Inbox Analytics - You’ve Got Mail… and Dashboards! 📊📬

From 📩 to 📈: Automating Email Reports into Visual Insights

What I Built

Inbox-analytics is a smart email-based reporting dashboard that processes inbound emails with CSV or Excel attachments (like sales reports or customer data). It automatically parses and visualizes these attachments into an interactive, analytics-rich dashboard—no manual downloads or spreadsheets required.

This tool helps teams instantly generate actionable insights from emailed reports using AI, charts, summaries, and anomaly detection.

🧠 Key Features:
Postmark webhook integration for real-time attachment ingestion

Automatic parsing of CSV and XLSX files

Interactive dashboards with visualizations (Plotly, Streamlit)

AI-powered insights using GPT

No manual downloads or Excel wrangling needed
**

Demo

Live App:
Dashboard:https://inbox-analytics-engine-dasboard.onrender.com
Backend: https://inbox-analytics-engine.onrender.com

🧪 Run Locally:

1. Clone the repository:
   git clone https://github.com/maninagulapati/Inbox-Analytics-Engine.git
   cd Inbox-Analytics-Engine

2. Set up a virtual environment and install dependencies:
   python -m venv venv
   source venv/bin/activate # Windows: venv\Scripts\activate
   pip install -r requirements.txt

3. Start the backend FastAPI server:
   uvicorn backend.main:app --reload

4. Run the Streamlit dashboard:
   streamlit run dashboard/app.py
   Point your Postmark inbound webhook to:
   http://localhost:8000/inbound
   Send an email with a .csv or .xlsx attachment to your Postmark inbound address.

Open the Streamlit dashboard to view parsed data, charts, and AI insights.

📂 Sample reports for testing are available in the data/uploads/ folder.

Code Repository

https://github.com/maninagulapati/Inbox-Analytics-Engine.git

How I Built It

Backend: FastAPI handles webhook requests and parses incoming attachments using pandas, openpyxl, and csv.

Frontend: Streamlit displays interactive dashboards with charts from plotly, summaries using numpy, and insights via GPT.

AI Insights: GPT-generated summaries help non-technical users understand trends and anomalies.

File Management: Incoming files are organized by sender and subject for structured, secure access.

Postmark Integration: Simple and reliable inbound parsing made possible with Postmark’s developer-friendly webhooks.

🔧 Tech Stack:
Python, FastAPI, Streamlit

📊 Pandas, Plotly, NumPy

📩 Postmark Inbound Webhooks

🧠 OpenAI GPT (for summaries & recommendations)

🗂️ CSV/XLSX parsing with openpyxl and csv modules

Uploaded dashboard sample images at https://github.com/maninagulapati/Inbox-Analytics-Engine/tree/master/Dashboard_sample_images

Comments

[JOHN RATNA KUMAR AKKISETTI]

Great Dashboard