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Emotion Detection - Hugging Face Spaces (CPU)
Gradio app for free deployment on HF Spaces.
DistilBERT is small enough (~268MB) for fast CPU inference.
"""
import gradio as gr
from transformers import pipeline
EMOJI_MAP = {
"sadness": "😢",
"joy": "😊",
"love": "❤️",
"anger": "😡",
"fear": "😨",
"surprise": "😲",
}
LABEL_MAP = {
"LABEL_0": "sadness",
"LABEL_1": "joy",
"LABEL_2": "love",
"LABEL_3": "anger",
"LABEL_4": "fear",
"LABEL_5": "surprise",
}
classifier = pipeline(
"text-classification",
model="./models/emotion_model",
tokenizer="./models/emotion_model",
)
def predict(text: str) -> str:
if not text.strip():
return "Please enter some text."
result = classifier(text)[0]
raw_label = result["label"]
emotion = LABEL_MAP.get(raw_label, raw_label)
confidence = result["score"]
emoji = EMOJI_MAP.get(emotion, "")
return f"{emoji} **{emotion.upper()}**\n\nConfidence: {confidence:.4f}"
examples = [
["I am so happy today, everything is going great!"],
["I feel terrible and nothing seems to work out."],
["This is absolutely terrifying, I can't stop shaking."],
["I can't believe you would do something like that to me!"],
["You are the most wonderful person I have ever met."],
["Wow, I never expected that to happen!"],
]
demo = gr.Interface(
fn=predict,
inputs=gr.Textbox(
label="Enter text",
placeholder="Type a sentence to detect its emotion...",
lines=3,
),
outputs=gr.Markdown(label="Prediction"),
title="Emotion Detection from Text",
description=(
"Detects emotions in English text using a fine-tuned **DistilBERT** model. "
"Classifies into 6 categories: sadness, joy, love, anger, fear, surprise. "
"Achieves **93.75% accuracy** on the test set."
),
examples=examples,
theme=gr.themes.Soft(),
flagging_mode="never",
)
if __name__ == "__main__":
demo.launch()
|