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from transformers import AutoTokenizer, AutoModelForSequenceClassification
import shap
import torch
import gradio as gr
import matplotlib.pyplot as plt
import numpy as np
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
model = AutoModelForSequenceClassification.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
# Define prediction function
def predict(texts):
processed_texts = []
for text in texts:
if isinstance(text, list):
processed_text = tokenizer.convert_tokens_to_string(text)
else:
processed_text = text
processed_texts.append(processed_text)
inputs = tokenizer(
processed_texts,
return_tensors="pt",
padding=True,
truncation=True,
max_length=512,
add_special_tokens=True
)
with torch.no_grad():
outputs = model(**inputs)
probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
return probabilities.numpy()
# Initialize SHAP components
output_names_list = [model.config.id2label[i] for i in range(len(model.config.id2label))]
masker = shap.maskers.Text(tokenizer=tokenizer, mask_token=tokenizer.mask_token, collapse_mask_token=True)
explainer = shap.Explainer(model=predict, masker=masker, output_names=output_names_list)
def analyze_text(text):
# Get predictions
probabilities = predict([text])[0]
predicted_class = np.argmax(probabilities)
predicted_label = model.config.id2label[predicted_class]
# Generate SHAP explanations
shap_values = explainer([text])
# Create HTML visualizations for all classes
html_plots = []
for i in range(shap_values.shape[-1]):
# Create SHAP text plot and convert to HTML
plot_html = shap.plots.text(shap_values[0, :, i], display=False)
html_plots.append(plot_html)
# Format confidence scores
confidence_scores = {model.config.id2label[i]: float(probabilities[i])
for i in range(len(probabilities))}
return (predicted_label,
confidence_scores,
*html_plots)
# Create Gradio interface with HTML components
with gr.Blocks(theme=gr.themes.Soft()) as demo:
gr.Markdown("## 🔍 BERT Sentiment Analysis with SHAP Explanations")
with gr.Row():
input_text = gr.Textbox(label="Input Text", placeholder="Enter text to analyze...")
with gr.Row():
predict_btn = gr.Button("Analyze Sentiment")
with gr.Row():
label_output = gr.Label(label="Predicted Sentiment")
prob_output = gr.Label(label="Confidence Scores")
with gr.Row():
gr.Markdown("""
### SHAP Explanations
Below you can see how each word contributes to different sentiment scores (1-5 stars).
Red text increases the score, blue decreases it.
""")
# Individual Explanation Rows
plot_components = []
for i in range(5):
with gr.Row():
plot_components.append(
gr.HTML(
label=f"Explanation for {model.config.id2label[i]}",
elem_classes=f"shap-plot-{i+1}"
)
)
predict_btn.click(
fn=analyze_text,
inputs=input_text,
outputs=[label_output, prob_output] + plot_components
)
examples = gr.Examples(
examples=[
["This product exceeded all my expectations!"],
["Terrible customer service experience."],
["The movie was okay, nothing special."],
["You are kinda cool"],
],
inputs=input_text
)
if __name__ == "__main__":
demo.launch(debug = True) |