IvánDíazLab

Ask about your data. Explore the answer.

Ask a question. Get safe SQL, charts and a report.

View on GitHubLive demo
Real project capture: AI Data Analyst.
AI Data AnalystReal project capture

FastAPI · Streamlit · Python · SQLAlchemy · Plotly · scikit-learn · OpenAI API

What changes.

Ask in natural language.

The platform generates SQL with read-only validation.

Explore the answer.

Interactive charts, analysis and downloadable reports in one workflow.

Take a closer look.

Real API documentation in Swagger.
Real API documentation in Swagger.
How it was built.Decisions, evidence and details

Stack

FastAPI Streamlit Python SQLAlchemy Plotly scikit-learn OpenAI API
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Objective

Build an end-to-end analytics product where users can upload data, ask business questions in natural language, and automatically receive SQL, analysis, charts, recommendations, and predictive outputs.

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Architecture

  1. Streamlit frontend for dataset upload, natural-language questions, charts, and report downloads.
  2. FastAPI backend exposing query, dataset registration, insight history, and chart endpoints.
  3. Specialized agent system for orchestration, SQL generation, analysis, visualization, and insights.
  4. Data layer compatible with CSV, SQLite, and PostgreSQL.
  5. Machine learning services for forecasting, clustering, and anomaly detection.
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Key capabilities

Natural Language to SQL

Interprets business questions and generates safe read-only SQL queries.

Automated EDA

Builds profiling, descriptive statistics, missing-value reports, correlations, and outlier checks automatically.

Smart visualization

Selects chart types automatically and delivers interactive Plotly outputs.

Prediction and anomalies

Extends analysis with forecasting, clustering, and anomaly detection workflows.

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Screenshots

AI Data Analyst Platform frontend screenshot
Streamlit workspace with natural-language query, analysis, and previewed results.
FastAPI Swagger documentation
Documented API surface in Swagger for queries, datasets, charts, and insights.
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Outcome

This project demonstrates product thinking, backend architecture, LLM integration, visualization, operational security, and VPS deployment. It is a strong flagship piece because it shows the ability to build complete analytics products, not just isolated dashboards.

Your operation can take the next step.

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