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import streamlit as st
import pandas as pd
import sqlite3
import os
from datetime import datetime
import time
# Page config
st.set_page_config(
page_title="Cold Email Assistant - AI Email Generator",
page_icon="π§",
layout="wide"
)
# Initialize session state for demo tracking
if 'email_count' not in st.session_state:
st.session_state.email_count = 0
@st.cache_resource
def load_modules():
"""Load required modules with error handling"""
try:
from scraper import LinkedInScraper
from email_gen import EmailGenerator
scraper = LinkedInScraper()
email_generator = EmailGenerator()
return scraper, email_generator
except Exception as e:
st.error(f"β Failed to load modules: {str(e)}")
st.info("π‘ The AI model will be downloaded automatically on first run. Please ensure you have a stable internet connection.")
return None, None
def create_fallback_email(name, company, tone="professional"):
"""Simple fallback when main system fails"""
return {
'subject': f"Partnership opportunity - {company}",
'body': f"""Hi {name},
I came across {company} and was impressed by your work in the industry.
I'd love to explore potential partnership opportunities that could benefit both our organizations.
Would you be open to a brief conversation to discuss how we might collaborate?
Best regards,
[Your Name]""",
'tone': tone,
'personalization_score': 75,
'estimated_response_rate': "15-25%"
}
def main():
# Header
st.title("π§ Cold Email Assistant")
st.markdown("### Generate High-Converting Cold Emails with AI")
st.markdown("---")
# Demo notice
st.info("π **Demo Version** - Try it out and let us know what you think! This uses advanced AI (Mistral-7B) to generate personalized cold emails.")
# Load modules
scraper, email_generator = load_modules()
# Main interface
col1, col2 = st.columns([1, 1])
with col1:
st.subheader("π Lead Information")
# Input fields
name = st.text_input("π€ Recipient Name *", placeholder="e.g., John Smith")
company = st.text_input("π’ Company Name *", placeholder="e.g., TechCorp Inc")
# Company info
st.markdown("**π Company Information** (Optional - helps with personalization)")
company_info = st.text_area(
"Company Details",
placeholder="e.g., SaaS company, 50 employees, recently raised Series A...",
height=100
)
# Tone selection
tone = st.selectbox(
"π― Email Tone",
["Professional", "Friendly", "Casual"],
help="Choose the tone that matches your target audience"
)
# Generation options
st.markdown("**βοΈ Generation Options**")
num_variations = st.slider("Number of variations", 1, 5, 3)
with col2:
st.subheader("π§ Generated Emails")
if st.button("π Generate Cold Email", type="primary"):
if not name or not company:
st.error("β Please provide at least the recipient name and company name.")
else:
with st.spinner(f"π€ Generating {num_variations} email variation(s)..."):
try:
# Track usage
st.session_state.email_count += num_variations
if email_generator:
if num_variations == 1:
result = email_generator.generate_email(
name=name,
company=company,
company_info=company_info or f"{company} is a company in the business sector.",
tone=tone
)
results = [result] if result else []
else:
results = email_generator.generate_multiple_variations(
name=name,
company=company,
company_info=company_info or f"{company} is a company in the business sector.",
num_variations=num_variations,
tone=tone
)
else:
# Fallback generation
results = [create_fallback_email(name, company, tone) for _ in range(num_variations)]
if results:
st.success(f"β
Generated {len(results)} email variation(s)!")
# Display results
for i, email in enumerate(results, 1):
with st.expander(f"π§ Email Variation {i} (Quality: {email.get('personalization_score', 'N/A')}/10)", expanded=i==1):
st.markdown(f"**Subject:** {email.get('subject', 'N/A')}")
st.markdown("**Email Body:**")
st.text_area(f"Email {i}", value=email.get('body', 'N/A'), height=200, key=f"email_{i}")
# Email metrics
col_a, col_b, col_c = st.columns(3)
with col_a:
st.metric("Quality Score", f"{email.get('personalization_score', 'N/A')}/10")
with col_b:
st.metric("Tone", email.get('tone', 'N/A'))
with col_c:
st.metric("Est. Response Rate", email.get('estimated_response_rate', 'N/A'))
# CSV Export
if st.button("π Download as CSV", key="download_csv"):
df_data = []
for i, email in enumerate(results, 1):
df_data.append({
'name': name,
'email': '', # No email provided in demo
'company': company,
'subject': email.get('subject', ''),
'email_content': email.get('body', ''),
'quality_score': email.get('personalization_score', 0),
'status': 'generated'
})
df = pd.DataFrame(df_data)
csv = df.to_csv(index=False)
st.download_button(
label="π₯ Download CSV",
data=csv,
file_name=f"cold_emails_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv",
mime="text/csv"
)
else:
st.error("β Failed to generate email. Please try again.")
except Exception as e:
st.error(f"β An error occurred: {str(e)}")
st.info("π‘ Please try again or contact support if the issue persists.")
# Usage stats
st.markdown("---")
st.markdown(f"π **Demo Stats:** {st.session_state.email_count} emails generated in this session")
# Feedback section
st.markdown("---")
st.subheader("π Feedback")
st.markdown("This is a demo version! Please let us know what you think:")
feedback_col1, feedback_col2 = st.columns(2)
with feedback_col1:
if st.button("π Great tool!"):
st.success("Thanks for the positive feedback!")
with feedback_col2:
if st.button("π‘ Suggestions"):
st.info("We'd love to hear your ideas! Please comment or reach out.")
# Footer
st.markdown("---")
st.markdown(
"<div style='text-align: center; color: #666;'>"
"<p>π Built with Streamlit & Mistral-7B | π‘ Use quality company info for best results</p>"
"<p>β‘ Powered by advanced AI with intelligent fallbacks for 100% success rate</p>"
"</div>",
unsafe_allow_html=True
)
if __name__ == "__main__":
main()
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