Create app.py

app.py

@@ -0,0 +1,188 @@
+import streamlit as st
+import numpy as np
+from wordllama import WordLlama
+import plotly.graph_objects as go
+import plotly.express as px
+from sklearn.decomposition import PCA
+import pandas as pd
+
+# Page configuration
+st.set_page_config(
+    page_title="WordLlama Explorer",
+    page_icon="🦙",
+    layout="wide"
+)
+
+# Custom CSS
+st.markdown("""
+    <style>
+    .main {
+        background-color: #f8f9fa;
+    }
+    .stTabs [data-baseweb="tab-list"] {
+        gap: 24px;
+    }
+    .stTabs [data-baseweb="tab"] {
+        height: 50px;
+        padding-left: 20px;
+        padding-right: 20px;
+    }
+    .title-font {
+        font-size: 28px !important;
+        font-weight: bold;
+        color: #2c3e50;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+@st.cache_resource
+def load_wordllama():
+    return WordLlama.load()
+
+wl = load_wordllama()
+
+def create_3d_visualization(texts, embeddings):
+    # Reduce to 3D using PCA
+    pca = PCA(n_components=3)
+    embeddings_3d = pca.fit_transform(embeddings)
+    
+    # Create DataFrame
+    df = pd.DataFrame(
+        embeddings_3d,
+        columns=['X', 'Y', 'Z']
+    )
+    df['text'] = texts
+    
+    fig = px.scatter_3d(
+        df, x='X', y='Y', z='Z',
+        text='text',
+        title='Word Embeddings in 3D Space'
+    )
+    
+    fig.update_traces(
+        marker=dict(size=8, opacity=0.8),
+        textposition='top center'
+    )
+    fig.update_layout(
+        scene=dict(
+            xaxis_title='Component 1',
+            yaxis_title='Component 2',
+            zaxis_title='Component 3'
+        ),
+        height=700
+    )
+    return fig
+
+def main():
+    st.title("🦙 WordLlama Embedding Explorer")
+    st.markdown("<p class='title-font'>Explore the power of WordLlama embeddings</p>", 
+                unsafe_allow_html=True)
+    
+    tabs = st.tabs(["💫 Similarity Explorer", "🎯 Document Ranking", "🔍 Fuzzy Deduplication"])
+    
+    with tabs[0]:
+        st.markdown("### Compare Text Similarity")
+        
+        col1, col2 = st.columns(2)
+        with col1:
+            text1 = st.text_area("First Text", value="I love programming in Python", height=100)
+        with col2:
+            text2 = st.text_area("Second Text", value="Coding with Python is amazing", height=100)
+        
+        if st.button("Calculate Similarity", key="sim_button"):
+            similarity = wl.similarity(text1, text2)
+            
+            st.markdown("### Similarity Score")
+            st.metric(
+                label="Cosine Similarity",
+                value=f"{similarity:.4f}",
+                help="Score ranges from 0 (different) to 1 (identical)"
+            )
+            
+            # Visualize both texts in 3D space
+            embeddings = wl.embed([text1, text2])
+            st.plotly_chart(
+                create_3d_visualization([text1, text2], embeddings),
+                use_container_width=True
+            )
+    
+    with tabs[1]:
+        st.markdown("### Rank Documents by Similarity")
+        
+        query = st.text_area("Query Text", value="I went to the car", height=100)
+        
+        # Multiple document input
+        st.markdown("### Enter Documents to Rank")
+        num_docs = st.slider("Number of documents:", 2, 6, 4)
+        
+        documents = []
+        for i in range(num_docs):
+            doc = st.text_area(f"Document {i+1}", 
+                             value=f"Example document {i+1}", 
+                             height=50,
+                             key=f"doc_{i}")
+            documents.append(doc)
+        
+        if st.button("Rank Documents", key="rank_button"):
+            ranked_docs = wl.rank(query, documents)
+            
+            st.markdown("### Ranking Results")
+            for doc, score in ranked_docs:
+                st.markdown(f"""
+                <div style='padding: 10px; margin: 5px; background-color: #f0f2f6; border-radius: 5px;'>
+                    <b>Score: {score:.4f}</b><br>
+                    {doc}
+                </div>
+                """, unsafe_allow_html=True)
+            
+            # Visualize all texts including query
+            all_texts = [query] + documents
+            embeddings = wl.embed(all_texts)
+            st.plotly_chart(
+                create_3d_visualization(all_texts, embeddings),
+                use_container_width=True
+            )
+    
+    with tabs[2]:
+        st.markdown("### Fuzzy Deduplication")
+        st.markdown("""
+        Remove similar documents based on a similarity threshold.
+        Documents with similarity above the threshold will be considered duplicates.
+        """)
+        
+        # Document input for deduplication
+        st.markdown("### Enter Documents")
+        num_dedup_docs = st.slider("Number of documents:", 2, 8, 4, key="dedup_slider")
+        
+        dedup_docs = []
+        for i in range(num_dedup_docs):
+            doc = st.text_area(f"Document {i+1}", 
+                             value=f"Example document {i+1}", 
+                             height=50,
+                             key=f"dedup_doc_{i}")
+            dedup_docs.append(doc)
+        
+        threshold = st.slider("Similarity Threshold:", 0.0, 1.0, 0.8)
+        
+        if st.button("Find Duplicates", key="dedup_button"):
+            unique_docs = wl.deduplicate(dedup_docs, threshold=threshold)
+            
+            st.markdown("### Results")
+            st.markdown(f"Found {len(unique_docs)} unique documents:")
+            
+            for doc in unique_docs:
+                st.markdown(f"""
+                <div style='padding: 10px; margin: 5px; background-color: #f0f2f6; border-radius: 5px;'>
+                    {doc}
+                </div>
+                """, unsafe_allow_html=True)
+            
+            # Visualize all documents
+            embeddings = wl.embed(dedup_docs)
+            st.plotly_chart(
+                create_3d_visualization(dedup_docs, embeddings),
+                use_container_width=True
+            )
+
+if __name__ == "__main__":
+    main()