Trigger Recognition for Enjoyable and Appropriate Television - R1

I was tired of getting grossed out watching unexpected scenes in movies and TV and losing my appetite, that's why I created TREAT.

The goal of this project is to empower viewers by forewarning them about potential triggers in the content they watch, making the viewing experience more enjoyable, inclusive, and appropriate for everyone.

TREAT is a web application that uses natural language processing to analyze movie and TV show scripts, identifying potential triggers to help viewers make informed choices.

Installation Instructions

Prerequisites

• Star the Repository to Show Your Support :P

• Clone the Repository to Your Local Machine:

  git clone https://github.com/Kuberwastaken/TREAT-R1.git

Hugging Face Login Instructions for DeepSeek-R1 1.5B Model

We will use the DeepSeek-R1 1.5B Model, which provides a 15% increase in accuracy and 35% boost in efficiency over the previous model

1. Login to Hugging Face in Your Environment:

   Run the following command in your terminal:

   huggingface-cli login

   Enter your Hugging Face access token when prompted.

2. Download the DeepSeek-R1 1.5B Model Model:

   The model will be downloaded automatically when running the script analysis for the first time, provided you have received access.

Environment Setup

To set up the development environment, you will need to create a virtual environment and install the necessary dependencies.

1. Create a Virtual Environment:

   python3 -m venv treat-r1

2. Activate the Virtual Environment:

   source treat-env/bin/activate   # On Unix or MacOS
   treat-env\Scripts�ctivate      # On Windows

3. Install Dependencies:

   Navigate to the project directory and run:

   pip install -r requirements.txt

Project Usage

1. Start the Flask Server:

   python run.py

2. Open Your Browser:

   Navigate to http://127.0.0.1:5000 to access the TREAT web interface.

3. Analyze Scripts:

   You can manually enter a script in the provided text area and click "Analyze Script."

File Descriptions

• app.py: The main Flask application file that handles routing.

• app/routes.py: Contains the Flask routes for handling script uploads.

• app/model.py: Includes the script analysis functions using the DeepSeek R1 1.5B model.

• templates/index.html: The main HTML file for the web interface.

• static/css/style.css: Custom CSS for styling the web interface.

• static/js/app.js: JavaScript for handling client-side interactions.

Types of Triggers Detected

The TREAT application focuses on identifying a variety of potential triggers in scripts, including but not limited to:

• Violence: Scenes of physical aggression or harm.

• Self-Harm: Depictions of self-inflicted injury.

• Death: Depictions of death or dying characters.

• Sexual Content: Any depiction or mention of sexual activity, intimacy, or behavior.

• Sexual Abuse: Instances of sexual violence or exploitation.

• Gun Use: Depictions of firearms and their usage.

• Gore: Graphic depiction of injury, blood, or dismemberment.

• Vomit: Depictions of vomiting or nausea-inducing content.

• Mental Health Issues: Depictions of mental health struggles, including anxiety, depression, or disorders.

• Animal Cruelty: Depictions of harm or abuse towards animals.

These categories help address a very real-world problem by forewarning viewers about potentially distressing content, enhancing their viewing experience.

Adding new categories is as simple as specifying a new category under model.py and utils.py

Design Choices

• Inspiration: I aimed for a simple and intuitive user experience, focusing on simplicity and ease of use. This decision stemmed from the need to create a tool that is easy to navigate for all users, regardless of background or age.

• Theme and Color Scheme: The chosen theme and color scheme create a visually appealing and engaging environment. The chocolate and sweets theme is intended to stick to the TREAT theme and make the experience enjoyable and pleasant.

• Script Analysis: The DeepSeek R1 by deepseek AI was chosen for its increased accuracy (about 15%) and 35% better efficiency compared to the prior Llama 3.2 1B Version. The decision was based on its ability to provide precise trigger recognition and ability to analyze triggers for large chunks of text while being open source. As a new and advanced model, it enhances the script analysis capabilities significantly.

Model Configuration Guide

Core Configuration Parameters ⚙️

1. Text Chunking Settings

# Located in analyze_script()
max_chunk_size = 1024  # Text segment length (tokens)
overlap = 128           # Context preservation between chunks

Recommended Adjustments:

• For long dialogues:

  max_chunk_size = 1536
  overlap = 256

• For action-heavy scripts:

  max_chunk_size = 768
  overlap = 64

2. Generation Controls

# In model.generate() parameters
{
    "temperature": 0.2,          # Range: 0.1 (strict) - 9.0 (creative)
    "top_p": 0.9,                # Range: 0.8 (focused) - 1.0 (diverse)
    "repetition_penalty": 1.05   # Range: 1.0 (none) - 2.0 (strict)
}

Use Case Examples:

• For sensitive content analysis:

  {"temperature": 0.1, "top_p": 0.8, "repetition_penalty": 1.2}

• For creative interpretation:

  {"temperature": 0.7, "top_p": 0.95, "repetition_penalty": 1.0}

3. Prompt Structure

prompt = f"""TEXT ANALYSIS:
Respond ONLY with this exact format:

VIOLENCE: [YES/NO]
...
MENTAL_HEALTH: [YES/NO]

Text: {chunk[:768]}..."""

Customization Guide:

• Maintain Respond ONLY... directive.
• Keep category list order consistent.
• Preserve ... after the text preview.

4. Response Parsing Logic

# Category normalization
category_map = {
    cat.upper().replace("_", " "): cat 
    for cat in expected_order
}

# Answer recognition pattern
pattern = r"\b({})\b\s*[:=]\s*\[?(YES|NO|MAYBE|Y|N|M)\]?".format(
    "|".join(re.escape(cat) for cat in category_map.keys())
)

Configuration Reference Table 📋

Parameter	Location	Default	Effect Range
max_chunk_size	analyze_script()	1024	512-2048
overlap	analyze_script()	128	32-256
temperature	model.generate()	0.2	0.1-1.0
top_p	model.generate()	0.9	0.7-1.0
repetition_penalty	model.generate()	1.05	1.0-2.0

Advanced Customization 🛠️

Adding New Categories

1. Add to expected_order list:

   expected_order = [
       ...,
       "NEW_CATEGORY"
   ]

2. Update the prompt template format section.
3. Test parsing with:

   # Test pattern with new category
   test_text = "NEW_CATEGORY: YES"
   assert "NEW_CATEGORY" in extract_answers(test_text, expected_order)

Modifying Response Format

Edit the regex pattern for different answer formats:

# Example: Allow 'Y'/'N' shorthand
pattern = r"\b({})\b\s*[:=]\s*\[?(Y|N)\]?".format(...)

Optimizing Performance

• Reduce VRAM usage:

  max_chunk_size = 768
  overlap = 64

• Faster processing (less accurate):

  {"temperature": 0.1, "top_p": 0.8}
  
  
  

To-Do List

• Fixing the model working but not the output

• Parallel Processing of Multiple chunks

• Replacing Llama model and using this as the main model for TREAT

Acknowledgements

I would like to thank:

• DeepSeek AI : For developing and allowing open access to the DeepSeek R1 model, a very critical component of this project.

• Parasite (2019): For that unexpected jumpscare that ruined my appetite and ultimately inspired this project.