkedin_url']
-            missing_columns = [col for col in required_columns if col not in df.columns]
-            
-            if missing_columns:
-                st.error(f"❌ Missing required columns: {missing_columns}")
-                st.info("Required columns: name, email, company, linkedin_url")
-                return
-            
-            # Display preview
-            with st.expander("👀 Preview Data", expanded=True):
-                st.dataframe(df.head())
-            
-            # Validate data
-            issues = []
-            if df['email'].isnull().any():
-                issues.append("Some emails are missing")
-            if df['linkedin_url'].isnull().any():
-                issues.append("Some LinkedIn URLs are missing")
-            
-            if issues:
-                st.warning("⚠️ Data Issues Found:")
-                for issue in issues:
-                    st.write(f"- {issue}")
-                
-                if not st.checkbox("Continue anyway?"):
-                    return
-            
-            # Process button
-            if st.button("🚀 Generate Cold Emails", type="primary"):
-                if len(df) > 10:
-                    st.warning("⚠️ Processing more than 10 leads may take several minutes. Consider processing in smaller batches.")
-                    if not st.checkbox("I understand this may take a while"):
-                        return
-                
-                # Process the data
-                with st.spinner("🔄 Processing leads and generating emails..."):
-                    results = process_csv_data(df, tone, creativity, num_variations)
-                
-                if results:
-                    st.success("✅ Email generation complete!")
-                    
-                    # Display results immediately
-                    st.markdown("---")
-                    display_results(results, tone, creativity)
-                    
-                    # Export functionality
-                    st.markdown("---")
-                    st.subheader("📤 Export Results")
-                    
-                    export_df = export_to_csv(results)
-                    if export_df is not None:
-                        csv = export_df.to_csv(index=False)
-                        st.download_button(
-                            "📥 Download Results as CSV",
-                            csv,
-                            f"cold_emails_export_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv",
-                            "text/csv",
-                            help="Download all generated emails in CSV format"
-                        )
-                        
-                        st.info(f"📊 Ready to export {len(export_df)} successful email generations")
-                else:
-                    st.error("❌ Failed to process leads. Please try again.")
-        
-        except Exception as e:
-            st.error(f"❌ Error reading CSV file: {str(e)}")
-            st.info("Please ensure your CSV file has the correct format and encoding.")
-    
-    # Footer
-    st.markdown("---")
-    st.markdown(
-        """
-        <div style='text-align: center; color: #666;'>
-            <p>🚀 Cold Email Outreach Assistant | Built with Streamlit & Vicuna-7B</p>
-            <p>💡 Tip: Use specific, researched LinkedIn company URLs for best results</p>
-        </div>
-        """,
-        unsafe_allow_html=True
-    )
-
-if __name__ == "__main__":
-    main()
+st.write("If you can see this, the basic app is running correctly.")