---
title: Market Performance Sentinel (Demo)
emoji: 🧬
colorFrom: indigo
colorTo: purple
sdk: streamlit
sdk_version: 1.39.0
app_file: app.py
pinned: false
license: mit
short_description: Multi-agent LangGraph chatbot for market analytics.
---

# Market Performance Sentinel — Demo

A multi-agent **LangGraph** chatbot that answers natural-language questions about pharma market
performance — built around a *supervisor + specialised agents* architecture.

> **This Space runs on 100% fictional data.**
> All company names, product names, and metric values are **synthetic** and were generated
> procedurally for demonstration purposes. The fictional company in the demo is **NovaPharma**
> and its products (NOVACOR, NOVAGLU, etc.) do **not** exist.

## What this demo showcases

The chatbot answers questions like:

- *"What's the market share of the top 3 products in France for the Growth Hormone market?"*
- *"In LATAM, which country has the biggest QTR-QoQ value change in the Hypothyroid market in 25Q3?"*
- *"Which cluster is leading the change in APAC for the Anti-EGFR market?"*
- *"What are the clusters for the Injectable Platform in APAC?"*

It is **not** a Retrieval-Augmented-Generation chatbot — it is a **multi-agent system** where
each agent has a specific responsibility:

```text
              ┌────────────────┐
              │ query_analyzer │   ← LLM classifies intent
              └───────┬────────┘
        ┌─────────────┼───────────────┐
   data_retrieval  parameter_info  out_of_scope
        │             │                │
        ▼             ▼                │
┌────────────────┐ ┌──────────────────┐│
│ data_extraction│ │  data_knowledge  ││  ← LLM-assisted filter parsing,
│   (SQL query)  │ │  (param tables)  ││    fuzzy parameter lookup
└────────┬───────┘ └────────┬─────────┘│
         │  (optional)      │          │
         ▼                  │          │
┌────────────────┐          │          │
│ leading_country│          │          │  ← deterministic country
│  (delta calc)  │          │          │    delta calculation
└────────┬───────┘          │          │
         │                  │          │
         ▼                  ▼          ▼
         ┌──────────────────────────────┐
         │      generate_response       │  ← LLM synthesises final
         │  (LLM synthesis + fallback)  │    business-friendly answer
         └──────────────────────────────┘
```

| Node | Role |
|------|------|
| `query_analyzer` | LLM classifies user intent: `data_retrieval`, `parameter_info`, `both`, or `out_of_scope`. |
| `data_extraction` | LLM parses filters (region, period, product, market, …) from the user query, then issues a **targeted SQL** query — no full-table scan. |
| `data_knowledge` | Keyword-scored lookup into the parameter tables (clusters, markets, country-region mapping). |
| `leading_country` | Deterministic, no-LLM calculation that reconstructs prior-period deltas and ranks countries by contribution. |
| `generate_response` | LLM synthesises a natural-language answer from extracted data + filters + conversation history; falls back to a deterministic CSV-formatted answer if the LLM is unavailable. |

## Tech stack

- **Streamlit** for the UI
- **LangGraph** for the agent graph
- **Hugging Face Inference Providers** (`InferenceClient`) for the LLM calls — free serverless tier
- **SQLite** (single-file `demo.sqlite`) as the metrics store — pre-seeded with synthetic data
- **Python 3.10+**

## Configuration

The Space needs **one secret**:

| Secret | Where to set it | Notes |
|---|---|---|
| `HF_TOKEN` | Space → Settings → Variables and secrets | A free Hugging Face read token works fine. |

Optional environment variables:

| Variable | Default | Description |
|---|---|---|
| `HF_MODEL` | `meta-llama/Llama-3.3-70B-Instruct` | Model used for all LLM calls. Any OpenAI-compatible chat model on HF Inference Providers will work. |
| `HF_PROVIDER` | `auto` | HF inference provider routing (`auto`, `hf-inference`, `together`, …). |

## Running locally

```bash
pip install -r requirements.txt
python seed_data.py            # creates demo.sqlite (one-off; idempotent)
export HF_TOKEN=hf_xxx          # Linux/macOS
# $env:HF_TOKEN = "hf_xxx"      # Windows PowerShell
streamlit run app.py
```

## Disclaimer

This is a **demonstration project**. The data, products, and company branding are entirely
fictional. Numbers were generated procedurally and have no relationship to any real-world
pharmaceutical market.

## License

MIT