Run the simplest test

app.py

@@ -1,152 +1,158 @@
-import os
-import pickle
+
+
+
+# import os
+# import pickle
 import streamlit as st
-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")
+
+
+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")