app.py

import gradio as gr
import random
from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification

# Models included within the interface
models = ["bert-base-uncased", "roberta-base"]

# Datasets included within the interface
datasets = ["No Dataset Finetuning",
            "vedantgaur/GPTOutputs-MWP - AI Data Only",
            "vedantgaur/GPTOutputs-MWP - Human Data Only",
            "vedantgaur/GPTOutputs-MWP - Both AI and Human Data",
            "dmitva/human_ai_generated_text - Both AI and Human Data"]

# Mapping of user-selected model and dataset to actual model name on Hugging Face
model_mapping = {
    ("bert-base-uncased", "No Dataset Finetuning"): "bert-base-uncased",
    ("bert-base-uncased", "vedantgaur/GPTOutputs-MWP - AI Data Only"): "SkwarczynskiP/bert-base-uncased-finetuned-vedantgaur-AI-generated",
    ("bert-base-uncased", "vedantgaur/GPTOutputs-MWP - Human Data Only"): "SkwarczynskiP/bert-base-uncased-finetuned-vedantgaur-human-generated",
    ("bert-base-uncased", "vedantgaur/GPTOutputs-MWP - Both AI and Human Data"): "SkwarczynskiP/bert-base-uncased-finetuned-vedantgaur-AI-and-human-generated",
    ("bert-base-uncased", "dmitva/human_ai_generated_text - Both AI and Human Data"): "SkwarczynskiP/bert-base-uncased-finetuned-dmitva-AI-and-human-generated",
    ("roberta-base", "No Dataset Finetuning"): "roberta-base",
    ("roberta-base", "vedantgaur/GPTOutputs-MWP - AI Data Only"): "SkwarczynskiP/roberta-base-finetuned-vedantgaur-AI-generated",
    ("roberta-base", "vedantgaur/GPTOutputs-MWP - Human Data Only"): "SkwarczynskiP/roberta-base-finetuned-vedantgaur-human-generated",
    ("roberta-base", "vedantgaur/GPTOutputs-MWP - Both AI and Human Data"): "SkwarczynskiP/roberta-base-finetuned-vedantgaur-AI-and-human-generated",
    ("roberta-base", "dmitva/human_ai_generated_text - Both AI and Human Data"): "SkwarczynskiP/roberta-base-finetuned-dmitva-AI-and-human-generated"
}

# Example text included within the interface
exampleText = [
    "ex1",
    "ex2",
    "ex3",
    "ex4"
]

# Example models and datasets included within the interface
exampleModels = ["bert-base-uncased", "roberta-base"]

# Example datasets included within the interface
exampleDatasets = ["No Dataset Finetuning",
            "vedantgaur/GPTOutputs-MWP - AI Data Only",
            "vedantgaur/GPTOutputs-MWP - Human Data Only",
            "vedantgaur/GPTOutputs-MWP - Both AI and Human Data",
            "dmitva/human_ai_generated_text - Both AI and Human Data"]

examples = [[random.choice(exampleModels), random.choice(exampleDatasets), random.choice(exampleText)] for example in exampleText]


def detect_ai_generated_text(model: str, dataset: str, text: str) -> dict:
    # Get the fine-tuned model using mapping
    finetuned_model = model_mapping.get((model, dataset))

    # Load the specific fine-tuned model
    tokenizer = AutoTokenizer.from_pretrained(finetuned_model)
    model = AutoModelForSequenceClassification.from_pretrained(finetuned_model)

    # Classify the input based on the fine-tuned model
    classifier = pipeline('text-classification', model=model, tokenizer=tokenizer)
    result = classifier(text)

    # Get the label and score
    label = "AI-generated" if result[0]['label'] == 'LABEL_1' else "Human-written"
    score = result[0]['score']

    # Create HTML for the colored bars
    ai_score = score if label == "AI-generated" else 1 - score
    human_score = 1 - ai_score
    ai_bar = f'<div style="background-color: red; width: {ai_score * 100}%; height: 20px;"></div>'
    human_bar = f'<div style="background-color: blue; width: {human_score * 100}%; height: 20px;"></div>'

    return {"label": f"{label} with confidence {score * 100:.2f}%", "ai_bar": ai_bar, "human_bar": human_bar}


interface = gr.Interface(
    fn=detect_ai_generated_text,
    inputs=[
        gr.Dropdown(choices=models, label="Model"),
        gr.Dropdown(choices=datasets, label="Dataset"),
        gr.Textbox(lines=5, label="Input Text")
    ],
    outputs=[
        gr.Label(label="Output"),
        gr.HTML(label="AI-generated"),
        gr.HTML(label="Human-written")
    ],
    examples=examples,
    title="AI Generated Text Detection"
)

if __name__ == "__main__":
    interface.launch()