diff --git a/Archive/agraphTest.py b/Archive/agraphTest.py
new file mode 100644
index 0000000000000000000000000000000000000000..b7570b1c1b08d520bc56e3aced9a6be77d799fee
--- /dev/null
+++ b/Archive/agraphTest.py
@@ -0,0 +1,170 @@
+import os
+
+import streamlit as st
+import torch
+import pandas as pd
+import numpy as np
+
+from datasets import load_dataset, Dataset, load_from_disk
+from huggingface_hub import login
+from streamlit_agraph import agraph, Node, Edge, Config
+from sklearn.manifold import TSNE
+
+
+@st.cache_data
+def load_hf_dataset():
+ # login to huggingface
+ login(token=os.environ.get("HF_TOKEN"))
+
+ # load from huggingface
+ roster = pd.DataFrame(load_dataset('MAPS-research/GEMRec-Roster', split='train'))
+ promptBook = pd.DataFrame(load_dataset('MAPS-research/GEMRec-Metadata', split='train'))
+
+ # process dataset
+ roster = roster[['model_id', 'model_name', 'modelVersion_id', 'modelVersion_name',
+ 'model_download_count']].drop_duplicates().reset_index(drop=True)
+
+ # add 'custom_score_weights' column to promptBook if not exist
+ if 'weighted_score_sum' not in promptBook.columns:
+ promptBook.loc[:, 'weighted_score_sum'] = 0
+
+ # merge roster and promptbook
+ promptBook = promptBook.merge(roster[['model_id', 'model_name', 'modelVersion_id', 'modelVersion_name', 'model_download_count']],
+ on=['model_id', 'modelVersion_id'], how='left')
+
+ # add column to record current row index
+ promptBook.loc[:, 'row_idx'] = promptBook.index
+
+ return roster, promptBook
+
+
+@st.cache_data
+def calc_tsne(prompt_id):
+ print('==> loading feats')
+ feats = {}
+ for pt in os.listdir('../data/feats'):
+ if pt.split('.')[-1] == 'pt' and pt.split('.')[0].isdigit():
+ feats[pt.split('.')[0]] = torch.load(os.path.join('../data/feats', pt))
+
+ print('==> applying t-SNE')
+ # apply t-SNE to entries in each feat in feats to get 2D coordinates
+ tsne = TSNE(n_components=2, random_state=0)
+ # for k, v in tqdm(feats.items()):
+ # feats[k]['tsne'] = tsne.fit_transform(v['all'].numpy())
+ # prompt_id = '90'
+ feats[prompt_id]['tsne'] = tsne.fit_transform(feats[prompt_id]['all'].numpy())
+
+ feats_df = pd.DataFrame(feats[prompt_id]['tsne'], columns=['x', 'y'])
+ feats_df['prompt_id'] = prompt_id
+
+ keys = []
+ for k in feats[prompt_id].keys():
+ if k != 'all' and k != 'tsne':
+ keys.append(int(k.item()))
+
+ feats_df['modelVersion_id'] = keys
+
+
+ return feats_df
+
+ # print(feats[prompt_id]['tsne'])
+
+
+if __name__ == '__main__':
+ st.set_page_config(layout="wide")
+
+ # load dataset
+ roster, promptBook = load_hf_dataset()
+ # prompt_id = '20'
+
+ with st.sidebar:
+ st.write('## Select Prompt')
+ prompts = promptBook['prompt_id'].unique().tolist()
+ # sort prompts by prompt_id
+ prompts.sort()
+ prompt_id = st.selectbox('Select Prompt', prompts, index=0)
+ physics = st.checkbox('Enable Physics')
+
+ feats_df = calc_tsne(str(prompt_id))
+
+ # keys = []
+ # for k in feats[prompt_id].keys():
+ # if k != 'all' and k != 'tsne':
+ # keys.append(int(k.item()))
+
+ # print(keys)
+
+ data = []
+ for idx in feats_df.index:
+ modelVersion_id = feats_df.loc[idx, 'modelVersion_id']
+ image_id = promptBook[(promptBook['modelVersion_id'] == modelVersion_id) & (
+ promptBook['prompt_id'] == int(prompt_id))].reset_index(drop=True).loc[0, 'image_id']
+ image_url = f"https://modelcofferbucket.s3-accelerate.amazonaws.com/{image_id}.png"
+ scale = 50
+ data.append((feats_df.loc[idx, 'x'] * scale, feats_df.loc[idx, 'y'] * scale, image_url))
+
+ image_size = promptBook[(promptBook['image_id'] == image_id)].reset_index(drop=True).loc[0, 'size'].split('x')
+
+ nodes = []
+ edges = []
+
+ for d in data:
+ nodes.append( Node(id=d[2],
+ # label=str(items.loc[idx, 'model_name']),
+ size=20,
+ shape="image",
+ image=d[2],
+ x=[d[0]],
+ y=[d[1]],
+ fixed=False if physics else True,
+ color={'background': '#00000', 'border': '#ffffff'},
+ shadow={'enabled': True, 'color': 'rgba(0,0,0,0.4)', 'size': 10, 'x': 1, 'y': 1},
+ # borderWidth=1,
+ # shapeProperties={'useBorderWithImage': True},
+ )
+ )
+
+
+ # nodes.append( Node(id="Spiderman",
+ # label="Peter Parker",
+ # size=25,
+ # shape="circularImage",
+ # image="http://marvel-force-chart.surge.sh/marvel_force_chart_img/top_spiderman.png")
+ # ) # includes **kwargs
+ # nodes.append( Node(id="Captain_Marvel",
+ # label="Carol Danvers",
+ # fixed=True,
+ # size=25,
+ # shape="circularImage",
+ # image="http://marvel-force-chart.surge.sh/marvel_force_chart_img/top_captainmarvel.png")
+ # )
+ # edges.append( Edge(source="Captain_Marvel",
+ # label="friend_of",
+ # target="Spiderman",
+ # length=200,
+ # # **kwargs
+ # )
+ # )
+ #
+ config = Config(width='100%',
+ height=800,
+ directed=True,
+ physics=physics,
+ hierarchical=False,
+ # **kwargs
+ )
+
+ cols = st.columns([3, 1], gap='large')
+
+ with cols[0]:
+ return_value = agraph(nodes=nodes,
+ edges=edges,
+ config=config)
+
+ # st.write(return_value)
+
+ with cols[1]:
+ try:
+ st.image(return_value, use_column_width=True)
+ except:
+ st.write('No image selected')
\ No newline at end of file
diff --git a/Archive/bokehTest.py b/Archive/bokehTest.py
new file mode 100644
index 0000000000000000000000000000000000000000..2e9ecfa1051018dcc95460ebd8a7b25748a2dca2
--- /dev/null
+++ b/Archive/bokehTest.py
@@ -0,0 +1,182 @@
+import os
+
+import streamlit as st
+import torch
+import pandas as pd
+import numpy as np
+import requests
+
+from bokeh.plotting import figure, show
+from bokeh.models import HoverTool, ColumnDataSource, CustomJSHover
+from bokeh.embed import file_html
+from bokeh.resources import CDN # Import CDN here
+from datasets import load_dataset, Dataset, load_from_disk
+from huggingface_hub import login
+from sklearn.manifold import TSNE
+from tqdm import tqdm
+
+
+@st.cache_data
+def load_hf_dataset():
+ # login to huggingface
+ login(token=os.environ.get("HF_TOKEN"))
+
+ # load from huggingface
+ roster = pd.DataFrame(load_dataset('MAPS-research/GEMRec-Roster', split='train'))
+ promptBook = pd.DataFrame(load_dataset('MAPS-research/GEMRec-Metadata', split='train'))
+
+ # process dataset
+ roster = roster[['model_id', 'model_name', 'modelVersion_id', 'modelVersion_name',
+ 'model_download_count']].drop_duplicates().reset_index(drop=True)
+
+ # add 'custom_score_weights' column to promptBook if not exist
+ if 'weighted_score_sum' not in promptBook.columns:
+ promptBook.loc[:, 'weighted_score_sum'] = 0
+
+ # merge roster and promptbook
+ promptBook = promptBook.merge(roster[['model_id', 'model_name', 'modelVersion_id', 'modelVersion_name', 'model_download_count']],
+ on=['model_id', 'modelVersion_id'], how='left')
+
+ # add column to record current row index
+ promptBook.loc[:, 'row_idx'] = promptBook.index
+
+ return roster, promptBook
+
+def show_with_bokeh(data, streamlit=False):
+ # Extract x, y coordinates and image URLs
+ x_coords, y_coords, image_urls = zip(*data)
+
+ # Create a ColumnDataSource
+ source = ColumnDataSource(data=dict(x=x_coords, y=y_coords, image=image_urls))
+
+ # Create a figure
+ p = figure(width=800, height=600)
+
+ # Add scatter plot
+ scatter = p.scatter(x='x', y='y', size=20, source=source)
+
+ # Define hover tool
+ hover = HoverTool()
+ # hover.tooltips = """
+ #
+ #
+ #
+ # """
+ # hover.formatters = {'@image': CustomJSHover(code="""
+ # const index = cb_data.index;
+ # const url = cb_data.source.data['image'][index];
+ # return '';
+ # """)}
+
+ hover.tooltips = """
+
+
+
';
+ """)}
+
+ p.add_tools(hover)
+
+ # Generate HTML with the plot
+ html = file_html(p, CDN, "Interactive Scatter Plot with Hover Images")
+
+ # Save the HTML file or show it
+ # with open("scatter_plot_with_hover_images.html", "w") as f:
+ # f.write(html)
+
+ if streamlit:
+ st.bokeh_chart(p, use_container_width=True)
+ else:
+ show(p)
+
+
+def show_with_bokeh_2(data, image_size=[40, 40], streamlit=False):
+ # Extract x, y coordinates and image URLs
+ x_coords, y_coords, image_urls = zip(*data)
+
+ # Create a ColumnDataSource
+ source = ColumnDataSource(data=dict(x=x_coords, y=y_coords, image=image_urls))
+
+ # Create a figure
+ p = figure(width=800, height=600, aspect_ratio=1.0)
+
+ # Add image glyphs
+ # image_size = 40 # Adjust this size as needed
+ scale = 0.1
+ image_size = [int(image_size[0])*scale, int(image_size[1])*scale]
+ print(image_size)
+ p.image_url(url='image', x='x', y='y', source=source, w=image_size[0], h=image_size[1], anchor="center")
+
+ # Define hover tool
+ hover = HoverTool()
+ hover.tooltips = """
+
+
+