Create app.py

app.py

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+import streamlit as st
+from transformers import BertModel, BertTokenizer
+import torch
+from sklearn.decomposition import PCA
+import plotly.graph_objs as go
+
+# BERT embeddings function
+def get_bert_embeddings(words):
+    # Load pre-trained BERT model and tokenizer
+    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+    model = BertModel.from_pretrained('bert-base-uncased')
+
+    embeddings = []
+    
+    # Extract embeddings
+    for word in words:
+        inputs = tokenizer(word, return_tensors='pt')
+        outputs = model(**inputs)
+        embeddings.append(outputs.last_hidden_state[0][0].detach().numpy())
+
+    # Reduce dimensions to 3 using PCA
+    pca = PCA(n_components=3)
+    reduced_embeddings = pca.fit_transform(embeddings)
+
+    return reduced_embeddings
+
+# Plotly plotting function
+def plot_interactive_bert_embeddings(embeddings, words):
+    data = []
+
+    for i, word in enumerate(words):
+        trace = go.Scatter3d(
+            x=[embeddings[i][0]], 
+            y=[embeddings[i][1]], 
+            z=[embeddings[i][2]],
+            mode='markers+text',
+            text=[word],
+            name=word
+        )
+        data.append(trace)
+
+    layout = go.Layout(
+        title='3D Scatter Plot of BERT Embeddings',
+        scene=dict(
+            xaxis=dict(title='PCA Component 1'),
+            yaxis=dict(title='PCA Component 2'),
+            zaxis=dict(title='PCA Component 3')
+        )
+    )
+
+    fig = go.Figure(data=data, layout=layout)
+    return fig
+
+# Streamlit app
+def main():
+    st.title("BERT Embeddings Visualization")
+
+    # Text input for words
+    words_input = st.text_area("Enter words/phrases separated by commas:", "Spider-Man, Rocket Racoon, Venom, Spider, Racoon, Snake")
+    words = [word.strip() for word in words_input.split(',')]
+
+    if st.button("Generate Embeddings"):
+        with st.spinner('Generating embeddings...'):
+            embeddings = get_bert_embeddings(words)
+            fig = plot_interactive_bert_embeddings(embeddings, words)
+            st.plotly_chart(fig, use_container_width=True)
+
+if __name__ == "__main__":
+    main()