Projects using Streamlit in LahoreProjects using Streamlit in Lahore
Cover image for Taking corporate email automation beyond
Taking corporate email automation beyond basic template generation. 🚀 I just open-sourced a new project: Autonomous Cold Email & Inbound Replier Agent! This is a modular, production-ready system built completely in pure Python using CrewAI for multi-agent orchestration and Streamlit for a clean, modern interactive UI. As shown in image_ea414f.png, the system moves away from single-prompt generation and splits the operational workload into specialized, sequential steps to ensure high-quality enterprise triage: Dynamic Context Parsing: Ingests dynamic company names, specific roles, and product offerings to craft completely personalized pitch assets based on an internal knowledge base. Multi-Agent Orchestration: Uses a CrewAI sequential flow to separate analysis from execution. A Triage Officer handles data categorization while a Business Correspondent handles context-aware drafting. 100% Free-Tier & Cost Compliance: Powered entirely by high-performance models via free-tier API orchestration (utilizing gemini-2.5-flash via Google AI Studio and Groq Cloud AI as a high-speed secondary model). Interactive UI Safety: Features a polished Streamlit interface (onee.py (http://onee.py)) to monitor active server queues and act as a human-in-the-loop gatekeeper to approve drafts before they go live. The repository is modularly structured, completely production-ready, and optimized with strict exception handling to respect rate limits safely. 🔗 Check out the code here: https://github.com/26FajarRizwan/Cold_Email_Replier_Agent #CrewAI #Streamlit #AIEngineering #Python #GenerativeAI #LLMs #OpenSource #Automation #GoogleGemini
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Cover image for AI-Powered Resume Screening System
Tired of
AI-Powered Resume Screening System Tired of manually sifting through hundreds of resumes? This intelligent screening system does the heavy lifting — automatically parsing resumes, extracting key skills, and semantically matching candidates to job descriptions in seconds. 🧠 How It Works The system uses advanced Natural Language Processing (NLP) to deeply understand both resumes and job descriptions — going far beyond simple keyword matching. It calculates semantic similarity using cosine similarity, meaning it understands context, not just words. ⚙️ Key Features 📄 Smart Resume Parsing — Automatically extracts skills, experience, and qualifications from any resume format 🔍 Semantic Job Matching — Matches candidates to roles based on meaning, not just keywords 🏆 Candidate Ranking — Instantly ranks applicants by relevance score 📊 Match Scoring — Clear percentage-based compatibility scores for every candidate 🕳️ Skill Gap Analysis — Identifies exactly what skills a candidate is missing for a role 🚀 Streamlit Dashboard — Clean, interactive UI deployable in one click 🛠️ Tech Stack Python · NLP · Scikit-learn · Cosine Similarity · Streamlit · SpaCy / NLTK 💼 Perfect For HR teams, recruitment agencies, startups, and any business drowning in job applications — this tool cuts screening time by up to 80%. 📈 Results It Delivers ✅ Faster hiring decisions ✅ Bias-reduced candidate evaluation ✅ Clear, data-backed shortlisting ✅ Scalable to thousands of resumes
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