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app.py

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+import gradio as gr
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from gradio_client import Client
+import json
+
+# Initialize the Gradio Client if not already done
+client = Client("duchaba/Friendly_Text_Moderation")
+
+def moderate_and_plot(text_input):
+    if not text_input:
+        raise gr.Error("Please enter some text for moderation.")
+
+    # Use the existing client to predict toxicity levels
+    prediction_result = client.predict(
+        msg=text_input,
+        safer=0.02, # This parameter might be specific to the original model's API
+        api_name="/fetch_toxicity_level"
+    )
+    # Parse the JSON string into a Python dictionary
+    dictionary_output = json.loads(prediction_result[-1])
+
+    # Filter out non-toxicity related keys from the dictionary_output for the plot
+    toxicity_data = {k: v for k, v in dictionary_output.items() if isinstance(v, (int, float)) and not k.startswith(('is_', 'max_', 'sum_', 'safer_')) and k != 'message'}
+
+    # Convert the dictionary to a pandas DataFrame for easier plotting
+    df_toxicity = pd.DataFrame(list(toxicity_data.items()), columns=['Toxicity Type', 'Score'])
+
+    # Sort the values for better visualization (optional)
+    df_toxicity = df_toxicity.sort_values(by='Score', ascending=False)
+
+    # Create a bar plot
+    plt.figure(figsize=(12, 7))
+    sns.barplot(x='Toxicity Type', y='Score', hue='Toxicity Type', data=df_toxicity, palette='viridis', legend=False)
+    plt.title('Toxicity Scores for the Sample Message')
+    plt.xlabel('Toxicity Type')
+    plt.ylabel('Score')
+    plt.xticks(rotation=45, ha='right') # Rotate labels to prevent overlap
+    plt.tight_layout()
+
+    # Add is_flagged and max_key as text labels on the plot
+    is_flagged_status = "Unsafe" if dictionary_output.get('is_flagged', False) else "Safe"
+    max_key_value = dictionary_output.get('max_key', 'N/A')
+
+    plt.text(0.02, 0.98, f"Flagged Status: {is_flagged_status}", transform=plt.gca().transAxes, fontsize=12, verticalalignment='top', bbox=dict(boxstyle='round,pad=0.5', fc='yellow', alpha=0.5))
+    plt.text(0.02, 0.90, f"Max Toxicity Type: {max_key_value}", transform=plt.gca().transAxes, fontsize=12, verticalalignment='top', bbox=dict(boxstyle='round,pad=0.5', fc='lightblue', alpha=0.5))
+
+    # Prepare detailed toxicity scores for text output
+    detailed_scores_output = "Toxicity Details:\n"
+    keys_to_display = [
+        'harassment', 'harassment_threatening', 'hate', 'hate_threatening',
+        'illicit', 'illicit_violent', 'self_harm', 'self_harm_instructions',
+        'self_harm_intent', 'sexual', 'sexual_minors', 'violence',
+        'violence_graphic', 'harassment/threatening', 'hate/threatening',
+        'illicit/violent', 'self-harm/intent', 'self-harm/instructions',
+        'self-harm', 'sexual/minors', 'violence/graphic'
+    ]
+    for key in keys_to_display:
+        score = dictionary_output.get(key)
+        if score is not None:
+            detailed_scores_output += f"- {key.replace('_', ' ').replace('/', '/ ')}: {score:.6f}\n"
+
+    return plt.gcf(), detailed_scores_output
+
+# Define custom CSS for centering text
+custom_css = """
+.centered-text textarea {
+    text-align: center;
+}
+"""
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=moderate_and_plot,
+    inputs=gr.Textbox(lines=5, label="Enter text for moderation"),
+    outputs=[
+        gr.Plot(label="Toxicity Levels Bar Graph"),
+        gr.Textbox(label="Detailed Toxicity Scores", lines=10, elem_classes=["centered-text"])
+    ],
+    title="Friendly Text Moderation Interface",
+    description="Enter text to get a visual breakdown of various toxicity levels using the `duchaba/Friendly_Text_Moderation` model.",
+    css=custom_css # Moved css argument here
+)
+
+# Launch the interface
+iface.launch(debug=True)

requirements.txt

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+gradio==4.29.0
+pandas==2.2.2
+matplotlib==3.8.4
+seaborn==0.13.2
+gradio-client==0.16.0