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| license: creativeml-openrail-m | |
| title: TrueEye Reports | |
| sdk: docker | |
| emoji: 👁 | |
| colorFrom: yellow | |
| colorTo: purple | |
| short_description: Analyze News - lies, bias, intentionality and more! | |
| <p align="center"> | |
| <img src="static/banner.gif" alt="Banner TrueEye" width="100%"> | |
| </p> | |
| # 🧿 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. | |
| --- | |
| ## 🚀 Demo | |
| * 🌐 [Try TrueEye on Hugging Face Spaces](https://huggingface.co/spaces/DeepRat/TrueEye_Reports) | |
| * 🖥️ [Official project site](https://trueeye.deeprat.tech) | |
| > Note: The demo requires internet access and may prompt you to log in to Hugging Face. | |
| --- | |
| ## 🧩 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. | |
| --- | |
| ## ⚙️ 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. | |
| --- | |
| ## 📁 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) | |
| ``` | |
| --- | |
| ## 💻 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: | |
| ```bash | |
| # 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. | |
| --- | |
| ## 📌 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 | | |
| --- | |
| ## 📚 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). | |
| --- | |
| ## ✍️ Author | |
| **Gonzalo Romero (DeepRat)** | |
| AI, Software & Systems Engineer · Prompt Engineer · Full-Stack Developer | |
| 🔗 [Web](https://deeprat.tech) | [Hugging Face](https://huggingface.co/DeepRat) | [GitHub](https://github.com/DeepRatAI) | [LinkedIn](https://www.linkedin.com/in/deeprat) | [Medium](https://medium.com/@deeprat) | |
| --- | |
| ## 🧠 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](mailto:info@deeprat.tech) | |
| --- |