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fix: Defer IPython import to prevent deployment error
- Moves the `IPython` import into the notebook-only helper function in `utils.py`. - This lazy import resolves the `ModuleNotFoundError` on Hugging Face by not loading dev dependencies in the production server. - Applies standard `ruff` formatting fixes.009e36a -
style: Format code and fix linter warnings
Applied `ruff format` to the entire codebase for consistency. Also fixed all outstanding Pyright and Ruff linter warnings: - Ignored warnings for correct but "private" imports (gradio, genai). - Added a check for the API key to fix a potential `None` type error. - Suppressed E402 import errors in the build script where required.280d562
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feat(ui): Overhaul Gradio app for interactivity and evaluation
Transforms the basic Gradio interface into a polished, feature-rich web application designed for demonstration and evaluation. The new UI provides a significantly improved user experience with interactive examples and adds robust features for capturing and analyzing the RAG pipeline's output. This work completes the transition from a back-end proof-of-concept to a user-facing demo. Key Changes: - **Gradio App Overhaul (`app.py`):** - Rewrote the UI using `gr.Blocks` for a custom two-column layout. - Added an "Example Scenarios" section with radio buttons to quickly populate the form with pre-defined student narratives. - Implemented an "Evaluation Data" accordion that displays the full RAG output (inputs, retrieval results, LLM output) in a JSON viewer. - Added a "Download JSON" button to allow users to save the complete evaluation data for offline analysis. - Refactored the API call to be a generator (`yield`), enabling progressive updates like "Processing..." messages an…abf3963
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feat(recommender): Implement generative synthesis for educator-friendly output
This commit completes the core Proof-of-Concept by implementing the final Generation step of the RAG pipeline. The system now moves beyond simple document retrieval to provide a fully synthesized, actionable recommendation tailored for educators. This fulfills the critical project requirement that the output must be clear and directly usable, not just a list of raw search results. This work completes the objectives outlined in Phase 3 of the implementation plan. Key Changes: - **Added Generative Synthesis**: Introduced a function in . This function leverages the Google Gemini API to process the top-k retrieved document chunks and synthesize them into a coherent, practical recommendation. - **Created Prompt Templates**: Added a new module to store and manage persona-specific prompts. The initial implementation includes a detailed prompt for the 'teacher' persona, designed to produce professional and encouraging output. - **Integrated into Main Application**: Refactored to call the …929cde0 -
feat(notebook): Add PoC notebook for RAG demonstration
This commit introduces the primary technical deliverable: a functional, interactive Proof-of-Concept (PoC) in a Google Colab notebook. The notebook () demonstrates the functionality of the Freshman On-Track Intervention Recommender. It guides the user through the entire process: - Setting up the local environment. - Loading the final, semantically chunked knowledge base. - Initializing the embedding model and FAISS vector database. - Accepting a user's free-text query about a student's challenges. - Performing a semantic search and displaying the top 3 recommendations. Key Changes: - **Added PoC Notebook**: Created to serve as the interactive demonstration of the RAG system. - **Created Display Utilities**: Introduced a new module with a helper function. This formats the raw search results into a clean, human-readable output, making the recommendations clear and actionable within the notebook. - **Updated Dependencies**: Added and to the dependencies in to ensure the project's…1550049