asteios/pathos

1import gradio as gr2from transformers import pipeline3import numpy as np4import plotly.graph_objects as go5 6# Initialize the emotion classification model7classifier = pipeline(task="text-classification", model="SamLowe/roberta-base-go_emotions", top_k=None)8 9# Expanded PAD mapping for predefined emotions10pad_mapping = {11    'admiration': (0.5, 0.3, 0.6),12    'amusement': (0.8, 0.7, 0.4),13    'anger': (-0.8, 0.9, -0.4),14    'annoyance': (-0.6, 0.7, -0.3),15}16 17def classify_text(text):18    results = classifier(text)[0]  # Assuming the first element is the list we want19    pad_values = np.array([0.0, 0.0, 0.0])20    total_weight = 021    22    # Since results are a list of dictionaries23    for result in results:24        emotion = result.get('label')25        score = result.get('score')26        if emotion in pad_mapping:27            emotion_pad = np.array(pad_mapping[emotion])28            weighted_pad = emotion_pad * score29            pad_values += weighted_pad30            total_weight += score31 32    if total_weight > 0:33        pad_values /= total_weight34    35    fig = go.Figure()36    37    # Add predefined emotion points38    for emotion, coords in pad_mapping.items():39        fig.add_trace(go.Scatter3d(40            x=[coords[0]], y=[coords[1]], z=[coords[2]],41            mode='markers',42            marker=dict(size=6),43            name=emotion44        ))45    46    # Add classified text point47    fig.add_trace(go.Scatter3d(48        x=[pad_values[0]], y=[pad_values[1]], z=[pad_values[2]],49        mode='markers',50        marker=dict(symbol='x', size=8, color='red'),51        name="Classified Text"52    ))53    54    fig.update_layout(55        title='Interactive PAD Space Representation',56        scene=dict(57            xaxis_title='Pleasure',58            yaxis_title='Arousal',59            zaxis_title='Dominance'60        )61    )62    63    return fig.to_html()64 65interface = gr.Interface(fn=classify_text, inputs="text", outputs="html")66interface.launch()67