Make on click event

app.py

@@ -89,7 +89,7 @@ fig = px.scatter(
     hover_data={"Title": df["title"]}, 
     labels={'x': 'UMAP Dimension 1', 'y': 'UMAP Dimension 2'},
     title="UMAP Scatter Plot of Reddit Titles",
-    color_discrete_sequence=["#01a8d3"]  # Set default blue color for points
+    color_discrete_sequence=["#ff504c"]  # Set default blue color for points
 )
 
 # Customize the layout to adapt to browser settings (light/dark mode)
@@ -98,27 +98,32 @@ fig.update_layout(
     plot_bgcolor="rgba(0, 0, 0, 0)",
     paper_bgcolor="rgba(0, 0, 0, 0)"
 )
-
+x, y = 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%")
-
-# 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']
-
-    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 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.")