develop -> main mvp version

.dockerignore

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+.git
+.github
+.venv
+__pycache__
+.pytest_cache
+.ruff_cache
+.mypy_cache
+.langgraph_api
+tests
+docs
+*.pyc
+*.pyo
+*.pyd
+*.log
+.env
+.env.*
+README.md
+TEMPLATE_README.md
+drawkit.xml

.github/workflows/publish-ghcr.yml

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+name: Build and Publish Container
+
+on:
+  push:
+    branches: [main]
+    tags: ["v*"]
+  workflow_dispatch:
+
+permissions:
+  contents: read
+  packages: write
+
+jobs:
+  publish:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v5
+
+      - name: Set image name
+        id: image
+        run: echo "name=ghcr.io/${GITHUB_REPOSITORY,,}" >> "$GITHUB_OUTPUT"
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+
+      - name: Log in to GHCR
+        uses: docker/login-action@v3
+        with:
+          registry: ghcr.io
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Extract image metadata
+        id: meta
+        uses: docker/metadata-action@v5
+        with:
+          images: ${{ steps.image.outputs.name }}
+          tags: |
+            type=ref,event=branch
+            type=ref,event=tag
+            type=sha
+            type=raw,value=latest,enable={{is_default_branch}}
+
+      - name: Build and push
+        uses: docker/build-push-action@v6
+        with:
+          context: .
+          file: ./Dockerfile
+          push: true
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}
+          cache-from: type=gha
+          cache-to: type=gha,mode=max

AGENTS.md

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+You are working inside a LangGraph-based Python project.
+
+Create project planning documentation for a new AI service named "InterviewGraph".
+
+Your task:
+Generate two markdown documents:
+
+1) README.md (high-level overview)
+2) docs/PLAN.md (detailed product & technical planning document)
+
+The project name is:
+InterviewGraph
+
+The core idea:
+InterviewGraph takes a resume PDF as input and generates structured, difficulty-rated interview questions based on the resume content.
+
+====================================
+README.md REQUIREMENTS
+====================================
+
+README.md must include:
+
+1. Project Title
+2. Short Description (3~5 lines)
+3. Core Features (bullet points)
+4. Architecture Overview (high-level explanation of LangGraph pipeline)
+5. Example Flow (PDF → Questions)
+6. Tech Stack (Python, LangGraph, FastAPI, LLM provider)
+7. MVP Scope
+8. Future Roadmap (short bullet list)
+9. How to Run (placeholder instructions acceptable)
+
+Tone:
+
+- Professional
+- Clear
+- Developer-focused
+- No marketing exaggeration
+
+====================================
+docs/PLAN.md REQUIREMENTS
+====================================
+
+PLAN.md must include structured sections:
+
+# 1. Project Vision
+
+- Why this project exists
+- Target users
+
+# 2. User Scenarios
+
+- Primary scenario: resume upload → question generation
+- Failure scenario: text extraction failure
+
+# 3. Functional Requirements
+
+Include:
+
+- PDF input handling
+- Resume section parsing
+- Signal extraction (skills, projects, keywords)
+- Interview question generation (15 questions)
+- Difficulty rating (1~5)
+- Structured JSON output
+- Markdown output
+
+# 4. Non-Functional Requirements
+
+Include:
+
+- Privacy considerations (no raw resume logging)
+- LLM output schema validation
+- Error handling & retry
+- Stateless default design
+
+# 5. LangGraph Architecture Design
+
+List required nodes:
+
+- extract_text
+- parse_sections
+- extract_signals
+- generate_questions
+- rate_difficulty
+- format_output
+
+Describe:
+
+- State design (raw_text, sections, signals, questions, markdown, errors)
+- Linear pipeline for MVP
+- Possible conditional branch for error handling
+
+# 6. Data Model Design
+
+Define structured interview question format:
+
+- id
+- category (tech | project | system | deep-dive)
+- difficulty (1~5)
+- question
+- expected_points
+- followups
+
+# 7. MVP Definition of Done
+
+Clearly define what counts as completed MVP.
+
+# 8. Out of Scope (for MVP)
+
+Explicitly list:
+
+- OCR
+- Vector DB / RAG
+- Multi-agent system
+- Mock interview answer evaluation
+
+# 9. Development Phases
+
+Phase 1: Schema & PDF extraction
+Phase 2: Section parsing & signal extraction
+Phase 3: Question generation & rating
+Phase 4: API integration
+
+====================================
+
+Formatting Rules:
+
+- Use clean markdown formatting
+- Use clear headers
+- No emojis
+- No casual tone
+- No unnecessary verbosity
+
+Do not generate code.
+Only generate the two markdown documents.
+
+If docs/ directory does not exist, create it logically in output structure.

Dockerfile

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+FROM python:3.12-slim
+
+ENV PYTHONDONTWRITEBYTECODE=1 \
+    PYTHONUNBUFFERED=1 \
+    UV_LINK_MODE=copy
+
+WORKDIR /app
+
+RUN pip install --no-cache-dir uv
+
+COPY pyproject.toml uv.lock ./
+COPY casts ./casts
+COPY app ./app
+
+RUN uv sync --frozen --no-dev --all-packages
+
+EXPOSE 8000
+
+CMD ["uv", "run", "uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]

README.md

@@ -1,23 +1,107 @@
-# Act: Interview Graph
+# 🙋‍♂️ InterviewGraph
 
-A LangGraph-based Act project scaffolded with Act Operator.
+InterviewGraph is a LangGraph-based service that generates structured interview questions from resume content.
 
-## Quick Start
+It accepts either resume text or a PDF file, extracts relevant signals, and returns 15 questions with difficulty ratings.
+
+The response includes both structured JSON and Markdown for interviewer-friendly review.
+
+> This project was developed through Act-Operator.
+>
+> [😺 Act-Operator Github](https://github.com/Proact0/act-operator)
+
+## What It Does
+
+- Accepts resume text or PDF input
+- Parses resume sections (summary, skills, experience, projects, education)
+- Extracts signals (skills, projects, keywords)
+- Generates 15 interview questions
+- Rates question difficulty (1-5)
+- Formats output as JSON and Markdown
+
+## Architecture (MVP)
+
+Pipeline:
+
+`extract_text -> parse_sections -> extract_signals -> generate_questions -> rate_difficulty -> format_output`
+
+## Quick Start (Local)
 
 1. Install dependencies:
-   ```bash
-   uv sync --all-packages
-   ```
 
-2. Run the development server:
-   ```bash
-   uv run langgraph dev
-   ```
+```bash
+uv sync --all-packages
+```
+
+1. Run API server:
+
+```bash
+uv run uvicorn app.main:app --reload
+```
+
+1. Open API docs:
+
+- `http://127.0.0.1:8000/docs`
+
+## API Usage
+
+- `POST /api/v1/interview-questions` for text input
+- `POST /api/v1/interview-questions/upload` for PDF upload (multipart/form-data)
+
+Example JSON payload:
+
+```json
+{
+  "resume_text": "Summary ... Skills ... Projects ..."
+}
+```
+
+## Container Usage
+
+Build image:
+
+```bash
+docker build -t interviewgraph:local .
+```
+
+Run container:
+
+```bash
+docker run --rm -p 8000:8000 interviewgraph:local
+```
+
+Open docs:
+
+- `http://127.0.0.1:8000/docs`
+
+## GHCR Publishing
+
+This repository includes `.github/workflows/publish-ghcr.yml`.
+
+- On push to `main`, the workflow builds and publishes to `ghcr.io/<owner>/<repo>`.
+- On push tag `v*`, it also publishes versioned tags.
+- You can manually trigger publishing with `workflow_dispatch`.
+
+Pull from GHCR:
+
+```bash
+docker pull ghcr.io/<owner>/<repo>:latest
+docker run --rm -p 8000:8000 ghcr.io/<owner>/<repo>:latest
+```
+
+## Current Scope (MVP)
+
+- Text-extractable PDF support
+- Resume-grounded question generation pipeline
+- Structured error payloads
+
+## Out of Scope (MVP)
 
-3. Access Studio UI:
-   - Studio UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024
-   - API: http://127.0.0.1:2024
-   - API Docs: http://127.0.0.1:2024/docs
+- OCR for scanned PDFs
+- Vector DB / RAG
+- Multi-agent orchestration
+- Mock interview answer scoring
 
-For detailed documentation, see [TEMPLATE_README.md](TEMPLATE_README.md).
+## License
 
+Apache License 2.0 - see [LICENSE](https://www.apache.org/licenses/LICENSE-2.0) for details.