Spaces:
Sleeping
Sleeping
| title: DVNC.AI | |
| emoji: 🧠 | |
| colorFrom: indigo | |
| colorTo: blue | |
| sdk: gradio | |
| sdk_version: 5.29.0 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| short_description: Connectome-native scientific discovery workspace | |
| # DVNC.AI | |
| DVNC.AI is a connectome-native scientific discovery workspace designed to search, expand, and structure research topics into an interactive knowledge graph. The application supports topic-led discovery, paper lookup, document upload, graph expansion, and AI-assisted reasoning over research artifacts. | |
| This repository currently runs as a **Gradio Space** with a root-level launcher file required by Hugging Face Spaces. The production UI lives in the nested application package, and the root `app.py` exists so the Space can boot correctly in the default Gradio runtime. | |
| ## Current repository layout | |
| ```text | |
| . | |
| ├── app.py # Root Hugging Face launcher | |
| ├── app_old.py # Legacy root app | |
| ├── requirements.txt # Python dependencies for the Space | |
| ├── README.md | |
| ├── dvnc_ai_hf/ # Earlier app package | |
| └── dvnc_ai_v2_hf/ # Current primary app package | |
| ``` | |
| ## How the current Space starts | |
| The Space is configured as a **Gradio Space**, which means Hugging Face expects a root `app.py` and installs dependencies from `requirements.txt`. The root launcher simply imports the active Gradio demo from `dvnc_ai_v2_hf.app` and starts it. | |
| That pattern is intentional and should remain in place unless the Space is migrated to Docker. | |
| ## Supported architecture options | |
| Two deployment patterns are supported for the next phase of development. | |
| ### Option 1: Gradio Space + external parser services | |
| This is the simplest path and is the recommended option if the goal is to keep the current Space lightweight. | |
| #### How it works | |
| - Hugging Face runs the Gradio app using the root `app.py`. | |
| - The main UI and orchestration logic live in `dvnc_ai_v2_hf/`. | |
| - External scholarly/document parsing services are called over HTTP. | |
| - PDF parsing can use a layered fallback: | |
| 1. External GROBID endpoint for scholarly TEI extraction. | |
| 2. Local Docling-based conversion for layout-aware parsing. | |
| 3. Local PyMuPDF fallback for raw text extraction. | |
| #### Recommended environment variables | |
| - `ANTHROPIC_API_KEY` — required for Claude-powered reasoning. | |
| - `GROBID_URL` — optional external GROBID server URL. | |
| - `SEMANTIC_SCHOLAR_API_KEY` — optional, improves Semantic Scholar API access. | |
| - `OPENALEX_EMAIL` — optional polite-pool identity for OpenAlex-style requests. | |
| - `CROSSREF_MAILTO` — optional polite contact for metadata requests. | |
| #### Recommended use cases | |
| Use this mode if: | |
| - the Space should remain a standard Gradio Space; | |
| - the parser stack can live outside the Space; | |
| - fast iteration is more important than bundling every service into one runtime. | |
| ### Option 2: Docker Space + bundled parsing stack | |
| This is the recommended option if the application needs a first-class parsing service bundled with the app runtime. | |
| #### How it works | |
| - The Space is converted from `sdk: gradio` to `sdk: docker`. | |
| - A custom `Dockerfile` starts the web app and any required background services. | |
| - GROBID can run inside the same container or through an internal companion service. | |
| - The app can expose a single user-facing web interface while running a richer backend. | |
| #### Recommended use cases | |
| Use this mode if: | |
| - the Space should include GROBID directly; | |
| - system packages or custom services are required; | |
| - document parsing quality is a core product feature; | |
| - the app needs more control over startup, ports, or service orchestration. | |
| #### YAML for Docker migration | |
| If the Space is migrated to Docker, replace the YAML block at the top of this README with: | |
| ```yaml | |
| *** | |
| title: DVNC.AI | |
| emoji: 🧠 | |
| colorFrom: indigo | |
| colorTo: blue | |
| sdk: docker | |
| pinned: false | |
| license: mit | |
| short_description: Connectome-native scientific discovery workspace with bundled parsing and graph expansion services. | |
| *** | |
| ``` | |
| In Docker mode, `app_file` is no longer used because startup is controlled by the `Dockerfile`. | |
| ## Product direction | |
| The target application architecture supports: | |
| - **Research topic discovery** — search papers by topic or concept. | |
| - **Paper lookup** — search by title, DOI, paper name, or direct link. | |
| - **Autonomous discovery** — retrieve candidates from multiple scholarly sources. | |
| - **User selection** — show candidate papers and let the user choose which ones enter the graph. | |
| - **PDF upload** — allow users to upload papers directly. | |
| - **Structured parsing** — extract title, abstract, sections, references, and metadata from documents. | |
| - **Graph expansion** — turn selected or parsed documents into graph nodes and edges for the self-learning graph. | |
| ## Planned source connectors | |
| The next implementation phase is designed to support a multi-source retrieval layer such as: | |
| - Crossref for DOI and bibliographic metadata. | |
| - OpenAlex for topic/title discovery and scholarly metadata enrichment. | |
| - Semantic Scholar for academic graph enrichment and relevance ranking. | |
| - arXiv for preprints and open metadata. | |
| - Europe PMC for biomedical and life-science literature. | |
| - Direct URL ingestion from paper landing pages and PDFs. | |
| ## Parser strategy | |
| Document parsing should use a priority-based parser stack: | |
| 1. **GROBID** for scholarly PDF parsing into structured TEI/XML. | |
| 2. **Docling** for layout-aware extraction, tables, and document conversion. | |
| 3. **PyMuPDF** for fast native PDF text extraction fallback. | |
| This approach keeps the PDF uploader in the product while improving document understanding significantly over plain text extraction alone. | |
| ## Running locally | |
| ### Gradio mode | |
| Install dependencies: | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| Start the Space locally: | |
| ```bash | |
| python app.py | |
| ``` | |
| If an external parser is used, export the parser endpoint first: | |
| ```bash | |
| export GROBID_URL=http://localhost:8070 | |
| export ANTHROPIC_API_KEY=your_key_here | |
| python app.py | |
| ``` | |
| ### Docker mode | |
| Once a `Dockerfile` is added, run locally with: | |
| ```bash | |
| docker build -t dvnc-ai . | |
| docker run -p 7860:7860 dvnc-ai | |
| ``` | |
| ## Deployment notes | |
| ### For Gradio Spaces | |
| Keep these files at the repository root: | |
| - `README.md` | |
| - `app.py` | |
| - `requirements.txt` | |
| Hugging Face Spaces expects the root application entrypoint for Gradio deployments. | |
| ### For Docker Spaces | |
| Add: | |
| - `Dockerfile` | |
| - any startup scripts or service config files | |
| Then switch the README YAML to `sdk: docker`. | |
| ## Secrets and configuration | |
| At minimum, set: | |
| - `ANTHROPIC_API_KEY` | |
| Depending on the selected architecture, also set: | |
| - `GROBID_URL` | |
| - `SEMANTIC_SCHOLAR_API_KEY` | |
| - source-specific API credentials if needed | |
| ## Development note | |
| At present, `dvnc_ai_v2_hf/` should be treated as the primary active application package. The `dvnc_ai_hf/` and `app_old.py` files appear to represent earlier iterations and should be retained only if they are still needed for rollback or reference. |