Update app.py

app.py

@@ -9,6 +9,14 @@ import pandas as pd
 # Page configuration
 st.set_page_config(layout="wide", page_title="🎯 Sentence Transformer Explorer")
 
+# Initialize session state for sentences if not exists
+if 'sentences' not in st.session_state:
+    st.session_state.sentences = [
+        "I love programming in Python",
+        "Coding with Python is my favorite",
+        "The weather is beautiful today"
+    ]
+
 # Load model
 @st.cache_resource
 def load_model():
@@ -16,56 +24,31 @@ def load_model():
 
 model = load_model()
 
-def get_embedding_and_similarity(sentences: List[str]) -> Tuple[np.ndarray, np.ndarray]:
-    embeddings = model.encode(sentences)
-    similarity_matrix = util.cos_sim(embeddings, embeddings).numpy()
-    return embeddings, similarity_matrix
-
-def create_word_importance_visualization(sentence: str, embedding: np.ndarray):
-    # Calculate word-level contribution to the embedding
-    words = sentence.split()
-    word_embeddings = model.encode(words)
-    
-    # Calculate each word's average contribution
-    word_importance = np.mean(np.abs(word_embeddings), axis=1)
-    
-    # Create word importance visualization
-    fig = go.Figure()
-    
-    # Add word bars
-    fig.add_trace(go.Bar(
-        x=words,
-        y=word_importance,
-        marker_color='rgb(158,202,225)',
-        text=np.round(word_importance, 3),
-        textposition='auto',
-    ))
-    
-    fig.update_layout(
-        title="Word Importance in Embedding",
-        xaxis_title="Words",
-        yaxis_title="Average Contribution",
-        height=400
-    )
-    return fig
+# Example templates
+EXAMPLE_TEMPLATES = {
+    "Similar Meanings": [
+        "I love programming in Python",
+        "Coding with Python is my favorite",
+        "I enjoy developing software using Python"
+    ],
+    "Different Topics": [
+        "The cat sleeps on the mat",
+        "Python is a programming language",
+        "The weather is beautiful today"
+    ],
+    "Semantic Relations": [
+        "Paris is the capital of France",
+        "Berlin is the capital of Germany",
+        "London is the capital of England"
+    ]
+}
 
-def create_similarity_heatmap(sentences: List[str], similarity_matrix: np.ndarray):
-    fig = go.Figure(data=go.Heatmap(
-        z=similarity_matrix,
-        x=sentences,
-        y=sentences,
-        colorscale='RdBu',
-        text=np.round(similarity_matrix, 3),
-        texttemplate='%{text}',
-        textfont={"size": 10},
-        hoverongaps=False
-    ))
-    
-    fig.update_layout(
-        title="Sentence Similarity Matrix",
-        height=400
-    )
-    return fig
+def load_example_sentences():
+    selected_template = st.session_state.get('template_selection')
+    st.session_state.sentences = EXAMPLE_TEMPLATES[selected_template]
+    # Force update of text inputs
+    for i, sentence in enumerate(st.session_state.sentences):
+        st.session_state[f'sentence_{i}'] = sentence
 
 def main():
     st.title("🎯 Interactive Sentence Transformer Explorer")
@@ -80,70 +63,64 @@ def main():
         4. **Interactive Examples**: Try different sentences and see the results
         """)
     
-    # Interactive sentence input
-    st.subheader("🔤 Enter Your Sentences")
-    
+    # Example selection
     col1, col2 = st.columns(2)
     
     with col1:
-        # Example templates
-        example_templates = {
-            "Similar Meanings": [
-                "I love programming in Python",
-                "Coding with Python is my favorite",
-                "I enjoy developing software using Python"
-            ],
-            "Different Topics": [
-                "The cat sleeps on the mat",
-                "Python is a programming language",
-                "The weather is beautiful today"
-            ],
-            "Semantic Relations": [
-                "Paris is the capital of France",
-                "Berlin is the capital of Germany",
-                "London is the capital of England"
-            ]
-        }
-        
-        selected_template = st.selectbox("Choose an example template:", 
-                                       list(example_templates.keys()))
+        st.selectbox(
+            "Choose an example template:",
+            options=list(EXAMPLE_TEMPLATES.keys()),
+            key='template_selection'
+        )
     
     with col2:
-        if st.button("Load Example"):
-            sentences = example_templates[selected_template]
-        else:
-            sentences = ["I love programming in Python",
-                        "Coding with Python is my favorite",
-                        "The weather is beautiful today"]
+        st.button(
+            "Load Example",
+            on_click=load_example_sentences,
+            type="primary"
+        )
     
     # Dynamic sentence input
     num_sentences = st.slider("Number of sentences:", 2, 5, 3)
     sentences = []
     
+    # Create text inputs with keys
     for i in range(num_sentences):
-        sentence = st.text_input(f"Sentence {i+1}", 
-                               value=sentences[i] if i < len(sentences) else "")
+        default_value = st.session_state.sentences[i] if i < len(st.session_state.sentences) else ""
+        sentence = st.text_input(
+            f"Sentence {i+1}",
+            value=default_value,
+            key=f'sentence_{i}'
+        )
         sentences.append(sentence)
     
     if st.button("Analyze Sentences", type="primary"):
         if all(sentences):
-            embeddings, similarity_matrix = get_embedding_and_similarity(sentences)
+            # Your existing analysis code here...
+            embeddings = model.encode(sentences)
+            similarity_matrix = util.cos_sim(embeddings, embeddings).numpy()
             
             st.subheader("📊 Analysis Results")
             
-            # Create tabs for different visualizations
-            tab1, tab2, tab3 = st.tabs(["Word Importance", "Sentence Similarity", "Embedding Space"])
+            tab1, tab2 = st.tabs(["Sentence Similarity", "Embedding Analysis"])
             
             with tab1:
-                st.markdown("### 🔍 Word-Level Analysis")
-                for i, sentence in enumerate(sentences):
-                    st.markdown(f"**Sentence {i+1}:** {sentence}")
-                    fig = create_word_importance_visualization(sentence, embeddings[i])
-                    st.plotly_chart(fig, use_container_width=True)
-            
-            with tab2:
-                st.markdown("### 🤝 Sentence Similarity Analysis")
-                fig = create_similarity_heatmap(sentences, similarity_matrix)
+                # Create similarity heatmap
+                fig = go.Figure(data=go.Heatmap(
+                    z=similarity_matrix,
+                    x=sentences,
+                    y=sentences,
+                    colorscale='RdBu',
+                    text=np.round(similarity_matrix, 3),
+                    texttemplate='%{text}',
+                    textfont={"size": 10},
+                    hoverongaps=False
+                ))
+                
+                fig.update_layout(
+                    title="Sentence Similarity Matrix",
+                    height=400
+                )
                 st.plotly_chart(fig, use_container_width=True)
                 
                 # Add similarity interpretation
@@ -156,11 +133,9 @@ def main():
                             else "Moderately similar" if similarity > 0.5 
                             else "Different"
                         )
-                        st.write(f"Sentences {i+1} & {i+2}: {interpretation} ({similarity:.3f})")
+                        st.write(f"Sentences {i+1} & {j+1}: {interpretation} ({similarity:.3f})")
             
-            with tab3:
-                st.markdown("### 🎯 Interactive Embedding Analysis")
-                
+            with tab2:
                 # Create embedding statistics
                 embedding_stats = pd.DataFrame({
                     'Sentence': sentences,