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# AI Interview Coach - Session Handoff Summary
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This document captures the current architecture, styling status, and immediate roadmap of the AI Interview Coach project for the next chat session.
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## 1. Project Directory & Git Status
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* **Active Branch:** `feature/job-analyzer-pipeline` (all changes committed and pushed upstream).
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* **Repository Path:** `c:\Users\ishan\OneDrive\Desktop\HF_Hackathon`
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---
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## 2. Architectural Code Split (Refactored)
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To make the codebase professional and maintainable, the monolithic `interview_coach.py` was refactored and split into three modular files:
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1. **`config.py`**: Holds static configuration settings, prompt definitions, CSS styles, and resources databases:
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* `OLLAMA_URL` (local model API endpoint)
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* `HISTORY_FILE` (`interview_history.json` path)
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* `QUESTION_PROMPTS` & `TIPS_DB`/`DEFAULT_TIPS`
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* `CUSTOM_CSS`
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2. **`engine.py`**: Houses all backend processing, database transactions, helper functions, and LLM utilities:
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* `load_history()` & `save_history()`
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* `ask_ollama()`, `clean_question()`, `generate_all_questions()`, `score_answer()`
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* `next_question()`, `render_session_log()`, `compute_stats()`, `render_history()`, `build_tips()`
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3. **`interview_coach.py`**: Contains only the core UI layout (`gr.Blocks`), HTML content, animated blob setups, and event listeners (`.click()`), cleanly importing configurations and functions from `config.py` and `engine.py`.
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---
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## 3. UI, Theming, and Styling Status
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* **Active Theme:** Dark mode base (`body { background: #0d0d1f }`) with Google Sans typography.
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* **Background Blobs:** 3 animated gradient blobs (`#6366f1`, `#8b5cf6`, `#f97316`) that drift using CSS keyframes.
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* **Gradio Transparency:** Custom CSS makes `.gradio-container` and `gradio-app` transparent so the background blobs display cleanly through the layers.
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* **Light Mode Note:** We experimented with a minimalist light/white theme but rolled back to the dark mode because standard Gradio text elements lost visibility. The styling remains on the darker theme with soft glowing blobs.
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* **Public Sharing:** Ready to run publicly via `demo.launch(share=True)`.
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---
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## 4. Immediate Next Steps & Hackathon Roadmap
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1. **Add Custom Text Analytics (Upskilling Showcase):**
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* Integrate a custom analysis function in `engine.py` (e.g., using pure Python or NLP tools) to measure candidate answer metrics like **vocabulary diversity**, **word count checks**, or **targeted keyword frequency matching** before/alongside LLM feedback.
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2. **Enhance Coaching Output:**
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* Render LLM evaluation details (Scores, Strengths, Weaknesses, Fixes) into dedicated structured UI components instead of a raw text block.
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