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| 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 | |