inventoryIQ - AI-Powered Retail Forecasting Engine

LA Tech Rising | Spring 2026 | Mentor: Ritesh Verma

A reusable retail forecasting engine that ingests historical sales data, forecasts future demand, flags unusual patterns, and generates plain-English summaries using the Gemini API.

Team

Role	Name	Primary Ownership
Project Lead · Architecture & Infrastructure	Mira Bhakta	System design, model selection, testing suite, CI/CD, deployment, cross-role debugging
Co-Project Lead · Alerting Engine · Forecasting Co-Owner	James Ybarra	models/alerter.py, models/forecaster.py (co-owner)
Data Preparation & Schema Mapping · Dashboard Support	Andrew Garcia Leopold	utils/processor.py - schema mapping + derived fields
Data Preparation (Derived Fields) · AI Insights Support	Krisna Vega	utils/trend.py + derived fields in processor.py
Forecasting Engine · Alerting Co-Owner	Alberto Barboza	models/forecaster.py, models/alerter.py (co-owner)
AI Insights & Reporting · Dashboard Support	Sarah Abdeen	utils/ai_summary.py + AI Summary panel in app.py
Dashboard & Visualization · Integration Lead	Justin Hernandez	app.py — all views, sidebar, filters, layout, loading states

Tech Stack

Backend

• Python
• FastAPI
• Uvicorn
• pandas
• NumPy
• scikit-learn
• LightGBM
• Google Gen AI SDK
• python-dotenv
• openpyxl

Frontend

• React 19
• Vite
• React Router DOM
• Axios
• Recharts

Local Setup

1. Clone the repository

git clone https://github.com/jybarra7/latech_InventoryIQ
cd latech_InventoryIQ

2. Set up the backend

Use Python 3.11 or 3.12. Python 3.14 may fail with older pinned dependencies on Windows.

python -m venv venv

Activate the virtual environment:

Windows PowerShell

venv\Scripts\Activate

macOS/Linux

source venv/bin/activate

Install backend dependencies:

pip install -r requirements.txt

Create a .env file in the project root:

GEMINI_API_KEY=your_api_key_here

Run the backend:

uvicorn main:app --reload

The backend should start at:

• http://localhost:8000
• API docs: http://localhost:8000/docs

3. Set up the frontend

Open a second terminal and run:

cd frontend
npm install
npm run dev

The frontend should start at:

• http://localhost:5173

Running the App

You need two terminals:

1. Backend

uvicorn main:app --reload

2. Frontend

cd frontend
npm run dev

Features

• Upload any retail sales CSV and get an analysis-ready dashboard
• Schema mapping and clean dataset export for downstream pipeline stages
• Demand forecasts for the next 1–3 months with model accuracy comparison
• Automated alert panel flagging anomalies, declining demand, and margin losses
• Plain-English AI summary generated on demand via the Gemini API
• Interactive filters by date range, category, store, and region

Security

Never commit your .env file. It is excluded from version control via .gitignore. If a key is accidentally exposed, revoke it immediately in the Gemini console and generate a new one.