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import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification
# Load the model from Hugging Face Model Hub
model_name = "SamLowe/roberta-base-go_emotions"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
# Define emotion labels used by the model
emotion_labels = ["admiration", "amusement", "anger", "annoyance", "approval",
"caring", "confusion", "curiosity", "desire", "disappointment",
"disapproval", "disgust", "embarrassment", "excitement",
"fear", "gratitude", "grief", "joy", "love", "nervousness",
"optimism", "pride", "realization", "relief", "remorse",
"sadness", "surprise", "neutral"]
def predict_emotion(text):
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
predicted_class = logits.argmax().item()
predicted_emotion = emotion_labels[predicted_class]
confidence = logits.softmax(dim=1).squeeze()[predicted_class].item()
return predicted_emotion, confidence # Return the predicted emotion and confidence
def get_confidence_color(confidence):
# Define a color scale based on confidence
if confidence >= 0.8:
return "green"
elif confidence >= 0.5:
return "orange"
else:
return "red"
iface = gr.Interface(
fn=predict_emotion,
inputs=gr.Textbox(),
outputs=[
gr.Textbox(),
"text",
gr.Textbox("Confidence Score:", default=""),
],
live=True,
title="Emotion Prediction",
description="Enter a sentence for emotion prediction.",
)
# Customize the interface appearance
iface.style(
confidence_score=get_confidence_color
)
iface.launch()
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