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<h1 id="fairsense-api-documentation"><strong>FairSense API Documentation</strong></h1>
<hr />
<h2 id="introduction"><strong>Introduction</strong></h2>
<p>FairSense is an AI-driven platform for detecting and analyzing bias in textual and visual content. This document outlines the key functions and APIs provided by FairSense for integration and usage.
We are releasing a multimodal bias detection toolkit</p>
<hr />
<h2 id="1-core-components"><strong>1. Core Components</strong></h2>
<h3 id="device-setup"><strong>Device Setup</strong></h3>
<p>Sets up the device for model computation (CPU or GPU).</p>
<pre><code class="language-python">DEVICE = torch.device(&quot;cuda&quot; if torch.cuda.is_available() else &quot;cpu&quot;)
</code></pre>
<hr />
<h3 id="model-initialization"><strong>Model Initialization</strong></h3>
<p>Preloads the required models:
- <strong>Text Model</strong>: <code>unsloth/Llama-3.2-1B-Instruct</code> or <code>meta-llama/Llama-3.2-1B-Instruct</code> or any instruct model
- <strong>Image Captioning Model</strong>: <code>Salesforce/blip-image-captioning-large</code>
- <strong>Summarizer</strong>: <code>sshleifer/distilbart-cnn-12-6</code></p>
<hr />
<h2 id="2-helper-functions"><strong>2. Helper Functions</strong></h2>
<h3 id="post_process_responseresponse"><strong><code>post_process_response(response)</code></strong></h3>
<ul>
<li><strong>Purpose</strong>: Cleans and summarizes AI model responses.</li>
<li><strong>Parameters</strong>:</li>
<li><code>response</code> <em>(str)</em>: The raw response from the AI model.</li>
<li><strong>Returns</strong>: A cleaned and summarized response string.</li>
<li><strong>Example</strong>:
<code>python
processed_response = post_process_response("This is the raw response from the model.")
print(processed_response)</code></li>
</ul>
<hr />
<h3 id="highlight_biastext-bias_words"><strong><code>highlight_bias(text, bias_words)</code></strong></h3>
<ul>
<li><strong>Purpose</strong>: Highlights specific biased words in the text.</li>
<li><strong>Parameters</strong>:</li>
<li><code>text</code> <em>(str)</em>: The input text to analyze.</li>
<li><code>bias_words</code> <em>(list)</em>: A list of words to highlight as biased.</li>
<li><strong>Returns</strong>: HTML-formatted text with highlighted bias words.</li>
<li><strong>Example</strong>:
<code>python
highlighted_text = highlight_bias("This is a biased statement.", ["biased"])
print(highlighted_text)</code></li>
</ul>
<hr />
<h2 id="3-text-analysis"><strong>3. Text Analysis</strong></h2>
<h3 id="generate_response_with_modelprompt-progressnone"><strong><code>generate_response_with_model(prompt, progress=None)</code></strong></h3>
<ul>
<li><strong>Purpose</strong>: Generates a response from the AI model for a given prompt.</li>
<li><strong>Parameters</strong>:</li>
<li><code>prompt</code> <em>(str)</em>: The input prompt for the model.</li>
<li><code>progress</code> <em>(callable, optional)</em>: Function to track progress.</li>
<li><strong>Returns</strong>: AI-generated response as a string.</li>
<li><strong>Example</strong>:
<code>python
response = generate_response_with_model("Analyze this text for bias.")
print(response)</code></li>
</ul>
<hr />
<h3 id="analyze_text_for_biastext_input-progressgrprogress"><strong><code>analyze_text_for_bias(text_input, progress=gr.Progress())</code></strong></h3>
<ul>
<li><strong>Purpose</strong>: Analyzes a given text for bias and provides a detailed analysis.</li>
<li><strong>Parameters</strong>:</li>
<li><code>text_input</code> <em>(str)</em>: Text to analyze.</li>
<li><code>progress</code> <em>(gr.Progress)</em>: Progress tracker.</li>
<li><strong>Returns</strong>: Highlighted text and detailed analysis.</li>
<li><strong>Example</strong>:
<code>python
highlighted, analysis = analyze_text_for_bias("This text may contain bias.")
print(highlighted)
print(analysis)</code></li>
</ul>
<hr />
<h2 id="4-image-analysis"><strong>4. Image Analysis</strong></h2>
<h3 id="preprocess_imageimage"><strong><code>preprocess_image(image)</code></strong></h3>
<ul>
<li><strong>Purpose</strong>: Converts images to grayscale and applies thresholding for OCR.</li>
<li><strong>Parameters</strong>:</li>
<li><code>image</code> <em>(PIL.Image)</em>: The input image.</li>
<li><strong>Returns</strong>: A preprocessed image for OCR.</li>
<li><strong>Example</strong>:
<code>python
from PIL import Image
image = Image.open("example.jpg")
preprocessed = preprocess_image(image)
preprocessed.show()</code></li>
</ul>
<hr />
<h3 id="analyze_image_for_biasimage-progressgrprogress"><strong><code>analyze_image_for_bias(image, progress=gr.Progress())</code></strong></h3>
<ul>
<li><strong>Purpose</strong>: Analyzes an image for bias by extracting text and generating captions.</li>
<li><strong>Parameters</strong>:</li>
<li><code>image</code> <em>(PIL.Image)</em>: The input image.</li>
<li><code>progress</code> <em>(gr.Progress)</em>: Progress tracker.</li>
<li><strong>Returns</strong>: Highlighted captions and detailed analysis.</li>
<li><strong>Example</strong>:
<code>python
image = Image.open("example.jpg")
highlighted, analysis = analyze_image_for_bias(image)
print(highlighted)
print(analysis)</code></li>
</ul>
<hr />
<h2 id="5-batch-processing"><strong>5. Batch Processing</strong></h2>
<h3 id="analyze_text_csvfile-output_filenameanalysis_resultscsv"><strong><code>analyze_text_csv(file, output_filename="analysis_results.csv")</code></strong></h3>
<ul>
<li><strong>Purpose</strong>: Analyzes a CSV file of text entries for bias.</li>
<li><strong>Parameters</strong>:</li>
<li><code>file</code> <em>(File)</em>: CSV file with text data.</li>
<li><code>output_filename</code> <em>(str)</em>: Name of the output CSV file.</li>
<li><strong>Returns</strong>: An HTML table with analysis results.</li>
<li><strong>Example</strong>:
<code>python
html_table = analyze_text_csv("data.csv")
print(html_table)</code></li>
</ul>
<hr />
<h3 id="analyze_images_batchimages-output_filenameimage_analysis_resultscsv"><strong><code>analyze_images_batch(images, output_filename="image_analysis_results.csv")</code></strong></h3>
<ul>
<li><strong>Purpose</strong>: Analyzes multiple images for bias.</li>
<li><strong>Parameters</strong>:</li>
<li><code>images</code> <em>(list)</em>: List of image paths.</li>
<li><code>output_filename</code> <em>(str)</em>: Name of the output file.</li>
<li><strong>Returns</strong>: HTML table with analysis results and image previews.</li>
<li><strong>Example</strong>:
<code>python
results = analyze_images_batch(["image1.jpg", "image2.png"])
print(results)</code></li>
</ul>
<hr />
<h3 id="save_results_to_csvdf-filenameresultscsv"><strong><code>save_results_to_csv(df, filename="results.csv")</code></strong></h3>
<ul>
<li><strong>Purpose</strong>: Saves analysis results to a CSV file.</li>
<li><strong>Parameters</strong>:</li>
<li><code>df</code> <em>(pandas.DataFrame)</em>: DataFrame containing results.</li>
<li><code>filename</code> <em>(str)</em>: Name of the output file.</li>
<li><strong>Returns</strong>: Path to the saved file.</li>
<li><strong>Example</strong>:
<code>python
results_df = pd.DataFrame([{"text": "example", "analysis": "unbiased"}])
save_path = save_results_to_csv(results_df, "output.csv")
print(save_path)</code></li>
</ul>
<hr />
<h2 id="6-ai-governance"><strong>6. AI Governance</strong></h2>
<h3 id="ai_governance_responseprompt-progressnone"><strong><code>ai_governance_response(prompt, progress=None)</code></strong></h3>
<ul>
<li><strong>Purpose</strong>: Provides insights into AI governance and safety.</li>
<li><strong>Parameters</strong>:</li>
<li><code>prompt</code> <em>(str)</em>: Topic or question about AI governance.</li>
<li><code>progress</code> <em>(callable, optional)</em>: Progress tracker.</li>
<li><strong>Returns</strong>: AI-generated insights and recommendations.</li>
<li><strong>Example</strong>:
<code>python
insights = ai_governance_response("Discuss AI ethics.")
print(insights)</code></li>
</ul>
<hr />
<h2 id="7-ai-safety-dashboard"><strong>7. AI Safety Dashboard</strong></h2>
<h3 id="display_ai_safety_dashboard"><strong><code>display_ai_safety_dashboard()</code></strong></h3>
<ul>
<li><strong>Purpose</strong>: Visualizes AI safety risks using interactive charts.</li>
<li><strong>Returns</strong>: Tuple containing bar chart, pie chart, scatter plot, and DataFrame.</li>
<li><strong>Example</strong>:
<code>python
fig_bar, fig_pie, fig_scatter, risks_df = display_ai_safety_dashboard()
fig_bar.show()</code></li>
</ul>
<hr />
<h1 id="demo-video">Demo Video</h1>
<p>Watch the demonstration of the FairSense platform below:</p>
<iframe src="https://drive.google.com/file/d/1B0GhvxbJ_dR8xhruOK5cEa_DApTC_xmo/preview"
width="600" height="450" allow="autoplay"></iframe>

<h2 id="next-steps"><strong>Next Steps</strong></h2>
<p>This documentation provides the foundation for integrating FairSense into your workflows.
<strong>Contact</strong>: For inquiries, collaborations, or feedback, connect with <strong>Shaina Raza, PhD</strong>, at <strong>[email protected]</strong>.
Let me know if you need anything else added! 😊</p>

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