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It is designed for a university multi-agent systems assignment and runs locally without cloud AI services.\n\n## Problem Addressed\n\nStudents often receive assignment briefs with deadlines, deliverables, formatting rules, marking criteria, and technical requirements spread across paragraphs. This assistant turns that unstructured text into a clearer execution plan.\n\n## Agent Workflow\n\nThe LangGraph workflow follows this sequence:\n\n1. Requirement Analysis Agent  \n   Extracts title, assignment type, requirements, deliverables, deadlines, formatting rules, required tools, marking criteria, and warnings.\n\n2. Task Breakdown Agent  \n   Converts the analyzed requirements into structured tasks and subtasks. It first builds a deterministic plan, then asks Ollama to refine it. If Ollama is unavailable, the deterministic plan is used.\n\n3. Timeline and Priority Agent  \n   Assigns practical priorities and groups task IDs into timeline phases.\n\n4. Final Review Agent  \n   Produces final warnings, a student-facing checklist, and a short readiness summary.\n\nThe workflow is:\n\n```text\nraw input -> requirement analysis -> task breakdown -> timeline/priority -> final review\n```\n\n## Project Structure\n\n```text\napp/\n  agents/\n    requirement_analysis/   Requirement extraction and summary generation\n    task_breakdown/         Structured task planning\n    timeline_priority/      Priority and timeline planning\n    final_review/           Final checklist and readiness output\n  core/                     Config, logging, shared state, LangGraph workflow\n  parsers/                  Text and PDF text extraction helpers\n  utils/                    Small validation and formatting helpers\nsample_inputs/             Example assignment brief used by the app\ntests/                     Unit tests for agents and workflow\nscripts/                   Optional cleanup helpers for submission packaging\n```\n\n## Current Input Support\n\nThe system currently works primarily with plain text assignment briefs. A simple PDF parser is included for local PDF files, but PDF quality depends on whether the PDF contains extractable text. Scanned image-only PDFs are not supported unless OCR is added separately.\n\n## Setup\n\nCreate and activate a virtual environment:\n\n```bash\ncd /Users/bhanukafernando/Documents/student-assignment-assistant\npython3 -m venv venv\nsource venv/bin/activate\n```\n\nInstall dependencies:\n\n```bash\npip install -r requirements.txt\n```\n\n## Ollama Setup\n\nStart Ollama:\n\n```bash\nollama serve\n```\n\nIn another terminal, pull the default model:\n\n```bash\nollama pull qwen3:0.6b\n```\n\nThe default settings are:\n\n```text\nOLLAMA_BASE_URL=http://localhost:11434\nOLLAMA_MODEL=qwen3:0.6b\n```\n\nYou can override these in `.env`.\n\n## Run the Application\n\nRun with the default sample input:\n\n```bash\npython -m app.main\n```\n\nRun with a specific text file:\n\n```bash\npython -m app.main --input sample_inputs/sample_assignment_1.txt --input-type text\n```\n\nRun with a local PDF file:\n\n```bash\npython -m app.main --input path/to/assignment.pdf --input-type pdf\n```\n\nThe app prints the final workflow state, including extracted requirements, task breakdown, priorities, timeline, warnings, checklist, and final output.\n\n## Run the Web Frontend\n\nStart the local web UI:\n\n```bash\npython -m app.web_server\n```\n\nThen open:\n\n```text\nhttp://127.0.0.1:8000\n```\n\nThe frontend provides a polished workspace for text input or PDF upload, then shows the multi-agent output as summary cards, extracted details, task breakdown, timeline, priorities, and final checklist.\n\nSave a generated sample output if needed:\n\n```bash\npython -m app.main --input sample_inputs/sample_assignment_1.txt --input-type text > sample_assignment_1_output.txt\n```\n\n## Run Tests\n\n```bash\npython -m pytest -q\n```\n\n## Sample Inputs\n\nThe default sample file is:\n\n```text\nsample_inputs/sample_assignment_1.txt\n```\n\nAdditional sample briefs are also available:\n\n```text\nsample_inputs/sample_assignment_2.txt\nsample_inputs/sample_assignment_3.txt\n```\n\nYou can edit these files or pass another file with `--input`.\n\n## Team Contributions\n\nRecord final team member names and contribution summaries in the project report. A suggested format is:\n\n```text\nName | Main contribution | Evidence or files worked on\n```\n\nThis README intentionally does not include empty student-name placeholders.\n\n## Submission Packaging\n\nRecommended final folder name:\n\n```text\nstudent-assignment-assistant\n```\n\nBefore zipping or submitting, remove local runtime/cache files:\n\n```bash\nsh scripts/clean_submission.sh\n```\n\nDo not include `venv/`, `.env`, logs, `.pytest_cache/`, `__pycache__/`, `.DS_Store`, or compiled `.pyc` files in the final ZIP.\n\n## Limitations\n\n- The system is designed for assignment brief analysis, not full assignment writing.\n- Text input is the most reliable path.\n- PDF parsing works only when text can be extracted from the PDF.\n- Ollama must be running locally for LLM refinement; otherwise deterministic fallbacks keep the workflow usable.\n","readmeExcerpt":"Student Assignment Assistant A locally hosted multi-agent system that helps students understand assignment briefs, extract key requirements, break the work into tasks, plan priorities, and generate a final submission checklist. 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