UI improvements: consolidate instructions into expandable sections and reorganize layout

app.py

@@ -13,20 +13,6 @@ st.markdown(
     """
     **Purpose:** 
     Quickly remove irrelevant keywords from your keyword research and move to the next step in your optimization!
-
-    Have you ever had to review a long list of queries to determine whether they were relevant to your target keyword? This Space aims to automate that process by entering your primary keyword and a list of related queries from any source you might do keyword research.
-    The resulting table is an ordered list of your comparison keywords based on the cosine similarity of each query's embeddings.
-
-    **Instructions:**
-    1. Enter your **Primary Keyword** in the input field.
-    2. Provide a list of **Keywords to Compare** (separated by new lines or commas).
-    3. Select an **Embedding Model** to compute keyword embeddings.
-    4. If using OpenAI embeddings, input your **API Key**.
-    5. Click **Calculate Similarities** to compute and rank your keywords by relevance.
-
-    **Output:**
-    - A sorted table of your comparison keywords based on their cosine similarity to your primary keyword.
-    - Option to download the results as a CSV file.
     """
 )
 
@@ -43,6 +29,23 @@ keywords = st.session_state.keywords
 model_name = st.selectbox("Select Embedding Model", ["sentence-transformers/LaBSE", "sentence-transformers/all-MiniLM-L6-v2", "OpenAI Embeddings"])
 openai_api_key = st.text_input("OpenAI API Key (optional)", type="password")
 
+# Instructions tooltip
+with st.expander("ℹ️ Instructions (click for details)"):
+    st.markdown(
+        """
+        **How to use this tool:**
+        1. Enter your **Primary Keyword** in the input field.
+        2. Provide a list of **Keywords to Compare** (separated by new lines or commas).
+        3. Select an **Embedding Model** to compute keyword embeddings.
+        4. If using OpenAI embeddings, input your **API Key**.
+        5. Click **Calculate Similarities** to compute and rank your keywords by relevance.
+
+        **What you'll get:**
+        - A sorted table of your comparison keywords based on their cosine similarity to your primary keyword.
+        - Option to download the results as a CSV file.
+        """
+    )
+
 # Process Button
 if st.button("Calculate Similarities"):
     if not primary_keyword or not keywords:
@@ -94,21 +97,23 @@ if st.button("Calculate Similarities"):
 
         # Display results
         st.header("Results")
-        df_results = pd.DataFrame(sorted_results)
-        st.table(df_results)
-
-        # Download results as CSV
+        
+        # Download results as CSV - moved up to appear right after Results title
         st.download_button(
-            label="Download Results as CSV",
-            data=df_results.to_csv(index=False),
+            label="📥 Download Results as CSV",
+            data=pd.DataFrame(sorted_results).to_csv(index=False),
             file_name="cosine_similarity_results.csv",
             mime="text/csv"
         )
+        
+        # Display the results table
+        df_results = pd.DataFrame(sorted_results)
+        st.table(df_results)
 
-        # Debugging/Intermediate Data
-        st.header("Debugging Info")
-        st.write("Primary Embedding:", primary_embedding)
-        st.write("Keyword Embeddings:", keyword_embeddings)
+        # Debugging/Intermediate Data - moved below results
+        with st.expander("🔧 Technical Details (click to expand)"):
+            st.write("Primary Embedding:", primary_embedding)
+            st.write("Keyword Embeddings:", keyword_embeddings)
 
 # Footer
 st.markdown("---")