Create app.py

app.py

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+import streamlit as st
+from bert_model import BERTEmbedding
+from utils import build_vocab, get_embedding_tensor
+from sklearn.decomposition import PCA
+import plotly.express as px
+
+
+def plot_2d_embeddings(embeddings, sentence):
+    pca = PCA(n_components=2)
+    reduced = pca.fit_transform(embeddings[0].detach().numpy())
+    fig = px.scatter(
+        x=reduced[:, 0], 
+        y=reduced[:, 1], 
+        text=sentence.split()
+    )
+    st.plotly_chart(fig)
+
+def plot_3d_embeddings(embeddings, sentence):
+    pca = PCA(n_components=3)
+    reduced = pca.fit_transform(embeddings[0].detach().numpy())
+    fig = px.scatter_3d(
+        x=reduced[:, 0], 
+        y=reduced[:, 1], 
+        z=reduced[:, 2], 
+        text=sentence.split()
+    )
+    st.plotly_chart(fig)
+
+# Configuration
+N_SEGMENTS = 2
+MAX_LEN = 512
+EMBED_DIM = 768
+N_LAYERS = 12
+ATTN_HEADS = 12
+DROPOUT = 0.1
+
+
+def main():
+    st.title("BERT Embeddings Visualization")
+
+    uploaded_file = st.file_uploader("Upload a text file to build vocabulary", type=['txt'])
+
+    if uploaded_file:
+        uploaded_data = uploaded_file.read().decode('utf-8').splitlines()
+        st.success("Vocabulary built successfully!")
+    else:
+        st.warning("Using default vocabulary.")
+        with open('data/default_vocab.txt', 'r') as file:
+            uploaded_data = file.read().splitlines()
+
+    vocab = build_vocab(uploaded_data)
+
+    VOCAB_SIZE = len(vocab)
+    embedding_layer = BERTEmbedding(VOCAB_SIZE, N_SEGMENTS, MAX_LEN, EMBED_DIM, DROPOUT)
+    
+    user_sentence = st.text_input("Enter your sentence:", "AI in healthcare predicts patient outcomes and diagnoses.")
+    
+    viz_option = st.selectbox("Select Visualization Type", ["2D", "3D"])
+    
+    if st.button('Visualize Embeddings'):
+        embedding_tensor = get_embedding_tensor(user_sentence, vocab, embedding_layer)
+        
+        if viz_option == "2D":
+            plot_2d_embeddings(embedding_tensor, user_sentence)
+        elif viz_option == "3D":
+            plot_3d_embeddings(embedding_tensor, user_sentence)
+
+if __name__ == "__main__":
+    main()