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# app.py for Hugging Face Space (Gradio)
import gradio as gr
from transformers import pipeline
import plotly.graph_objects as go
# Load pre-trained emotion classifier from Hugging Face
# Model trained on datasets similar to dair-ai/emotion (6 emotions: anger, fear, joy, love, sadness, surprise)
classifier = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", return_all_scores=True)
# Define prediction function for Gradio
def predict_emotion(text):
# Get model predictions (list of [{label, score}] for each emotion)
predictions = classifier(text)[0]
# Extract emotion labels and scores
emotions = [pred['label'] for pred in predictions]
scores = [pred['score'] for pred in predictions]
# Find the top emotion
top_emotion = emotions[scores.index(max(scores))]
# Create a bar chart with Plotly
fig = go.Figure(
data=[
go.Bar(x=emotions, y=scores, marker_color=['#FF6384', '#36A2EB', '#FFCE56', '#4BC0C0', '#9966FF', '#FF9F40'])
],
layout=go.Layout(
title="Emotion Probabilities",
xaxis_title="Emotions",
yaxis_title="Probability",
yaxis_range=[0, 1]
)
)
# Return top emotion and chart
return f"Predicted Emotion: {top_emotion}", fig
# Create Gradio interface
iface = gr.Interface(
fn=predict_emotion,
inputs=gr.Textbox(label="Enter a tweet or text", placeholder="e.g., I'm so happy today!"),
outputs=[
gr.Text(label="Prediction"),
gr.Plot(label="Emotion Probabilities")
],
title="Emotion Analyzer",
description="Enter a tweet or short text to predict its emotion (anger, fear, joy, love, sadness, surprise).",
examples=[
["I'm so excited for the weekend!"], # Should predict Joy
["This news is terrifying."], # Should predict Fear
["I miss you so much."] # Should predict Sadness
]
)
# Launch the interface (handled by Hugging Face Spaces)
if __name__ == "__main__":
iface.launch() |