test

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load the model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("BenjaminOcampo/peace_hatebert")
+model = AutoModelForSequenceClassification.from_pretrained("BenjaminOcampo/peace_hatebert")
+
+# Define more nuanced labels for the model output
+nuanced_labels = {
+    0: "Non-Hate Speech",
+    1: "Explicit Hate",
+    2: "Implicit Hate",
+    3: "White Grievance"
+}
+
+# Microaggressions detection rules
+microaggressions = {
+    "You're so articulate": "This phrase can imply surprise that the individual can speak well, often used in a way that suggests it is unexpected for someone of their background.",
+    "Where are you really from": "This question implies that the individual does not belong or is not truly part of the community.",
+    "I don't see color": "This statement can negate the experiences and identities of people of different races.",
+    "You're a credit to your race": "This phrase implies that most people of the individual’s race are not successful or commendable."
+}
+
+# A sample set of explanations and suggestions
+bias_suggestions = {
+    "Explicit Hate": {
+        "suggestion": "Consider using language that promotes inclusivity and respect.",
+        "explanation": "The text contains explicit hate speech, which is overtly harmful and discriminatory. It is important to foster communication that is inclusive and respectful of all individuals."
+    },
+    "Implicit Hate": {
+        "suggestion": "Try rephrasing to avoid subtle bias and ensure clarity.",
+        "explanation": "The text contains implicit hate speech, which can perpetuate stereotypes and bias in a less overt manner. Aim for language that is clear and free from insinuations."
+    },
+    "White Grievance": {
+        "suggestion": "Reconsider any generalized claims about racial groups.",
+        "explanation": "The text appears to express grievances linked to racial identity, which can contribute to divisive narratives. Strive for dialogue that acknowledges diversity and avoids stereotyping."
+    },
+    "Non-Hate Speech": {
+        "suggestion": "No problematic content detected.",
+        "explanation": "The text does not appear to contain hate speech or bias. It seems respectful and neutral."
+    },
+    "Microaggression": {
+        "suggestion": "Be mindful of how certain phrases can be interpreted by others.",
+        "explanation": "The text includes phrases that may be considered microaggressions, which can subtly perpetuate stereotypes or biases."
+    }
+}
+
+def analyze_text(text):
+    # Tokenize input text
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+    
+    # Get model predictions
+    with torch.no_grad():
+        outputs = model(**inputs)
+    
+    # Get prediction probabilities
+    probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    predicted_class = torch.argmax(probs, dim=-1).item()
+    
+    # Map the predicted class to the nuanced label
+    label = nuanced_labels.get(predicted_class, "Unknown")
+    
+    # Check for microaggressions using the predefined rules
+    for phrase, explanation in microaggressions.items():
+        if phrase.lower() in text.lower():
+            label = "Microaggression"
+            suggestion = bias_suggestions[label]["suggestion"]
+            explanation = bias_suggestions[label]["explanation"]
+            return label, suggestion, explanation
+    
+    # Fetch suggestion and explanation based on label
+    suggestion = bias_suggestions[label]["suggestion"]
+    explanation = bias_suggestions[label]["explanation"]
+    
+    return label, suggestion, explanation
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=analyze_text,
+    inputs=gr.inputs.Textbox(lines=5, placeholder="Enter text to analyze..."),
+    outputs=[
+        gr.outputs.Textbox(label="Classification"),
+        gr.outputs.Textbox(label="Suggestion"),
+        gr.outputs.Textbox(label="Explanation")
+    ],
+    title="Nuanced Bias and Microaggression Detection",
+    description="Analyze text for nuanced bias categories such as implicit hate, explicit hate, or white grievance, and detect microaggressions to provide suggestions for improvement."
+)
+
+# Launch the interface
+interface.launch()