Movie Reviews Model

Code instructions: Instructions for creating code to submit for the Movie Reviews Sentiment Analysis challenge.

Quick Start
Introduction
Requirements
Project Structure
Detailed Tutorial
Submission Guidelines
License

Quick Start

# 1. Install dependencies (Do not add any other libraries to requirments.txt file)
pip install -r requirements.txt

# 2. Start developing your solution. Follow the tutorial below to implement your AI model

# 3. Run demo to test implement
python demo.py

Note: You need to implement your movie reviews solution following the tutorial guide.

Introduction

The Movie Reviews Model Challenge is a beginner-friendly introduction to machine learning, teaching you how to classify movie reviews. It’s the perfect hands-on way to master text analysis and build your first NLP model!

AIOZ Movie Reviews Challenge

In this challenge, participants will be provided with:

Model Access: movie_reviews_model.
- Code baseline to develop solutions
- Predefined libraries and tools in requirements.txt (Do not add any other libraries to requirments.txt file)
Dataset Access: movie_reviews_data.
- Training dataset to train your AI models
- Testing dataset to predict labels for generating the submission file

Goal: It is your job to classify the movie reviews is positive or negative.

Requirements

System Requirements

Python 3.10+

Dependencies

Install all required packages:

pip install -r requirements.txt

Project Structure

Your AI library should follow this structure:

repository/
├── my_ai_lib/                   # Your AI library
│   ├── __init__.py              # Required: Library initialization
│   ├── run.py                   # Required: Main workflow function
│   ├── predict_submission.py    # Required: Generate predictions for challenge submission.
│   └── [your_modules]/          # Your custom modules
├── models/                      # Model weights directory
├── demo.py                      # Demo script
├── requirements.txt             # Dependencies
└── README.md                    # Documentation

Description

The input and output of the AI model are defined in my_ai_lib/run.py.
The AI model is designed to process a single input item.
In my_ai_lib/predict_submission.py, we process all items in the test dataset and save the results to result.csv in the required submission format.
Please refer to the instructions below for more details.

Key Components

Component	Description	Status
`my_ai_lib/`	Core AI library directory	Required
`my_ai_lib/__init__.py`	Library initialization	Required
`my_ai_lib/predict_submission.py`	Generate predictions for challenge submission.	Required
`my_ai_lib/run.py`	Main AI workflow	Required
`demo.py`	Demo and testing script	Required

Detailed Tutorial

Step 1: Initialize Your AI Library

1.1 Define `my_ai_lib/init.py`

from .run import run

This file defines that the run() function in run.py as an attribute of the my_ai_lib and that I can call it by my_ai_lib.run()

1.2 Define Input/Output Objects in `my_ai_lib/run.py`

Create your custom input and output classes:

from pathlib import Path
from typing import Any, Union, Literal
from aioz_ainode_adapter.schemas import InputObject, OutputObject, FileObject


class MyInput(InputObject):
    input_image: str
    example_param: Any


class MyOutput(OutputObject):
    text: str
    output_image: FileObject

Step 2: Understanding AIOZ Schema Objects

The aioz_ainode_adapter library defines 3 core object types based on pydantic.BaseModel:

🔸 InputObject

Define the format for input when the AIOZ-AI-Node system sends to your AI library.

Default Parameters:

Parameter	Type	Description
`device`	Choice	Device for your model: `["cuda", "cpu"]`
`model_storage_directory`	String	Directory containing model weights

Important : Always use model_storage_directory for model weight paths, as AIOZ-AI-Node will specify this location.

🔸 OutputObject

Define the format for output when your AI library sends to the AIOZ-AI-Node system.

🔸 FileObject

Define the format for the file, if your output has a file. This object has two fields:

Field	Type	Description
`data`	Choice	File data: `io.BufferedReader`, `Path`, or `URL`
`name`	String	File name

Example FileObject creation:

output_file = FileObject(data=open("file/path.csv", "rb"), name="output.csv")

Note:

If your input param have a file, it must be a local file path or URL

If your output has a file, it must be a FileObject

Step 3: Implement the Main Workflow

3.1 Define Your AI Task Function

def do_ai_task(
        input_image: Union[str, Path],
        example_param: Any,
        model_storage_directory: Union[str, Path],
        device: Literal["cpu", "cuda"] = "cpu",
        *args, **kwargs) -> Any:
    """
    Define AI task: load model, pre-process, post-process, etc.
    
    Args:
        input_image: Path to input image
        example_param: Example parameter
        model_storage_directory: Directory containing model weights
        device:
        
    Returns:
        Result
    """
    # Define your AI task workflow. Below is an example
    text = "This is the AI task result"
    output_image = open("wiki/aioz.png", "rb")  # io.BufferedReader
    return text, output_image

3.2 Implement the Required `run()` Function

def run(input_obj: InputObject) -> OutputObject:
    """
    Main entry point for your AI library.
    
    Args:
        input_obj: Input object containing all parameters
        
    Returns:
        OutputObject: Results of AI processing
    """
    # Validate and parse input
    my_input = MyInput.model_validate(input_obj.model_dump())
    print(f"Input: {my_input}")

    # Execute AI task
    text, output_image = do_ai_task(
        input_image=my_input.input_image,
        example_param=my_input.example_param,
        model_storage_directory=my_input.model_storage_directory,
        device=my_input.device
    )

    # Create output object
    output_file = FileObject(data=output_image, name="output_image.png")
    output_obj = MyOutput(text=text, output_image=output_file)
    return output_obj

Critical: The run() function name is mandatory and cannot be changed. The do_ai_task() function can be renamed and customized.

Step 4: Create Demo Script

Create demo.py to test your implementation:

import my_ai_lib
from aioz_ainode_adapter.schemas import InputObject


def main():
    """Demo function to test your AI library."""
    input_obj = InputObject(
        input_image="wiki/aioz.png", 
        example_param="example"
    )
    output_obj = my_ai_lib.run(input_obj)
    print(f"Output: {output_obj}")


if __name__ == '__main__':
    main()

The my_ai_lib.run() function receives an InputObject and returns an OutputObject.

Expected console output:

Input: type='InputObj' device='cuda' model_storage_directory='models' input_image='wiki/aioz.png' example_param='example'
Output: type='OutputObj' text='This is the AI task result' output_image=FileObject(type='FileObj', data=<_io.BufferedReader name='wiki/aioz.png'>, name='output_image.png')

Step 5: Add Model Weights

Place your trained model files in the models/ directory:

models/
├── model.pth          # Your trained model
├── config.json        # Model configuration
└── etc.

Step 6: Create Prediction Script (For Submission)

Implement the predict_submission() function in my_ai_lib/predict_submission.py:

Requirements:

Function accepting test data folder path (string)
Load your trained model
Process test dataset (test.csv in test data folder)
Generate predictions
Save results as ./result.csv

Implementation Template:

from aioz_ainode_adapter.schemas import InputObject
import my_ai_lib
def predict_submission(test_data_folder: str):
    """
    Generate predictions for challenge submission.
    
    Args:
        test_data_folder: Path to test data directory
    """
    # Load your model
    # Process test data
    # Generate predictions
    # Save to ./result.csv

    # Example:
    # Find test data (test.csv) in test data folder (using os.walk)
    # Loop with each review:
    # - You define InputObject
    #     input_obj = InputObject(
    #     input_text=str(review), 
    # )
    # - Output (sentiment is predicted from your model): output = my_ai_lib.run(input_obj)
    # - Write the predicted sentiment (output.text) to result.csv
    pass


def main():
    """Main function for testing submission."""
    predict_submission("path/to/test/data")


if __name__ == '__main__':
    main()

Important: The result.csv must match the challenge's sample submission format.

Verify Your Submission:

python -m my_ai_lib.predict_submission

Submission Guidelines

Submission format

The submission file has two field:

review_index: The unique identifier/index corresponding to each review
sentiment: The predicted sentiment associated with the review

Example:

	review_index, sentiment
	12345,negative
	12346,positive

License

This repository is licensed under the MIT License.

All Challenges

Movie Reviews Sentiment Analysis Challenge

Movie Reviews Model

Table of Contents

Quick Start

Introduction

AIOZ Movie Reviews Challenge

Requirements

System Requirements

Dependencies

Project Structure

Description

Key Components

Detailed Tutorial

Step 1: Initialize Your AI Library

1.1 Define `my_ai_lib/init.py`

1.2 Define Input/Output Objects in `my_ai_lib/run.py`

Step 2: Understanding AIOZ Schema Objects

🔸 InputObject

🔸 OutputObject

🔸 FileObject

Step 3: Implement the Main Workflow

3.1 Define Your AI Task Function

3.2 Implement the Required `run()` Function

Step 4: Create Demo Script

Step 5: Add Model Weights

Step 6: Create Prediction Script (For Submission)

Requirements:

Implementation Template:

Verify Your Submission:

Submission Guidelines

Submission format

License

AIOZAI

Movie Reviews Sentiment Analysis Challenge

All Challenges

Movie Reviews Sentiment Analysis Challenge

Movie Reviews Model

Table of Contents

Quick Start

Introduction

AIOZ Movie Reviews Challenge

Requirements

System Requirements

Dependencies

Project Structure

Description

Key Components

Detailed Tutorial

Step 1: Initialize Your AI Library

1.1 Define my_ai_lib/__init__.py

1.2 Define Input/Output Objects in my_ai_lib/run.py

Step 2: Understanding AIOZ Schema Objects

🔸 InputObject

🔸 OutputObject

🔸 FileObject

Step 3: Implement the Main Workflow

3.1 Define Your AI Task Function

3.2 Implement the Required run() Function

Step 4: Create Demo Script

Step 5: Add Model Weights

Step 6: Create Prediction Script (For Submission)

Requirements:

Implementation Template:

Verify Your Submission:

Submission Guidelines

Submission format

License

AIOZAI

Movie Reviews Sentiment Analysis Challenge

1.1 Define `my_ai_lib/init.py`

1.2 Define Input/Output Objects in `my_ai_lib/run.py`

3.2 Implement the Required `run()` Function