The internet has revolutionized communication, fostering connection and knowledge sharing. However, this digital landscape can also breed negativity. Online toxicity - hateful comments, insults, and harassment - can create a hostile environment, discouraging meaningful discourse and pushing valuable voices away.

This blog post will guide you through building a Streamlit app that utilizes the power of Lyzr Automata and OpenAI to identify potentially toxic comments.
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Why Text Classification Matters?
Text classification tools can play a vital role in mitigating online toxicity. By identifying potentially harmful comments, these tools empower platforms and individuals to:

• Promote respectful dialogue: By flagging toxic comments, platforms can encourage users to reconsider their language and foster more civil discussions.
• Protect users from harassment: Identifying toxic comments allows for intervention and the protection of users from targeted negativity.
• Create a safer online space: By filtering out toxic content, online communities can become more welcoming and inclusive for everyone.

Building Your Text Classifier with Lyzr Automata
Lyzr Automata provides a user-friendly platform to build AI-powered applications. In this case, we'll utilize Lyzr Automata alongside OpenAI's powerful language models to create a Streamlit application for text toxicity classification.

Libraries and API Key:

import streamlit as st
from lyzr_automata.ai_models.openai import OpenAIModel
from lyzr_automata import Agent,Task
from lyzr_automata.pipelines.linear_sync_pipeline import LinearSyncPipeline
from PIL import Image
from dotenv import load_dotenv
import os

load_dotenv()
api = os.getenv("OPENAI_API_KEY")

The code imports necessary libraries like streamlit, lyzr_automata, PIL (for image processing), and dotenv (for environment variables).
load_dotenv() loads the OpenAI API key stored in a separate .env file (remember to create a .env file and store your API key securely!).

User Input:

query=st.text_input("Enter your comment: ")

st.text_input("Enter your comment: ") creates a text box where users can enter the comment they want to classify.

OpenAI Model Setup:

open_ai_text_completion_model = OpenAIModel(
    api_key=api,
    parameters={
        "model": "gpt-4-turbo-preview",
        "temperature": 0.2,
        "max_tokens": 1500,
    },
)

OpenAIModel() from lyzr_automata is used to define the OpenAI model to be used. Here, it's set to "gpt-4-turbo-preview" with specific parameters like temperature and maximum tokens.

Toxicity Classification Function:

def toxicity_classifier(query):

    toxicity_agent = Agent(
            role="toxicity expert",
            prompt_persona=f"You are an Expert toxicity finder. Your task is to find whether the {query} is toxic or not"
        )

    prompt=f"""you are a toxicity classifier and you have to find whether {query} is toxic or not.[!IMPORTANT]ONLY ANSWER WHETHER SENTENCE IS TOXIC OR NOT.nothing else apart from this"""

    toxicity_task  =  Task(
        name="Toxicity Classifier",
        model=open_ai_text_completion_model,
        agent=toxicity_agent,
        instructions=prompt,
    )

    output = LinearSyncPipeline(
        name="Toxicity Pipline",
        completion_message="pipeline completed",
        tasks=[
              toxicity_task
        ],
    ).run()

    answer = output[0]['task_output']

    return answer

toxicity_classifier(query) defines a function that takes the user's input (query) as an argument.
An Agent is created within the function, specifying its role as a "toxicity expert" and its purpose of identifying toxicity in the provided text.
The prompt_persona provides context to the model.
A clear prompt is defined using another st.markdown() call. It emphasizes that the model should only classify the text as toxic or non-toxic, avoiding any additional information.
A Task is created using Task(), specifying the task name, the OpenAI model, the agent, and the instructions (prompt) for the model.
A LinearSyncPipeline is created using LinearSyncPipeline(). This pipeline executes the defined tasks sequentially.
The pipeline is named "Toxicity Pipeline" with a completion message.
The list of tasks includes the previously defined toxicity_task.
.run() executes the pipeline, triggering the model's analysis.
The function retrieves the task's output (output[0]['task_output']) which contains the model's classification (toxic or non-toxic).
Finally, the function returns the retrieved answer.

Run Button and Output:

if st.button("Solve"):
    solution = toxicity_classifier(query)
    st.markdown(solution)

st.button("Solve") creates a button labeled "Solve." Clicking this button triggers the code below it.
If the button is pressed, the solution variable stores the result of the toxicity_classifier function, passing the user's input (query).
st.markdown(solution) displays the model's classification (toxic or non-toxic) on the Streamlit app using the markdown function.

This code demonstrates how to leverage Streamlit, Lyzr Automata, and OpenAI to create a user-friendly application for text toxicity classification. With this Streamlit app, users can easily identify potentially toxic comments, promoting a more positive online environment.
try it now:https://lyzr-toxicity-classifier.streamlit.app/
For more information explore the website: Lyzr