Fix app?

app.py

@@ -13,7 +13,7 @@ from sklearn.decomposition import PCA
 from streamlit_plotly_events import plotly_events
 import plotly.graph_objects as go
 import logging
-
+import utils
 # Activate tqdm with pandas
 tqdm.pandas()
 
@@ -40,7 +40,9 @@ def file_cache(file_path):
 @st.cache_resource
 def vector_compressor_from_config():
     # Return UMAP with 2 components for dimensionality reduction
-    return UMAP(n_components=2)
+    # return UMAP(n_components=2)
+    return PCA(n_components=2)
+
 
 # Caching the dataframe since loading from an external source can be time-consuming
 @st.cache_data
@@ -100,50 +102,50 @@ fig.update_layout(
 )
 
 x, y = 0.0, 0.0
-
-# Display the scatterplot and capture click events
-selected_points = plotly_events(fig, click_event=True, hover_event=False, override_height=600, override_width="100%")
-
-# Sidebar for additional information
-st.sidebar.header("Scatter Plot Info")
-st.sidebar.write("""
-This scatter plot visualizes the UMAP dimensionality reduction of Reddit post titles. 
-Each point represents a post, with similar titles being positioned closer together.
-""")
-st.sidebar.write("Use the form below to select coordinates or click on a point in the scatter plot.")
-st.sidebar.markdown("---")
-
-st.sidebar.header("How to Use")
-st.sidebar.write("""
-1. **Click a point** in the scatter plot to see the corresponding coordinates.
-2. **Adjust the coordinates** using the form inputs if needed.
-3. **Submit** to see the reconstructed text output.
-""")
-
-# Form for inputting coordinates
-with st.form(key="form1"):
-    # If a point is clicked, handle the embedding inversion
-    if selected_points:
-        clicked_point = selected_points[0]
-        x_coord = x = clicked_point['x']
-        y_coord = y = clicked_point['y']
-
-    x = st.number_input("X Coordinate", value=x, format="%.10f")
-    y = st.number_input("Y Coordinate", value=y, format="%.10f")
-
-    submit_button = st.form_submit_button("Submit")
-
-    if selected_points or submit_button:
-        inferred_embedding = reducer.inverse_transform(np.array([[x, y]]) if not isinstance(reducer, UMAP) else np.array([[x, y]]))
-        inferred_embedding = inferred_embedding.astype("float32")
-
-        output = vec2text.invert_embeddings(
-            embeddings=torch.tensor(inferred_embedding).cuda(),
-            corrector=corrector,
-            num_steps=20,
-        )
-
-        st.text(str(output))
-        st.text(str(inferred_embedding))
-    else:
-        st.text("Click on a point in the scatterplot to see its coordinates.")
+vec = np.array([x, y]).astype("float32")
+
+# Add a card container to the right of the content with Streamlit columns
+col1, col2 = st.columns([3, 1])  # Adjusting ratio to allocate space for the card container
+
+with col1:
+    # Main content stays here (scatterplot, form, etc.)
+    selected_points = plotly_events(fig, click_event=True, hover_event=False, #override_height=600, override_width="100%"
+                                    )
+    with st.form(key="form1_main"):
+        if selected_points:
+            clicked_point = selected_points[0]
+            x_coord = x = clicked_point['x']
+            y_coord = y = clicked_point['y']
+
+        x = st.number_input("X Coordinate", value=x, format="%.10f")
+        y = st.number_input("Y Coordinate", value=y, format="%.10f")
+        vec = np.array([x, y]).astype("float32")
+
+
+        submit_button = st.form_submit_button("Submit")
+
+        if selected_points or submit_button:
+            inferred_embedding = reducer.inverse_transform(np.array([[x, y]]) if not isinstance(reducer, UMAP) else np.array([[x, y]]))
+            inferred_embedding = inferred_embedding.astype("float32")
+
+            output = vec2text.invert_embeddings(
+                embeddings=torch.tensor(inferred_embedding).cuda(),
+                corrector=corrector,
+                num_steps=20,
+            )
+
+            st.text(str(output))
+            st.text(str(inferred_embedding))
+        else:
+            st.text("Click on a point in the scatterplot to see its coordinates.")
+
+with col2:
+    closest_sentence_index = utils.find_exact_match(vectors_2d, vec, decimals=3)
+    st.write(f"{vectors_2d.dtype}  {vec.dtype}")
+    if closest_sentence_index > -1:
+        st.write(df["title"].iloc[closest_sentence_index])
+    # Card content
+    st.markdown("## Card Container")
+    st.write("This is an additional card container to the right of the main content.")
+    st.write("You can use this space to show additional information, actions, or insights.")
+    st.button("Card Button")