level-6: Abhinav Chaudhary

.devcontainer/devcontainer.json

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+{
+  "name": "Python 3",
+  // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
+  "image": "mcr.microsoft.com/devcontainers/python:1-3.11-bookworm",
+  "customizations": {
+    "codespaces": {
+      "openFiles": [
+        "README.md",
+        "agent/agent.py"
+      ]
+    },
+    "vscode": {
+      "settings": {},
+      "extensions": [
+        "ms-python.python",
+        "ms-python.vscode-pylance"
+      ]
+    }
+  },
+  "updateContentCommand": "[ -f packages.txt ] && sudo apt update && sudo apt upgrade -y && sudo xargs apt install -y <packages.txt; [ -f requirements.txt ] && pip3 install --user -r requirements.txt; pip3 install --user streamlit; echo '✅ Packages installed and Requirements met'",
+  "postAttachCommand": {
+    "server": "streamlit run agent/agent.py --server.enableCORS false --server.enableXsrfProtection false"
+  },
+  "portsAttributes": {
+    "8501": {
+      "label": "Application",
+      "onAutoForward": "openPreview"
+    }
+  },
+  "forwardPorts": [
+    8501
+  ]
+}

submissions/abhinav-chaudhary/level6/.env.example

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+NEO4J_URI=
+NEO4J_USER=
+NEO4J_PASSWORD=

submissions/abhinav-chaudhary/level6/DASHBOARD_URL.txt

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+https://abhinav-factorygraph.streamlit.app/

submissions/abhinav-chaudhary/level6/README.md

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+# Factory Knowledge Graph Dashboard
+
+A graph-based production dashboard for a Swedish steel fabrication factory.
+Data from three CSVs is loaded into Neo4j as a knowledge graph, then explored through a Streamlit app with Plotly charts.
+
+Built for the Level 6 factory graph challenge focused on Neo4j and Streamlit..
+
+\---
+
+## What it does
+
+The factory runs 8 projects across 9 production stations with 13 workers.
+Raw CSV data (production runs, worker assignments, weekly capacity) is converted into a Neo4j graph with 9 node labels and 12 relationship types.
+
+The dashboard has five pages:
+
+|Page|What it shows|
+|-|-|
+|**Project Overview**|Variance heatmap — which projects are ahead or behind plan, by week|
+|**Station Load**|Planned vs actual hours per station, overrun highlighting|
+|**Capacity Tracker**|Weekly capacity breakdown (own / hired / overtime) vs demand|
+|**Worker Coverage**|Which workers can cover each station, cert gaps, full roster|
+|**Self-Test**|Schema validation — connection, node counts, rel counts, edge properties|
+
+\---
+
+## Tech stack
+
+* **Python 3.11+**
+* **Neo4j 5.x** — graph database
+* **Streamlit** — dashboard UI
+* **Plotly** — interactive charts
+* **Pandas** — dataframe wrangling
+* **python-dotenv** — local credential management
+
+\---
+
+## Project structure
+
+```
+.
+├── app.py                  # Streamlit dashboard
+├── seed\_graph.py           # Loads CSVs → Neo4j graph
+├── factory\_production.csv  # Production runs data
+├── factory\_workers.csv     # Worker \& certification data
+├── factory\_capacity.csv    # Weekly capacity snapshots
+├── schema.md               # Graph schema (Mermaid diagram + reference tables)
+├── answers.md              # Level 5/6 analysis write-up
+├── .env                    # Your credentials (not committed)
+└── README.md
+```
+
+\---
+
+## Setup
+
+### 1\. Clone and install dependencies
+
+```bash
+git clone <your-repo-url>
+cd <your-repo>
+
+python -m venv venv
+source venv/bin/activate        # Windows: venv\\Scripts\\activate
+
+pip install streamlit neo4j pandas plotly python-dotenv
+```
+
+### 2\. Start Neo4j
+
+You can use [Neo4j Desktop](https://neo4j.com/download/), [AuraDB](https://neo4j.com/cloud/aura-free/) (free tier), or Docker:
+
+```bash
+docker run \\
+  --name neo4j-factory \\
+  -p 7474:7474 -p 7687:7687 \\
+  -e NEO4J\_AUTH=neo4j/yourpassword \\
+  neo4j:5
+```
+
+### 3\. Create your `.env`
+
+Create a `.env` file in the project root:
+
+```env
+NEO4J\_URI=bolt://localhost:7687
+NEO4J\_USER=neo4j
+NEO4J\_PASSWORD=yourpassword
+```
+
+> For AuraDB, use the `neo4j+s://` URI from your instance dashboard.
+
+### 4\. Seed the graph
+
+This loads all three CSVs into Neo4j. Run it once (or re-run after a `MATCH (n) DETACH DELETE n` to reset):
+
+```bash
+python seed\_graph.py
+```
+
+### 5\. Run the dashboard
+
+```bash
+streamlit run app.py
+```
+
+Open [http://localhost:8501](http://localhost:8501) in your browser.
+
+\---
+
+## Streamlit Cloud deployment
+
+1. Push the repo to GitHub (make sure `.env` is in `.gitignore`).
+2. Go to [share.streamlit.io](https://share.streamlit.io) and connect the repo.
+3. Add your credentials under **Settings → Secrets**:
+
+```toml
+NEO4J\_URI = "neo4j+s://xxxx.databases.neo4j.io"
+NEO4J\_USER = "neo4j"
+NEO4J\_PASSWORD = "yourpassword"
+```
+
+The app reads `st.secrets` first, so no other changes needed.
+
+\---
+
+## Graph schema (quick reference)
+
+**9 node labels:** `Project` · `ProductionEntry` · `Station` · `Product` · `Worker` · `Week` · `CapacitySnapshot` · `Certification` · `BOP`
+
+**12 relationship types:** `HAS\_RUN` · `USES\_PRODUCT` · `PROCESSED\_AT` · `SCHEDULED\_IN` · `REQUIRES\_STATION` · `STRUCTURED\_BY` · `PRIMARILY\_AT` · `CAN\_COVER` · `WORKED\_ON` · `HOLDS` · `REQUIRES\_CERT` · `HAS\_SNAPSHOT`
+
+Two relationships carry data properties:
+
+* `PROCESSED\_AT` → `planned\_hours`, `actual\_hours`, `completed\_units`
+* `SCHEDULED\_IN` → `planned\_hours`, `actual\_hours`
+
+See `schema.md` for the full Mermaid diagram and relationship reference table.
+
+\---
+
+## Running the self-test
+
+Navigate to the **Self-Test** page in the sidebar and click **▶ Run All Tests**.
+
+It checks:
+
+1. Neo4j connection
+2. All 9 node labels present
+3. All 12 relationship types present
+4. Node count ≥ 50
+5. Relationship count ≥ 100
+6. Variance query returns results from `PROCESSED\_AT` edge properties
+
+\---
+
+## Notes
+
+* Data is cached for 5 minutes (`@st.cache\_data(ttl=300)`) — refresh the page or restart Streamlit to force a reload.
+* All dashboard queries run directly against Neo4j after the graph is seeded.
+* `seed\_graph.py` is idempotent — it uses `MERGE` throughout, so re-running won't duplicate data.
+