Projects using Streamlit in LahoreProjects using Streamlit in Lahore
Cover image for Built a personalized LinkedIn post
Built a personalized LinkedIn post generator, and it's actually working end to end now. Most AI writing tools just take a prompt and spit out generic content. I wanted something different, a tool that understands who you are before it writes anything, and checks what's genuinely working on a topic right now instead of guessing. Here's what it actually does: → Personalized profile setup — set your niche, profession, key skills, and preferred tone once, it's reused for every post after that → Real-time research before writing — searches how people are actually discussing your topic right now, and extracts real patterns: hook style, structure, tone, without ever copying anyone's actual words → Fully personalized generation — combines your profile with the research patterns to write something original that sounds like you, not a generic AI voice → Editable output — every generated post is editable before you copy it, plus you get a breakdown of exactly which patterns informed the draft → Post history — every post you've generated is saved and browsable anytime → Fully self-contained build — Python, Streamlit, SQLite, no Supabase or external auth provider, just OpenRouter for generation and Serper for real-time research, with proper password hashing for authentication Still early, and currently invite-only while I manage access and costs on the research side. Link below, drop a comment or DM me for access. 🔗 https://post-pilot-ai.streamlit.app/ #AI (https://www.linkedin.com/search/results/all/?keywords=%23ai&origin=HASH_TAG_FROM_FEED) #Python (https://www.linkedin.com/search/results/all/?keywords=%23python&origin=HASH_TAG_FROM_FEED) #Streamlit (https://www.linkedin.com/search/results/all/?keywords=%23streamlit&origin=HASH_TAG_FROM_FEED) #BuildInPublic (https://www.linkedin.com/search/results/all/?keywords=%23buildinpublic&origin=HASH_TAG_FROM_FEED) #OpenSource (https://www.linkedin.com/search/results/all/?keywords=%23opensource&origin=HASH_TAG_FROM_FEED) #MachineLearning (https://www.linkedin.com/search/results/all/?keywords=%23machinelearning&origin=HASH_TAG_FROM_FEED)
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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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