Create app.py

app.py

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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)