Create app.py

app.py

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+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import shap
+import torch
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Load model and tokenizer with caching
+@st.cache_resource
+def load_model():
+    tokenizer = AutoTokenizer.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
+    model = AutoModelForSequenceClassification.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
+    return tokenizer, model
+
+tokenizer, model = load_model()
+
+# Define prediction function
+def predict(texts):
+    processed_texts = []
+    for text in texts:
+        processed_texts.append(text if not isinstance(text, list) 
+                              else tokenizer.convert_tokens_to_string(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)
+    
+    return torch.nn.functional.softmax(outputs.logits, dim=-1).numpy()
+
+# Initialize SHAP components
+output_names = [model.config.id2label[i] for i in range(5)]
+masker = shap.maskers.Text(tokenizer=tokenizer, mask_token=tokenizer.mask_token, collapse_mask_token=True)
+explainer = shap.Explainer(predict, masker, output_names=output_names)
+
+# Streamlit UI
+st.title("🎯 BERT Sentiment Analysis with SHAP")
+st.markdown("""
+**How it works:**
+1. Enter text in the box below
+2. See predicted sentiment (1-5 stars)
+3. View confidence scores and word-level explanations
+""")
+
+text_input = st.text_area("Input Text", placeholder="Enter text to analyze...", height=100)
+
+if st.button("Analyze Sentiment"):
+    if text_input.strip():
+        with st.spinner("Analyzing..."):
+            # Get predictions
+            probabilities = predict([text_input])[0]
+            predicted_class = np.argmax(probabilities)
+            
+            # Display results
+            st.subheader("📊 Results")
+            cols = st.columns(2)
+            cols[0].metric("Predicted Sentiment", output_names[predicted_class])
+            
+            with cols[1]:
+                st.markdown("**Confidence Scores**")
+                for i, (label, score) in enumerate(zip(output_names, probabilities)):
+                    st.progress(score, text=f"{label}: {score:.1%}")
+            
+            # Generate SHAP explanations
+            st.subheader("🔍 Explanation")
+            st.markdown("""
+            **Word impacts**  
+            Red → Increases score | Blue → Decreases score  
+            Intensity shows magnitude of impact
+            """)
+            
+            shap_values = explainer([text_input])
+            
+            # Create tabs for each sentiment class
+            tabs = st.tabs(output_names)
+            for i, tab in enumerate(tabs):
+                with tab:
+                    fig = shap.plots.text(shap_values[:, :, i], display=False)
+                    st.pyplot(fig)
+                    plt.close()
+    else:
+        st.warning("Please enter some text to analyze")
+
+st.markdown("---")
+st.markdown("Example texts to try:")
+examples = st.columns(4)
+example_texts = [
+    "This product exceeded all my expectations!",
+    "Terrible customer service experience.",
+    "The movie was okay, nothing special.",
+    "You are kinda cool"
+]
+
+for col, text in zip(examples, example_texts):
+    with col:
+        if st.button(text, use_container_width=True):
+            st.session_state.last_input = text
+
+if 'last_input' in st.session_state:
+    text_input = st.text_area("", value=st.session_state.last_input, height=100)