Revert back a couple commits

app.py

@@ -1,158 +1,151 @@
-
-
-
-# import os
-# import pickle
+import os
+import pickle
 import streamlit as st
-
-
-st.text("This is a test")
-# import pandas as pd
-# import vec2text
-# import torch
-# from transformers import AutoModel, AutoTokenizer
-# from umap import UMAP
-# from tqdm import tqdm
-# import plotly.express as px
-# import numpy as np
-# 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()
-
-# # Custom file cache decorator
-# def file_cache(file_path):
-#     def decorator(func):
-#         def wrapper(*args, **kwargs):
-#             # Check if the file already exists
-#             if os.path.exists(file_path):
-#                 # Load from cache
-#                 with open(file_path, "rb") as f:
-#                     print(f"Loading cached data from {file_path}")
-#                     return pickle.load(f)
-#             else:
-#                 # Compute and save to cache
-#                 result = func(*args, **kwargs)
-#                 with open(file_path, "wb") as f:
-#                     pickle.dump(result, f)
-#                     print(f"Saving new cache to {file_path}")
-#                 return result
-#         return wrapper
-#     return decorator
-
-# @st.cache_resource
-# def vector_compressor_from_config():
-#     # Return UMAP with 2 components for dimensionality reduction
-#     # 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
-# def load_data():
-#     return pd.read_csv("https://huggingface.co/datasets/marksverdhei/reddit-syac-urls/resolve/main/train.csv")
-
-# df = load_data()
-
-# # Caching the model and tokenizer to avoid reloading
-# # @st.cache_resource
-# # def load_model_and_tokenizer():
-# #     encoder = AutoModel.from_pretrained("sentence-transformers/gtr-t5-base").encoder.to("cuda")
-# #     tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/gtr-t5-base")
-# #     return encoder, tokenizer
-
-# # encoder, tokenizer = load_model_and_tokenizer()
-
-# # Caching the vec2text corrector
-# # @st.cache_resource
-# # def load_corrector():
-# #     return vec2text.load_pretrained_corrector("gtr-base")
-
-# # corrector = load_corrector()
-
-# # Caching the precomputed embeddings since they are stored locally and large
-# @st.cache_data
-# def load_embeddings():
-#     return np.load("syac-title-embeddings.npy")
-
-# embeddings = load_embeddings()
-
-# # Custom cache the UMAP reduction using file_cache decorator
-# @st.cache_data
-# @file_cache(".cache/reducer_embeddings.pickle")
-# def reduce_embeddings(embeddings):
-#     reducer = vector_compressor_from_config()
-#     return reducer.fit_transform(embeddings), reducer
-
-# vectors_2d, reducer = reduce_embeddings(embeddings)
-
-# # 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([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,
-#     #                                 )
-#     selected_points = None
-#     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")
+import pandas as pd
+import vec2text
+import torch
+from transformers import AutoModel, AutoTokenizer
+from umap import UMAP
+from tqdm import tqdm
+import plotly.express as px
+import numpy as np
+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()
+
+# Custom file cache decorator
+def file_cache(file_path):
+    def decorator(func):
+        def wrapper(*args, **kwargs):
+            # Check if the file already exists
+            if os.path.exists(file_path):
+                # Load from cache
+                with open(file_path, "rb") as f:
+                    print(f"Loading cached data from {file_path}")
+                    return pickle.load(f)
+            else:
+                # Compute and save to cache
+                result = func(*args, **kwargs)
+                with open(file_path, "wb") as f:
+                    pickle.dump(result, f)
+                    print(f"Saving new cache to {file_path}")
+                return result
+        return wrapper
+    return decorator
+
+@st.cache_resource
+def vector_compressor_from_config():
+    # Return UMAP with 2 components for dimensionality reduction
+    # 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
+def load_data():
+    return pd.read_csv("https://huggingface.co/datasets/marksverdhei/reddit-syac-urls/resolve/main/train.csv")
+
+df = load_data()
+
+# Caching the model and tokenizer to avoid reloading
+@st.cache_resource
+def load_model_and_tokenizer():
+    encoder = AutoModel.from_pretrained("sentence-transformers/gtr-t5-base").encoder.to("cuda")
+    tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/gtr-t5-base")
+    return encoder, tokenizer
+
+encoder, tokenizer = load_model_and_tokenizer()
+
+# Caching the vec2text corrector
+@st.cache_resource
+def load_corrector():
+    return vec2text.load_pretrained_corrector("gtr-base")
+
+corrector = load_corrector()
+
+# Caching the precomputed embeddings since they are stored locally and large
+@st.cache_data
+def load_embeddings():
+    return np.load("syac-title-embeddings.npy")
+
+embeddings = load_embeddings()
+
+# Custom cache the UMAP reduction using file_cache decorator
+@st.cache_data
+@file_cache(".cache/reducer_embeddings.pickle")
+def reduce_embeddings(embeddings):
+    reducer = vector_compressor_from_config()
+    return reducer.fit_transform(embeddings), reducer
+
+vectors_2d, reducer = reduce_embeddings(embeddings)
+
+# 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([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")