Hugging Face's logo

Spaces:
DeepRat
/
TrueEye_Reports
Paused

App Files Community
TrueEye_Reports
File size: 5,227 Bytes 
ce81a00
 
 
 
 
 
 
c336a9a
ce81a00
f259c81
8735561
f259c81
ce81a00
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
c403f50
ce81a00
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
 
---
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)

---