File size: 3,233 Bytes
bed30b5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 | import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import numpy as np
# Load model and tokenizer
model_name = "Aakash22134/bert-twitter-sentiment-classifier"
print("Loading model...")
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
print("Model loaded successfully!")
# Emotion labels
id2label = {
0: "Sadness 😢",
1: "Joy 😊",
2: "Love ❤️",
3: "Anger 😠",
4: "Fear 😨",
5: "Surprise 😲"
}
def predict_emotion(text):
"""
Predict emotion from text
"""
if not text.strip():
return {label: 0.0 for label in id2label.values()}
# Tokenize
inputs = tokenizer(
text,
return_tensors="pt",
truncation=True,
max_length=512,
padding=True
)
# Predict
with torch.no_grad():
outputs = model(**inputs)
predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
probabilities = predictions[0].numpy()
# Create results dictionary
results = {id2label[i]: float(probabilities[i]) for i in range(len(probabilities))}
return results
# Example texts
examples = [
["I just got promoted at work! This is the best day ever!"],
["I can't believe they cancelled my favorite show. I'm so upset right now."],
["Missing you so much. Can't wait to see you again."],
["Why does everything always go wrong for me? This is so frustrating!"],
["I'm really worried about the test tomorrow. What if I fail?"],
["Oh wow! I never expected this to happen!"]
]
# Create Gradio interface
demo = gr.Interface(
fn=predict_emotion,
inputs=gr.Textbox(
label="Enter your text",
placeholder="Type something here...",
lines=3
),
outputs=gr.Label(label="Emotion Prediction", num_top_classes=6),
title="🎭 Twitter Sentiment Classifier",
description="""
This AI model analyzes text and predicts the underlying emotion.
It can detect 6 different emotions: **Sadness, Joy, Love, Anger, Fear, and Surprise**.
**How to use:** Simply type or paste any text and the model will predict the emotion!
""",
examples=examples,
theme=gr.themes.Soft(),
article="""
### About This Model
- **Base Model**: BERT (bert-base-uncased)
- **Task**: Multi-class emotion classification
- **Test Accuracy**: 90.06%
- **Training Data**: 16,000 emotion-labeled tweets
- **Classes**: Sadness, Joy, Love, Anger, Fear, Surprise
### Performance Metrics
| Emotion | Precision | Recall | F1-Score |
|---------|-----------|--------|----------|
| Sadness | 0.93 | 0.95 | 0.94 |
| Joy | 0.92 | 0.91 | 0.92 |
| Love | 0.76 | 0.75 | 0.76 |
| Anger | 0.91 | 0.91 | 0.91 |
| Fear | 0.89 | 0.88 | 0.88 |
| Surprise | 0.75 | 0.72 | 0.74 |
### Limitations
- Works best with English text
- Trained on Twitter/social media style text
- May not perform as well on formal or technical text
- Shorter texts (like tweets) work better than very long texts
"""
)
if __name__ == "__main__":
demo.launch() |