| --- |
| title: Dukaan Saathi – AI WhatsApp Reply Assistant |
| emoji: 🚀 |
| colorFrom: yellow |
| colorTo: yellow |
| sdk: gradio |
| sdk_version: 6.18.0 |
| python_version: '3.13' |
| app_file: app.py |
| pinned: false |
| --- |
| |
| # 🏪 Dukaan Saathi — AI WhatsApp Reply Assistant for Indian Shop Owners |
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| > *Built for the [Gradio × Hugging Face Build-Small Hackathon 2026](https://huggingface.co/build-small-hackathon) · Track: Backyard AI* |
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| ## The Problem |
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| My neighbor runs a medical store in Ahmedabad. Every day, 40–60 customers message him on WhatsApp asking about medicine availability, prices, and home delivery. He's one person behind a counter — he can't reply to everyone fast enough, and he loses orders because of it. |
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| Big AI tools like ChatGPT aren't an option for him: he doesn't want customer prescription details going to some American server. And he doesn't have time to learn a new app. |
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| **Dukaan Saathi** solves this with a small, privacy-respecting model that runs entirely on Hugging Face infrastructure — no data sent to big cloud APIs, no subscriptions, no complexity. |
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| ## What It Does |
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| 1. Shop owner sets up their profile once (shop name, type, items they sell) |
| 2. Paste any customer WhatsApp message |
| 3. Choose language: Hindi, Gujarati, English, or Hinglish |
| 4. Get a perfectly worded, human-sounding reply in 3 seconds |
| 5. Copy → paste → send |
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| ## Why Small Models Are the *Right* Choice Here |
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| This isn't a compromise — small models are actually **better** for this use case: |
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| - **Privacy**: Customer messages (some containing prescription details or personal info) never leave a controlled environment |
| - **Speed**: 7B models respond in seconds, not minutes |
| - **Cost**: Shop owners can't pay per-API-call; small models make this sustainable |
| - **Offline potential**: The same model can run locally on a mid-range laptop |
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| ## Model |
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| **Qwen/Qwen2.5-7B-Instruct** — 7 billion parameters (well within the 32B cap) |
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| Chosen because: |
| - Best-in-class multilingual performance for Hindi, Gujarati, and Hinglish |
| - Instruction-following quality that produces natural, conversational replies |
| - Lightweight enough to be genuinely "small model" in spirit |
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| ## How to Run Locally |
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| ```bash |
| git clone https://huggingface.co/spaces/YOUR_USERNAME/dukaan-saathi |
| cd dukaan-saathi |
| pip install -r requirements.txt |
| HF_TOKEN=your_token python app.py |
| ``` |
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| ## Hackathon Info |
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| - **Event**: [Gradio × HF Build-Small Hackathon 2026](https://huggingface.co/build-small-hackathon) |
| - **Track**: Chapter One — Backyard AI |
| - **Constraint**: ≤32B parameters (using 7B) |
| - **Builder**: Parth Bhuptani ([@ParthBhuptani](https://github.com/ParthBhuptani)) |
