README.md

metadata

license: creativeml-openrail-m
title: TrueEye Reports
sdk: docker
emoji: 👁
colorFrom: yellow
colorTo: purple
short_description: Analyze News - lies, bias, intentionality and more!

🧿 TrueEye — Intelligent Media Literacy System

TrueEye is an AI-powered tool designed to analyze news articles and web content to detect narrative bias, identify the target audience, and reveal hidden intentions or manipulative rhetorical structures. In other words, it doesn’t just detect fake news — it analyzes who the content is written for and why. The system generates a detailed report to support media literacy, highlighting subtle signals embedded in the text.

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🚀 Demo

• 🌐 Try TrueEye on Hugging Face Spaces
• 🖥️ Official project site

Note: The demo requires internet access and may prompt you to log in to Hugging Face.

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🧩 What Does TrueEye Do?

When given a news article URL, TrueEye performs three consecutive analyses:

📊 Bias & Narrative Tone

• Detects narrative polarity (positive, negative, neutral).
• Identifies rhetorical strategies (fear, polarization, irony).
• Summarizes the content and flags questionable claims.

🎯 Audience Profiling

• Infers demographic and emotional profile of the target reader.
• Identifies values, beliefs, or cognitive biases being exploited.

⚠️ Intent & Risk Evaluation

• Detects manipulative discourse or symbolic violence.
• Highlights hidden agendas, information gaps, and potential societal risk.

The report includes links to trustworthy sources for fact-checking.

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⚙️ Architecture Overview

TrueEye consists of three main components:

• 🧱 Frontend: Static web interface built with HTML, TailwindCSS, and JavaScript (static/index.html).
• 🧠 Backend: REST API written in Python using FastAPI (main.py).
• 🔁 AI Orchestration: LangFlow flow (TrueEyeBeta.json) powered by Claude models (Opus / Sonnet).

The heavy analysis is performed by external LLMs through LangFlow API calls.

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📁 Project Structure

TrueEye_v1/
├── static/
│   ├── index.html        # Frontend UI
│   └── te.png            # Project logo
├── main.py               # FastAPI backend
├── requirements.txt      # Python dependencies
├── Dockerfile            # Deployment config (Hugging Face Spaces)
├── TrueEyeBeta.json      # LangFlow pipeline (AI logic)

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💻 How to Run It Locally

🔧 Requirements

• ✅ Python 3.10+
• ✅ Internet access (to connect with AI APIs)
• ✅ Claude API key or other compatible LLM provider
• ✅ LangFlow installed (pip install langflow)

💡 No GPU or specialized hardware needed — all heavy lifting is done remotely.

🧪 Installation Steps:

# 1. Clone the repository
git clone https://github.com/DeepRatAI/TrueEye_v1.git
cd TrueEye_v1

# 2. Install backend dependencies
pip install -r requirements.txt

# 3. Set the LangFlow API URL
export FLOW_API_URL="http://localhost:7860/predict"  # Adjust to your LangFlow instance

# 4. Start the FastAPI backend
uvicorn main:app --reload

Once the server is running, open the file static/index.html in your browser. Paste a news article URL, click "Analyze", and you'll receive an AI-generated report.

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📌 Roadmap

Version	Status	Description
✅ v1.0	Production	Full text analysis with explainable AI (current version)
🔄 v2.0	In design	"TrueEye Chat": interactive conversation with persistent memory
🖼️ v3.0	Planned	Multimodal reasoning (text + images/audio/video)
🧪 v4.0	Planned	Deepfake and synthetic content detection

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📚 Technologies Used

• FastAPI — Python web framework for REST APIs.
• LangFlow — Flow-based orchestration of LLMs and tools.
• Claude (Opus / Sonnet) — Large language models via Anthropic API.
• TailwindCSS & JS — Frontend interface styling and logic.
• Docker — Deployment (e.g. Hugging Face Spaces using provided Dockerfile).

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✍️ Author

Gonzalo Romero (DeepRat) AI, Software & Systems Engineer · Prompt Engineer · Full-Stack Developer

🔗 Web | Hugging Face | GitHub | LinkedIn | Medium

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🧠 License

This project is licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. You are free to share and adapt the code as long as you credit the author (DeepRat) and do not use it for commercial purposes without permission.

For commercial use or extended licensing, please contact: info@deeprat.tech

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