DEV Community: SRIRAM S

I Built an Offline “Sales Analyst” for Small Shops with Ollama and DuckDB

SRIRAM S — Sat, 17 Oct 2026 12:28:06 +0000

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

What I Built

AskMyShop is a local app that lets a small shop or restaurant owner ask questions about their sales in plain language and get a chart plus a one-line answer.

Many small businesses keep sales in Excel or CSV exports from a billing app, but can’t afford an analyst. They’re left asking questions like “which dish is not selling?” or “which day is my busiest?” and guessing the answers.

With AskMyShop, the owner uploads their sheet, types a question (e.g. “Which items sold least last month?”), and gets:

a chart
a one-line insight in simple words
the SQL used, so they can see how the answer was found

Who would pay: small restaurants, tiffin shops and grocery stores. Pricing idea: [₹499 one-time setup, or ₹199/month for monthly report templates].

Demo

Tip: Show the Wi-Fi turned off in the video. It proves the “private and offline” claim in two seconds.

Code

AWT-SRIRAM / SRM_AUTOSUB_BOT

🎬 SRM AUTOSUB BOT

An asynchronous, GPU-accelerated Telegram Bot engineered using Pyrogram and faster-whisper (distil-large-v3). Automatically extracts English audio streams, transcribes multi-channel media, and generates mobile-optimized, styled Advanced SubStation Alpha (.ass) English subtitles.

🔗 Quick Links

🚀 Key Features

⚡ High-Performance ASR Engine

distil-large-v3 Architecture: Utilises quantized distil-large-v3 running on CUDA float16 hardware acceleration, achieving 5x–12x real-time transcription performance.
Word-Level Timestamping: Generates exact word‑level timestamp alignments and applies centisecond rounding (round()‑based) to eliminate IEEE 754 drift on long files.
FFmpeg Audio Extraction: Automatically probes multi‑track containers (.mkv, .mp4, .avi), prefers explicit English streams, and converts raw Audio to 16kHz mono PCM WAV with a 3x volume boost to capture quiet dialogue.

🎨 Styled .ASS Output (Custom Look)

Embedded Styling: Outputs Advanced SubStation Alpha (.ass) files with a pre‑defined style – Trebuchet MS Italic…

View on GitHub

How I Built It
Model: [Qwen2.5 7B / Qwen2.5-Coder] running locally through Ollama
Query engine: DuckDB, which queries the uploaded CSV/Excel directly with no database setup
Structured output: Instructor with a schema, so the model returns the SQL and the chart type in a predictable format
UI and charts: Streamlit with Plotly
Safety layer: only read-only SELECT queries are allowed, and if the SQL fails, the error is sent back to the model for one retry
Prompt design: the prompt includes the table schema, sample rows, and a few example questions with correct SQL, tuned to the shop’s own column names
(Optional) faster-whisper for asking questions by voice

What I learned: [e.g. small models sometimes write wrong SQL, so showing the query under every answer built trust, and schema plus examples in the prompt improved accuracy.]

Why Does Open Innovation Matter?
Privacy: sales and customer data never leave the owner’s laptop. For a small shop, that data is the business.
Cost: there’s no per-question API fee, so a low flat price still leaves a margin. With a paid API, every question the owner asks would eat into a ₹199/month plan.
Works offline: many small shops have unreliable internet, and this works without it.
Customization: I could tune the prompt with the shop’s own column names and item names (including Tamil names), which a generic tool handles poorly.
No lock-in: if a better open model comes out, or the shop has an older laptop, I can swap the model without rewriting the app.

[Add one honest comparison here, e.g. “I tried the same questions on a hosted model: accuracy was [X], but it required uploading the data and costs [Y] per query.”]

My First User

[Who they are: a shop owner, a relative who runs a business, a tea stall, a hotel.]

What they asked first:
What worked / what confused them:
Would they pay, and how much:
What they asked for next:

If you haven’t shown it to anyone by the deadline, delete this section.

Bird.king

SRIRAM S — Sat, 10 Oct 2026 17:17:16 +0000

the BirdNET*This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass*

What I Built

TrailCall is an offline bird-call companion for walks. You record a short clip on the trail, and it identifies the birds singing around you, then turns the detections into a short field note and adds them to your personal life list.

The screen is the shortest part of the experience: tap record, put the device away, and keep walking. The detail only appears once you're back or at a rest stop. The goal is to make people look up and listen instead of scrolling.

It's for beginner birders, students, and anyone who hears a call and wonders "what is that?", especially in places with weak or no signal.

Demo

[TODO: video link or screenshots]

Field test: [TODO: where you went, how long, how many species it identified, what it got wrong. Write this from your real outing.]

Code

[TODO: embed your GitHub repo]

How I Built It

Detection: BirdNET-Analyzer, an open-source bird sound classifier, runs fully locally on audio clips in Python.
Field notes: a small open-weight model (e.g. Qwen2.5 or Llama 3.2 via Ollama) turns the raw detections (species, confidence, time) into a readable note with a short, plain-language fact about each bird.
Storage: a local SQLite life list with date, species, and confidence.
Interface: a minimal Streamlit page (or CLI) with a record/upload button and a life-list view.
Flow: record clip → BirdNET detections → confidence filter → local LLM writes the note → save to SQLite.

Everything runs on a laptop with no internet connection.

Why Does Open Innovation Matter?

Works where there's no signal. A cloud API is useless on a trail. Local models mean it works exactly where birds are.
Your recordings and locations stay with you. Sightings and GPS context are sensitive, and nothing is uploaded to a server
Swappable and tunable. I can swap the language model, change the note style, or later fine-tune on local species without waiting on a vendor.
Licensing note: BirdNET's model weights are released under a non-commercial license, so this is a learning and community tool, not a commercial product.

My Agent Session

[TODO: DevRelay link or embed, or remove this section]

Prize Categories

[TODO: list the partner categories you're entering, or remove this section]

Student Career Hub: a career tracker I built for [friend's name], who had no single place to plan their path

SRIRAM S — Sun, 04 Oct 2026 11:04:50 +0000

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend

What I Built

[Friend's name] is a final-year student who is [applying for jobs / unsure which career to pick / juggling applications in notes and chats]. Student Career Hub puts it all in one place:

Career exploration to see what roles exist and what they need
Internship and job tracking so no application gets lost
Skill roadmaps that show what to learn next
Resume studio to build a resume
Interview prep to practise before the real thing

[Add 2 to 3 personal sentences: what [friend] was struggling with and what you noticed. This is the part judges read most closely.]

Demo

Live site: https://student-carrer-seven.vercel.app

Code

https://github.com/tgbsri2006-alt/STUDENT_CARRER

How I Built It

The app is built with Next.js and deployed on Vercel.

Open-source AI: AI is actually in the app, for example an open-weight model through Ollama powering interview prep or resume feedback. Say how the app talks to it and why you chose that model.

Why Does Open Innovation Matter?

[Friend]'s resume, applications and interview answers are personal career data. open-source AI lets you do here: keep that data off a closed API, cost nothing to run, or swap the model without rewriting the app.