truth-ahoy

truth-ahoy is an advanced information verification tool designed to help users quickly assess the authenticity of online content. It can search across multiple web sources, detect deepfake images, perform reverse image searches, and provide detailed insights to separate fact from misinformation. By combining powerful AI analysis with comprehensive web scanning, Truth-Ahoy empowers users to make informed decisions and uncover the truth behind digital media.

Note

• If installing new packages onto the environment, run pip freeze > requirements.txt to update the requirements.txt file.
• To test the Vision tools, enable the Cloud Vision API in the Google Cloud Console, then create a service account and download its key and write the filepath to the .env (default : vision_gcp.json)

Setting Up

1. Create and Activate Virtual Environment

• On Windows:

# Create virtual environment
python -m venv venv

# Activate virtual environment
venv\Scripts\activate

• On Mac/Linux

# Create virtual environment
python3 -m venv venv

# Activate Virtual environment
source venv/bin/activate

• Installing required python libraries

pip install -r requirements.txt

Testing

# run this command at the project root
python backend/main.py

.env

# Conditionals
USE_TAVILY=true
USE_OLLAMA=false

#REDDIT
REDDIT_USERNAME =
REDDIT_PASSWORD =
REDDIT_CLIENT_ID =
REDDIT_SECRET_KEY =
OLLAMA_MODEL=llama3.2:latest
GOOGLE_GENAI_USE_VERTEXAI=FALSE

#TAVILY KEY
TAVILY_API_KEY=

# ADK AGENTS USE THIS
GOOGLE_API_KEY=
GEMINI_MODEL=gemini-1.5-flash

#SPECFICALLY FOR IMAGE ANALYSIS
GEMINI_IMAGE=

# SERVICE ACCOUNT KEY
GOOGLE_CLOUD_SERVICE_KEY=

# BIGQUERY
KEY_PATH = ""
PROJECT_ID = ""
DATASET_ID = ""
TABLE_ID = ""
GENAI_API_KEY =""

#VERTEX AI RAG RETRIEVAL 
GOOGLE_CLOUD_PROJECT=""
BIGQUERY_DATASET=""
BIGQUERY_TABLE=""
CONTENT_COLUMN=""
EMBEDDING_COLUMN=""
METADATA_COLUMNS=""

Citations

@inproceedings{barbaresi-2021-trafilatura,
  title = {{Trafilatura: A Web Scraping Library and Command-Line Tool for Text Discovery and Extraction}},
  author = "Barbaresi, Adrien",
  booktitle = "Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations",
  pages = "122--131",
  publisher = "Association for Computational Linguistics",
  url = "https://aclanthology.org/2021.acl-demo.15",
  year = 2021,}