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Parth Kulkarni
Parth Kulkarni

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Sift

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Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle Submission 💸

Offline Hinglish DM-to-Dispatch Engine for Instagram Sellers

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle

What I Built

Across India, thousands of small Instagram thrift stores, crochet artists, home bakers, and independent D2C boutiques run their entire business inside Instagram and WhatsApp DMs.

There is just one massive bottleneck: customers don't order in clean forms—they order in messy Hinglish (a blend of Hindi and English). A typical DM looks like this:

"Bhai 2 oversized black acid-wash tees bhej do L size mein. Name: Rohan Deshmukh, Ph: 9823011223. Address: Flat 402, Sai Residency, Kothrud, Pune. GPay kar diya hai 1398 rs."

Every single night, store owners spend 1.5 to 2 hours manually reading through chat dumps, copying names, phone numbers, sizes, and addresses, and pasting them into Excel sheets to generate courier dispatch labels.

Sift solves this completely offline. It is a local-first AI desktop utility that takes raw, unstructured Hinglish chat dumps and extracts validated, courier-ready dispatch tables in seconds—running 100% locally on the seller's laptop at ₹0.00 ($0.00) inference cost.

Who Would Pay For It & The Business Model

  • Target Customers: Instagram thrift pages, home bakers, small boutiques, and WhatsApp commerce sellers handling 15–100+ orders a day.
  • Why They Pay: Saving 45+ hours of manual data entry every month without paying per-message cloud API fees or risking customer privacy.
  • Pricing Model: Offered as a ₹499/month (~$6/mo) flat local license or a ₹1,999 (~$24) one-time local setup where I configure Ollama, the open-weight model, and Sift directly on their machine. Because all inference runs on the user's own hardware, my cloud hosting cost is $0, making every sale 100% net profit.

Demo

Sift Local Dashboard Screenshot

Code

https://github.com/p-rth12999/Sift

How I Built It

Sift is architected from the ground up around local open-weight inference and deterministic schema enforcement:

  1. Local Inference Engine (Ollama): Instead of routing messages to a paid cloud endpoint, Sift connects to a local Ollama server (localhost:11434) running compact open-weight models—specifically Google's gemma2:2b and Meta's llama3.2:3b. These 2B–3B parameter models are the sweet spot for consumer laptops: they consume ~2 GB of RAM, run rapidly on standard laptop CPUs without requiring a discrete GPU, and understand Indian Hinglish slang ("bhej do", "GPay kar diya", "COD rakhna").
  2. Strict Structured Outputs (Pydantic): Small models can occasionally get chatty. To make the extraction 100% reliable for a real business workflow, I paired the Ollama Python SDK with Pydantic (OrderExtraction.model_json_schema()). By passing the JSON schema directly into the model's format parameter at temperature=0.0, the open-weight model is constrained at the token level to emit strictly valid JSON matching our exact database columns (customer_name, phone_number, item_name, quantity, size, total_amount, payment_status, delivery_address).
  3. Zero-Cloud Persistence & Dispatch UI (SQLite3 + Streamlit + Pandas): Extracted orders are committed immediately to a local SQLite database (sift_orders.db). The Streamlit dashboard supports Batch DM Processing (splitting bulk chat dumps via ---), live revenue and payment status KPIs, and one-click CSV/Excel dispatch sheet exports.

System Architecture 

flowchart LR
    A[📥 Messy Hinglish DMs] -->|Batch Input| B(Streamlit Seller UI)
    B -->|Raw Text| C{Local Ollama Engine\ngemma2:2b / llama3.2:3b}
    D[Pydantic Schema\nOrderExtraction] -->|Token-Level JSON Constraint| C
    C -->|Validated JSON| E[(SQLite Local DB\nsift_orders.db)]
    E --> F[📊 Live Revenue KPIs]
    E --> G[📦 Courier CSV Export]

Why Does Open Innovation Matter?

Building Sift with closed, proprietary APIs (like GPT-4o or Claude) would have killed this side hustle before it even launched for three specific reasons:

  1. Unit Economics That Actually Work for Micro-Businesses: Indian Instagram sellers operate on razor-thin margins. If a tool charges them per token or requires a $20/month API subscription on top of the software cost, they simply will not buy it—they will go back to manual copy-pasting. Running open-weight models locally on their existing laptop means marginal cost per parsed order is strictly $0.00, allowing me to price Sift at an impulse-buy flat rate with zero server overhead.
  2. 100% Customer PII Sovereignty: Every single order DM contains highly sensitive Personally Identifiable Information (full names, personal mobile numbers, and exact home addresses). Sending customer residential data to external third-party cloud servers creates privacy and trust barriers. With Sift's local open-weight architecture, customer data never leaves the seller's laptop—in fact, the entire pipeline runs with Wi-Fi completely turned off.
  3. Model Swappability & Zero Vendor Lock-In: Using Ollama as a local harness lets sellers seamlessly switch between gemma2:2b and llama3.2:3b depending on their laptop's RAM and CPU speed, or fine-tune a small model on regional dialects (Marathi/Hindi/Tamil transliteration) without ever worrying about API deprecations or sudden pricing changes.

My Agent Session

Sift review — Offline Hinglish DM-to-Dispatch Engine
You

Review the Sift project files app.py, parser.py, and database.py for the Hacktoberfest Week 2 Side Hustle DEV Challenge, then save/share this agent session to my DEV account and provide the exact agent_session Liquid tag and URL.

Agent

Reviewed Sift's Streamlit UI, Pydantic/Ollama parser, SQLite persistence layer, README, and requirements. Verified all three bundled fallback-parser samples: quantities, payment statuses, amounts, names, and addresses matched the sample intent. Verified all three reviewed Python files have no syntax errors.

Findings: (1) High reliability risk: parser.py catches every Exception around Ollama import/call/JSON decoding and silently falls back, so missing Ollama, timeouts, malformed JSON, schema violations, and runtime defects are indistinguishable from a successful parse. Surface the failure and validate the response with OrderExtraction before persisting. (2) Data-integrity risk: OrderExtraction uses unconstrained str/int/float fields even though the prompt says payment_status is strictly Paid/COD/Pending and phone/quantity/amount have stated semantics; enforce those constraints before database insertion. (3) Fallback scope risk: cloud_demo_fallback is explicitly demo-sample-oriented (hard-coded names/items and narrow regexes), so production messages can be misclassified while appearing successful. Label it as demo-only or make the fallback fail visibly for unsupported inputs. (4) Portability risk: database.py uses a process-current-working-directory relative database path, so launching Streamlit from another directory can create/use a different database. Resolve the DB path from the application directory or configure it explicitly. (5) Transaction/resource robustness: database operations manually open/close connections without context managers or rollback handling; an exception can leave resources open and a partial batch can be reported ambiguously. (6) UX consistency: app.py advertises llama3.2:3b in the caption but defaults the selector to gemma2:2b, and always reports the local Ollama engine as active even when the fallback path will be used.

Static checks: Pylance reported missing imports for streamlit and ollama in the selected interpreter, while pandas and pydantic resolved. This is an environment/dependency installation issue rather than a Python syntax issue; requirements.txt declares both packages. Runtime fallback probe passed for the three bundled samples.

Challenge context: Hacktoberfest Open-Source AI Challenge: Week 2, theme Side Hustle, asks for open-weight/open-source AI that someone would actually pay for. Judging emphasizes writing quality, theme relevance, creativity, technical execution, and optional partner technology. Submission requires a DEV post using the #hf26challenge tag; the challenge context says October 18 at 11:59 PM PDT, and saving/embedding an agent session is optional but helpful.

2 of 2 messages

(Link to session: https://dev.to/agent_sessions/sift-review-offline-hinglish-dm-to-dispatch-engine-opp4cu
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Prize Categories

  • Best Use of Gemma: Sift integrates Google's open-weight gemma2:2b model locally via Ollama alongside Pydantic structured outputs to extract Hinglish order entities on consumer laptop CPUs with minimal RAM overhead.
  • Best Use of Entire: Shared the full end-to-end architectural planning, debugging, and local deployment agent session behind Sift using DevRelay / Entire.
  • Best Use of GitHub Copilot: Utilized GitHub Copilot inside VS Code to accelerate building the Pydantic validation schema, regex cloud-demo fallback, and SQLite database CRUD operations.

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