Add views

views.py

@@ -0,0 +1,80 @@
+import streamlit as st
+import vec2text
+import torch
+from umap import UMAP
+import plotly.express as px
+import numpy as np
+from streamlit_plotly_events import plotly_events
+from resources import reduce_embeddings
+import utils
+import pandas as pd
+
+
+def diffs(embeddings: np.ndarray, corrector):
+    st.text(f"Embedding shape: {embeddings.shape}")
+    st.html('<a href="https://www.flaticon.com/free-icons/array" title="array icons">Array icons created by Voysla - Flaticon</a>')
+
+def plot(df: pd.DataFrame, embeddings: np.ndarray, vectors_2d, reducer, corrector):
+    
+
+    # Add a scatter plot using Plotly
+    fig = px.scatter(
+        x=vectors_2d[:, 0], 
+        y=vectors_2d[:, 1], 
+        opacity=0.6,
+        hover_data={"Title": df["title"]}, 
+        labels={'x': 'UMAP Dimension 1', 'y': 'UMAP Dimension 2'},
+        title="UMAP Scatter Plot of Reddit Titles",
+        color_discrete_sequence=["#ff504c"]  # Set default blue color for points
+    )
+
+    # Customize the layout to adapt to browser settings (light/dark mode)
+    fig.update_layout(
+        template=None,  # Let Plotly adapt automatically based on user settings
+        plot_bgcolor="rgba(0, 0, 0, 0)",
+        paper_bgcolor="rgba(0, 0, 0, 0)"
+    )
+
+    x, y = 0.0, 0.0
+    vec = np.array([x, y]).astype("float32")
+
+    # Add a card container to the right of the content with Streamlit columns
+    col1, col2 = st.columns([0.6, 0.4])  # 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.markdown(
+            f"### Selected text:\n```console\n{df.title.iloc[closest_sentence_index] if closest_sentence_index > -1 else '[no selected text]'}\n```"
+        )