app.py

###======================== Reference ========================###
# Text Detector Model: https://huggingface.co/fakespot-ai/roberta-base-ai-text-detection-v1
# LLM Model: OpenAI GPT-5 Mini
# Agentic frameworks: LangChain, LangGraph
# UI: Gradio
###===========================================================###

import os
from urllib.parse import uses_query
from dotenv import load_dotenv

from langchain_core.messages import SystemMessage, HumanMessage
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

### For fetching the AI text detector model (from HF)
from transformers import pipeline
import torch

import gradio as gr

### Clean text for better detection performance
from utils import clean_text
from html_blocks import FEATURES_HTML, NOTE_HTML
from css_blocks import CSS_ELEMENTS


load_dotenv()

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
OPENAI_MODEL_ID = "gpt-5-mini"
TEXT_DETECTOR_MODEL_ID = "fakespot-ai/roberta-base-ai-text-detection-v1"
llm = ChatOpenAI(api_key=OPENAI_API_KEY, model=OPENAI_MODEL_ID)

system_message = SystemMessage("You are a helpful assistant that most accurately can tell ai written text from human written one.")
user_input="""Brooo, you won’t believe what happened today 😭 So I’m just minding my own business, right? Grabbing coffee like a civilized human being, and this barista legit calls out “Caramel macchiato for Michael” — and I’m like “bet, that’s me,” grab it, walk out all confident… only to realize halfway down the street it says soy latte, extra shot, no whip 😭😭 Bro, I straight up stole someone’s drink and been sipping on sadness the whole way home. It was actually kinda fire tho ngl 😅 Anyway, how’s your day been? You still surviving or already plotting your escape from adulthood?
"""


@tool
def detect_text(text):
    """ Process AI text detection using fakespot-ai/roberta-base-ai-text-detection-v1 model.
    It returns its result in a List[Dict] form.
    e.g. [{'label': 'AI', 'score': 0.9998624324798584}]
    """
    classifier = pipeline(
        "text-classification",
        model=TEXT_DETECTOR_MODEL_ID
    )

    ### Split text into max 300 words into multiple batches in case the text contains more than 350 words
    ### due to the max token limit of the detection model
    num_words = 300
    batched_words = []
    words: List = text.split()

    if len(text.split()) >= num_words:
        for i in range(0, len(words), num_words):
            chunk = " ".join(words[i: i + num_words])
            batched_words.append(chunk)

            ### Works with a batch of texts
            cleaned_text = classifier([clean_text(t) for t in batched_words])
            label_result = cleaned_text[0]["label"]
            score_result = cleaned_text[0]["score"]

        return label_result, score_result
    else:
        cleaned_text = classifier(clean_text(text))
        label_result = cleaned_text[0]["label"]
        score_result = cleaned_text[0]["score"]

        return label_result, score_result
        

def generate_dynamic_query(text, label, score):

    query = f"""
    Detect the text of the given input: {text}  
    AI-text likelihood score is given by another open source llm and the score will be given through the tool call as below:
    Label: {label}
    Score: {score:.3f}
    
    Based on all those(input text for your own analysis) and the score given by the function,
    give the final answer within 3-5 lines why it's assumed to be human or AI written text in a narrative and descriptive manner.
    """
    return query


def run_agent(text_input):

    ### Run the text detector model and extract label(whether AI or Human text) and likelihood score
    ### The result can be passively sent to the LLM but explicitly extracts label and score
    ### as the LLM doesn't take the accurate values by tool calling, for some reason I couldn't figure out
    ### So in that way, the run_agent calls detect_text twice which results in a higher latency for a couple more seconds.
    label, score = detect_text(text_input)
    query = generate_dynamic_query(text_input, label, score)

    tools = [detect_text]

    ### Issue: Tool calling is actually redundant in this case -- as label, score = detect_text(user_text) actually runs the function separately and extract all the llm needs to take
    agent_executor = create_react_agent(
        model=llm, tools=tools, prompt=system_message
    )

    result = agent_executor.invoke(
        {"messages": [{"role": "user", "content": query}]}
    )

    ### Process output formats
    label = "🧒 Likely " + label if label == "Human" else "🤖 Likely " + label
    score = f"{score * 100: .2f}" + "%"

    ### Return 3 elements in a tuple: content, text label and score
    return result["messages"][-1].content, label, score


###================= User Interface =================###

with gr.Blocks(css=CSS_ELEMENTS, title="AI WRITTEN TEXT DETECTOR") as demo:
    gr.Markdown(
        """
        ## 🕵️ AI GENERATED TEXT DETECTOR
        Analyze your text and discover how likely it is to be AI-generated.
        """,
        elem_id="app_title"
    )
    with gr.Column():
        with gr.Row():
            inp = gr.TextArea(placeholder="Paste your text to analyze", label="Text Input", lines=20, scale=2)
            features_desc = gr.HTML(FEATURES_HTML, elem_classes=["features_description"])

        button = gr.Button("Analyze Text", elem_classes=["analyze_btn"])
        gr.HTML(NOTE_HTML)

    with gr.Column():
        with gr.Row():
            label_output = gr.Textbox(
                label="Detected Label",
                placeholder="AI / Human",
                scale=1
            )
            score_output = gr.Textbox(
                label="Confidence Score",
                placeholder="0.000",
                scale=1
            )

        analysis_output = gr.TextArea(
            label="Analysis Result",
            placeholder="Model's explanation will appear here...",
            lines=6
        )

    button.click(
        fn=run_agent,
        inputs=inp,
        outputs=[analysis_output, label_output, score_output]
    )

if __name__ == "__main__":
    port = os.getenv("PORT", 7860)
    demo.launch(server_name="0.0.0.0", server_port=port)