Hypothesis Forge

An AI-powered tool that automatically generates and tests hypotheses on data, providing actionable insights through statistical analysis.

Overview

Hypothesis Forge analyzes your data and generates hypotheses that you can test. It then automatically tests them and provides detailed results with statistical significance.

flowchart TD
    A[Data Input] --> B[Hypothesis Generation]
    B --> C[Statistical Testing]
    C --> D[Results Synthesis]

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Features

• Automated Hypothesis Generation: Creates relevant hypotheses based on data context and audience
• Statistical Testing:
  • Automatic selection of appropriate statistical tests
  • Support for t-tests, chi-square, correlation significance tests
  • P-value calculation and interpretation
• Interactive Interface:
  • Real-time hypothesis testing
  • Dynamic results visualization
  • Dark mode support
  • Mobile-responsive design
  • "Run All" feature to test multiple hypotheses at once
  • Result synthesis for actionable insights
• Multiple Data Formats:
  • CSV files
  • SQLite databases (.sqlite3, .sqlite, .db, .s3db, .sl3)
  • Support for various data types (numeric, categorical, temporal)

Usage

1. Select a dataset from the available demos
2. The application will:
   • Load and analyze the data
   • Generate relevant hypotheses
   • Display hypotheses with test buttons
3. For each hypothesis:
   • Click "Test" to run statistical analysis
   • View detailed results and interpretation
   • See p-values and statistical significance
4. After testing hypotheses:
   • Click "Synthesize" to get actionable insights
   • Use "Reset" to clear results and start over

Setup

Prerequisites

• Modern web browser with JavaScript enabled
• LLM Foundry account for API access
• Internet connection for loading dependencies

Local Setup

1. Clone this repository:

git clone https://github.com/gramener/hypoforge.git
cd hypoforge

2. Serve the files using a static server:

uvx https://raw.githubusercontent.com/sanand0/staticauth/main/app.py

3. Open http://localhost:8000 in your browser
4. Log in when prompted with your LLM Foundry credentials

Technical Details

Dependencies

• asyncLLM - API interaction with LLM endpoints
• d3.js - Data visualization and CSV parsing
• Bootstrap - UI framework and styling
• Bootstrap Icons - Icon system
• Pyodide - Python runtime for browser
• Marked - Markdown parsing
• Highlight.js - Code syntax highlighting
• sqlite-wasm - SQLite database support in browser
• partial-json - JSON parsing

License

MIT