| --- |
| title: JanMat |
| emoji: 🏛️ |
| colorFrom: blue |
| colorTo: green |
| sdk: gradio |
| sdk_version: 4.44.0 |
| app_file: app.py |
| pinned: false |
| --- |
| |
| # JanMat |
|
|
| A chatbot that reads Indian legislative bills — their PRS Legislative |
| Research PDF briefs in full, the bill pages themselves, related articles, |
| and public opinion — and answers questions grounded in that material, with |
| sources cited. Supports both typed and spoken questions (voice via Sarvam |
| AI), and speaks answers back. |
|
|
| This is a single Gradio Space: the chat UI, the retrieval engine, and the |
| ingestion pipeline all run here — no separate frontend deployment needed. |
|
|
| ## Required secrets (Space Settings → Variables and secrets) |
|
|
| Add each of these as a **Secret**: |
|
|
| | Secret name | Where to get it | |
| |------------------------|------------------| |
| | `SUPABASE_URL` | Supabase project → Settings → API → Project URL | |
| | `SUPABASE_SERVICE_KEY` | Supabase project → Settings → API → service_role key | |
| | `GROQ_API_KEY` | console.groq.com → API Keys | |
| | `SARVAM_API_KEY` | sarvam.ai → dashboard → API keys | |
| | `CRON_SECRET` | any random string you make up yourself | |
|
|
| ## First-time setup |
|
|
| 1. Create a Supabase project, open its SQL Editor, and run `supabase/schema.sql` |
| from this repo once. This creates the tables and the vector search function. |
| 2. Add the five secrets above in this Space's settings. |
| 3. Once the Space is running, open the "Admin: refresh the knowledge base" |
| section at the bottom of the app, enter your `CRON_SECRET`, and click |
| "Run ingestion now". This scrapes PRS bills + their PDFs and fills the |
| knowledge base — it takes a few minutes. |
| 4. Once ingestion finishes, ask a question in the chat box above. |
|
|
| ## How it works |
|
|
| - **Reading**: `pdf_ingest.py` downloads and reads every page of every linked |
| PDF (and every paragraph of every HTML page) in full — nothing is |
| summarized at this stage. |
| - **Storage**: `embeddings.py` + `db.py` chunk and embed that text (locally, |
| free, no extra API key) and store it in Supabase with pgvector. |
| - **Answering**: `rag.py` finds the most relevant chunks for a question and |
| asks Groq to answer using only those chunks, citing which document each |
| part came from. |
| - **Voice**: `voice.py` calls Sarvam AI for speech-to-text (your question) |
| and text-to-speech (the spoken answer). |
|
|
