llm_projects

These are the projects I have implemented using Ed Donner's course - On my own

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File Overview & Problem Statement

1. code_explainer.ipynb

• High-Level Overview: An interactive Gradio web application integrated with OpenRouter/Groq LLM APIs that takes a code snippet in one programming language and explains it tailored to a user's preferred language of expertise.
• What it Solves: Helps developers and students understand code snippets written in unfamiliar programming languages by translating concepts into terms and patterns from a language they already know well.

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2. general_tool_simulator.ipynb

• High-Level Overview: A Gradio chatbot powered by Groq's function calling capabilities, connected to local Python utility functions for precise text analysis.
• What it Solves: Overcomes LLM limitations in exact counting and quantitative reasoning by dynamically delegating character and word count queries to deterministic function tools rather than relying on model generation.

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3. jokes_scrapper.ipynb

• High-Level Overview: An automated scraper and content generator that extracts text from any given web URL using BeautifulSoup and prompts an LLM to craft targeted, original comedy.
• What it Solves: Automates context-aware content generation by transforming raw webpage content into grounded observational humor and clean, structured joke lists.

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4. multichat_orcehstrator.ipynb

• High-Level Overview: A multi-agent conversation simulator that orchestrates multi-turn group discussions among three distinct AI personas (Alex, Raghav, and Harish), tracking shared dialogue history.
• What it Solves: Simulates collaborative brainstorming and multi-perspective discussion dynamics by having specialized personas synthesize ideas, analyze practical implementations, and ask insightful questions in turn.

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5. pipeline_orchestrator.ipynb

• High-Level Overview: A multimodal orchestration workflow combining Hugging Face pipelines and Diffusers to generate text, synthesize speech audio, and render generated images.
• What it Solves: Demonstrates end-to-end multimodal AI orchestration by turning a simple text prompt into generated text content, spoken audio output, and dynamic visual assets within a single pipeline.

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6. problem_job_evaluator.ipynb

• High-Level Overview: An automated hiring assessment tool using web scraping and LLM evaluation within a Gradio interface.
• What it Solves: Streamlines technical candidate evaluation by scraping a candidate's online portfolio, evaluating their skills against a specific business problem, and delivering structured pros, cons, and hiring recommendations for decision-makers.