Cold_email_generator/Project_working.html at main · ShriyashP/Cold_email_generator · GitHub

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<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Cold Email Generator - Demo</title>
    <style>
        * {
            margin: 0;
            padding: 0;
            box-sizing: border-box;
        }

        body {
            font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
            line-height: 1.6;
            color: #333;
            background: #ced0d1;
        }

        .container {
            max-width: 1200px;
            margin: 0 auto;
            padding: 20px;
        }

        .header {
            text-align: center;
            margin-bottom: 40px;
            padding: 40px 0;
            background: linear-gradient(135deg, #f53434 0%, #aa6ea8 100%);
            color: white;
            border-radius: 12px;
        }

        .header h1 {
            font-size: 2.5rem;
            margin-bottom: 10px;
        }

        .header p {
            font-size: 1.2rem;
            opacity: 0.9;
        }

        .section {
            background: white;
            margin: 30px 0;
            padding: 30px;
            border-radius: 12px;
            box-shadow: 0 2px 10px rgba(0,0,0,0.1);
        }

        .section h2 {
            color: #2c3e50;
            margin-bottom: 20px;
            font-size: 1.8rem;
            border-bottom: 3px solid #3498db;
            padding-bottom: 10px;
        }

        .workflow {
            display: grid;
            grid-template-columns: repeat(auto-fit, minmax(300px, 1fr));
            gap: 20px;
            margin: 20px 0;
        }

        .workflow-step {
            background: #f8f9fa;
            padding: 20px;
            border-radius: 8px;
            border-left: 4px solid #3498db;
            position: relative;
        }

        .workflow-step::before {
            content: attr(data-step);
            position: absolute;
            top: -10px;
            left: -10px;
            background: #3498db;
            color: white;
            width: 30px;
            height: 30px;
            border-radius: 50%;
            display: flex;
            align-items: center;
            justify-content: center;
            font-weight: bold;
            font-size: 14px;
        }

        .workflow-step h3 {
            margin-bottom: 10px;
            color: #2c3e50;
        }

        .tech-stack {
            display: flex;
            flex-wrap: wrap;
            gap: 10px;
            margin: 20px 0;
        }

        .tech-badge {
            background: #e3f2fd;
            color: #1976d2;
            padding: 8px 16px;
            border-radius: 20px;
            font-size: 14px;
            font-weight: 500;
        }

        .demo-interface {
            background: #f8f9fa;
            border: 2px dashed #ddd;
            border-radius: 8px;
            padding: 20px;
            margin: 20px 0;
            text-align: center;
        }

        .input-demo {
            background: white;
            border: 1px solid #ddd;
            border-radius: 4px;
            padding: 12px;
            margin: 10px;
            width: 300px;
            max-width: 100%;
        }

        .btn-demo {
            background: #3498db;
            color: white;
            border: none;
            padding: 12px 24px;
            border-radius: 4px;
            cursor: pointer;
            font-size: 16px;
            margin: 10px;
        }

        .btn-demo:hover {
            background: #2980b9;
        }

        .email-output {
            background: #fff;
            border: 1px solid #ddd;
            border-radius: 8px;
            padding: 20px;
            margin: 20px 0;
            font-family: 'Courier New', monospace;
            white-space: pre-line;
        }

        .process-flow {
            display: flex;
            align-items: center;
            justify-content: space-between;
            margin: 20px 0;
            flex-wrap: wrap;
        }

        .process-step {
            background: #3498db;
            color: white;
            padding: 15px 20px;
            border-radius: 8px;
            text-align: center;
            flex: 1;
            margin: 5px;
            min-width: 150px;
        }

        .arrow {
            font-size: 24px;
            color: #3498db;
            margin: 0 10px;
        }

        .code-block {
            background: #2d3748;
            color: #e2e8f0;
            padding: 20px;
            border-radius: 8px;
            overflow-x: auto;
            font-family: 'Courier New', monospace;
            font-size: 14px;
            margin: 15px 0;
        }

        .highlight {
            background: #fff3cd;
            padding: 15px;
            border-left: 4px solid #ffc107;
            margin: 15px 0;
            border-radius: 4px;
        }

        @media (max-width: 768px) {
            .header h1 {
                font-size: 2rem;
            }

            .process-flow {
                flex-direction: column;
            }

            .arrow {
                transform: rotate(90deg);
            }
        }
    </style>
</head>
<body>
    <div class="container">
        <!-- Header -->
        <div class="header">
            <h1>Cold Email Generator</h1>
            <p>AI-Powered Business Outreach for Service Companies</p>
        </div>

        <!-- Project Overview -->
        <div class="section">
            <h2>🎯 Project Overview</h2>
            <p>The Cold Email Generator is an intelligent tool designed specifically for service companies to automate and personalize their business outreach efforts. By leveraging advanced AI technologies, it extracts job listings from company career pages and generates highly targeted cold emails that match specific job requirements with relevant service offerings.</p>

            <div class="tech-stack">
                <span class="tech-badge">Llama 3.1 LLM</span>
                <span class="tech-badge">Groq</span>
                <span class="tech-badge">LangChain</span>
                <span class="tech-badge">Streamlit</span>
                <span class="tech-badge">ChromaDB</span>
                <span class="tech-badge">Python</span>
            </div>
        </div>

        <!-- How It Works -->
        <div class="section">
            <h2>⚡ How It Works</h2>

            <div class="demo-interface">
                <h3>Live Demo Interface</h3>
                <input type="text" class="input-demo" placeholder="Enter company career page URL" value="https://jobs.nike.com/careers">
                <br>
                <button class="btn-demo" onclick="simulateProcess()">Extract Jobs & Generate Emails</button>
                <div id="demo-status" style="margin-top: 15px; font-style: italic; color: #666;"></div>
            </div>

            <div class="process-flow">
                <div class="process-step">URL Input</div>
                <div class="arrow">→</div>
                <div class="process-step">Web Scraping</div>
                <div class="arrow">→</div>
                <div class="process-step">Job Extraction</div>
                <div class="arrow">→</div>
                <div class="process-step">Resume Matching</div>
                <div class="arrow">→</div>
                <div class="process-step">Email Generation</div>
            </div>
        </div>

        <!-- Technical Workflow -->
        <div class="section">
            <h2>🔧 Technical Workflow Deep Dive</h2>

            <div class="workflow">
                <div class="workflow-step" data-step="1">
                    <h3>Data Extraction Layer</h3>
                    <p><strong>Web Scraping Engine:</strong> Uses BeautifulSoup4 and Selenium to extract job postings from career pages. Handles dynamic content loading and JavaScript-rendered pages.</p>
                    <div class="code-block">
# Web scraping implementation
from selenium import webdriver
from bs4 import BeautifulSoup
import requests

def extract_jobs(url):
    driver = webdriver.Chrome()
    driver.get(url)
    html = driver.page_source
    soup = BeautifulSoup(html, 'html.parser')
    return parse_job_listings(soup)</div>
                </div>

                <div class="workflow-step" data-step="2">
                    <h3>Job Parsing & Structuring</h3>
                    <p><strong>LangChain Integration:</strong> Processes raw HTML content to extract structured job data including titles, requirements, descriptions, and company information.</p>
                    <div class="code-block">
# Job parsing with LangChain
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.schema import Document

def structure_job_data(raw_jobs):
    structured_jobs = []
    for job in raw_jobs:
        job_doc = Document(
            page_content=job['description'],
            metadata={
                'title': job['title'],
                'requirements': job['requirements'],
                'company': job['company']
            }
        )
        structured_jobs.append(job_doc)
    return structured_jobs</div>
                </div>

                <div class="workflow-step" data-step="3">
                    <h3>Vector Database Storage</h3>
                    <p><strong>ChromaDB Integration:</strong> Converts job descriptions into embeddings and stores them in a vector database for semantic similarity matching.</p>
                    <div class="code-block">
# ChromaDB vector storage
import chromadb
from langchain.vectorstores import Chroma
from langchain.embeddings import HuggingFaceEmbeddings

def store_in_vectordb(job_documents):
    embeddings = HuggingFaceEmbeddings()
    vectorstore = Chroma.from_documents(
        documents=job_documents,
        embedding=embeddings,
        persist_directory="./job_vectors"
    )
    return vectorstore</div>
                </div>

                <div class="workflow-step" data-step="4">
                    <h3>Resume Portfolio Matching</h3>
                    <p><strong>Semantic Search:</strong> Matches job requirements with relevant portfolios/resumes using cosine similarity. Identifies best-fit candidates for each position.</p>
                    <div class="code-block">
# Resume matching algorithm
def match_resumes_to_jobs(job_embeddings, resume_portfolio):
    matches = []
    for job in job_embeddings:
        similar_resumes = vectorstore.similarity_search(
            job.page_content,
            k=3,
            filter={'skills': job.metadata['requirements']}
        )
        matches.append({
            'job': job,
            'matched_resumes': similar_resumes
        })
    return matches</div>
                </div>

                <div class="workflow-step" data-step="5">
                    <h3>AI Email Generation</h3>
                    <p><strong>Llama 3.1 via Groq:</strong> Generates personalized cold emails using the matched job-resume pairs. Creates contextually relevant content with specific value propositions.</p>
                    <div class="code-block">
# Email generation with Llama 3.1
from groq import Groq

def generate_cold_email(job_match, company_info):
    client = Groq(api_key="your-groq-api-key")
    prompt = f"""
    Generate a professional cold email for:
    Job: {job_match['job'].metadata['title']}
    Company: {company_info['name']}
    Matched Resume: {job_match['matched_resumes'][0]}

    Focus on specific value propositions and relevance.
    """

    response = client.chat.completions.create(
        messages=[{"role": "user", "content": prompt}],
        model="llama3-70b-8192"
    )
    return response.choices[0].message.content</div>
                </div>

                <div class="workflow-step" data-step="6">
                    <h3>Output Delivery</h3>
                    <p><strong>Streamlit Interface:</strong> Presents generated emails in an intuitive web interface with editing capabilities, export options, and campaign management features.</p>
                    <div class="code-block">
# Streamlit interface
import streamlit as st

def display_generated_emails(emails):
    st.title("Generated Cold Emails")
    for i, email in enumerate(emails):
        with st.expander(f"Email {i+1}: {email['subject']}"):
            st.text_area("Email Content", email['content'])
            if st.button(f"Export Email {i+1}"):
                export_email(email)</div>
                </div>
            </div>
        </div>

        <!-- Complete Example -->
        <div class="section">
            <h2>📧 Complete Example</h2>

            <div class="highlight">
                <strong>Scenario:</strong> A web development agency wants to reach out to Nike for their Senior Frontend Developer position
            </div>

            <h3>Input Data:</h3>
            <div class="code-block">
Job Title: Senior Frontend Developer
Company: Nike
Requirements: React, TypeScript, 5+ years experience, E-commerce
Location: Portland, OR
Posted: 2 days ago

Matched Resume: John Smith - Frontend Specialist
- 6 years React development
- E-commerce platform expertise
- TypeScript proficiency
- Portfolio: 15+ successful projects</div>

            <h3>Generated Cold Email:</h3>
            <div class="email-output">Subject: Enhancing Nike's Frontend Development with Proven E-commerce Expertise

Dear Nike Hiring Team,

I noticed your recent posting for a Senior Frontend Developer position and wanted to reach out regarding our specialized frontend development services.

Our team has extensive experience in the exact technologies you're seeking:

• **React & TypeScript Mastery**: Our lead developer, John Smith, brings 6+ years of React development experience with deep TypeScript expertise
• **E-commerce Specialization**: We've successfully delivered 15+ e-commerce platforms, understanding the unique challenges of high-traffic retail environments
• **Performance Focus**: Our solutions consistently achieve 95+ PageSpeed scores, crucial for conversion optimization

Given Nike's position as a global e-commerce leader, we understand the critical importance of:
- Seamless user experiences across devices
- Fast loading times for product catalogs
- Scalable architecture for traffic spikes during launches

**Value Proposition**: Instead of the typical 3-6 month hiring and onboarding process, we can provide immediate frontend development support while you find the perfect full-time candidate.

**Recent Success**: We recently helped [Similar Company] reduce their product page load time by 40%, resulting in a 15% increase in conversions.

Would you be open to a brief 15-minute call to discuss how our frontend expertise could support Nike's immediate development needs?

Best regards,
[Your Name]
[Company Name]
[Contact Information]

P.S. I've attached our portfolio showcasing similar e-commerce projects that might interest your team.</div>

            <h3>Email Analytics:</h3>
            <div class="code-block">
Personalization Score: 92/100
Relevance Match: 89/100
Professional Tone: 95/100
Call-to-Action Strength: 88/100

Key Matching Elements:
✓ Technology stack alignment (React, TypeScript)
✓ Industry experience (E-commerce)
✓ Seniority level match (6+ years vs 5+ required)
✓ Location consideration (Remote-friendly)
✓ Portfolio relevance (15+ projects)</div>
        </div>

        <!-- Technical Architecture -->
        <div class="section">
            <h2>🏗️ System Architecture</h2>

            <div class="process-flow">
                <div class="process-step">Streamlit Frontend</div>
                <div class="arrow">↕</div>
                <div class="process-step">LangChain Processing</div>
                <div class="arrow">↕</div>
                <div class="process-step">ChromaDB Vector Store</div>
                <div class="arrow">↕</div>
                <div class="process-step">Groq + Llama 3.1</div>
            </div>

            <h3>Data Flow:</h3>
            <div class="workflow">
                <div class="workflow-step" data-step="A">
                    <h3>Input Processing</h3>
                    <p>Career page URLs → Web scraper → Raw HTML → Job parser → Structured data</p>
                </div>
                <div class="workflow-step" data-step="B">
                    <h3>Vector Processing</h3>
                    <p>Job descriptions → Embeddings → ChromaDB storage → Similarity search → Resume matching</p>
                </div>
                <div class="workflow-step" data-step="C">
                    <h3>Email Generation</h3>
                    <p>Matched pairs → Prompt engineering → Llama 3.1 → Generated emails → Quality scoring</p>
                </div>
                <div class="workflow-step" data-step="D">
                    <h3>Output Delivery</h3>
                    <p>Email content → Streamlit interface → User review → Export/Send → Campaign tracking</p>
                </div>
            </div>
        </div>

        <!-- Key Features -->
        <div class="section">
            <h2>🚀 Key Features</h2>
            <div class="workflow">
                <div class="workflow-step" data-step="⚡">
                    <h3>Intelligent Matching</h3>
                    <p>Uses vector similarity to match job requirements with portfolio skills, ensuring highly relevant email content and improved response rates.</p>
                </div>
                <div class="workflow-step" data-step="🎯">
                    <h3>Dynamic Personalization</h3>
                    <p>Each email is uniquely crafted based on specific job requirements, company culture, and matched candidate profiles.</p>
                </div>
                <div class="workflow-step" data-step="⏱️">
                    <h3>Batch Processing</h3>
                    <p>Process multiple job listings simultaneously, generating dozens of targeted emails in minutes rather than hours.</p>
                </div>
                <div class="workflow-step" data-step="📊">
                    <h3>Quality Scoring</h3>
                    <p>Built-in analytics rate email quality, relevance, and personalization level to optimize outreach effectiveness.</p>
                </div>
            </div>
        </div>
    </div>

    <script>
        function simulateProcess() {
            const statusDiv = document.getElementById('demo-status');
            const steps = [
                'Connecting to career page...',
                'Extracting job listings...',
                'Processing job descriptions...',
                'Matching with resume portfolio...',
                'Generating personalized emails...',
                '✅ Generated 12 targeted cold emails!'
            ];

            let currentStep = 0;
            const interval = setInterval(() => {
                statusDiv.textContent = steps[currentStep];
                statusDiv.style.color = currentStep === steps.length - 1 ? '#28a745' : '#007bff';
                currentStep++;

                if (currentStep >= steps.length) {
                    clearInterval(interval);
                }
            }, 1500);
        }
    </script>
</body>
</html>