Spaces:
Sleeping
Sleeping
fixing checkboxes
Browse files- requirements.txt +1 -1
- src/display.py +8 -5
requirements.txt
CHANGED
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@@ -4,4 +4,4 @@ matplotlib
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plotly
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streamlit-nightly
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streamlit-aggrid
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streamlit-searchbox
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plotly
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streamlit-nightly
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streamlit-aggrid
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streamlit-searchbox==0.1.8
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src/display.py
CHANGED
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@@ -41,11 +41,11 @@ def display_app():
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# Dynamic search boxes
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def search_model(model_name: str):
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model_list = None
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if model_name is not None:
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models = dataframe["model_name"].str.contains(model_name)
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model_list = dataframe["model_name"][models]
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else:
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model_list = []
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return model_list
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model_list = []
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@@ -114,7 +114,6 @@ def display_app():
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row = dataframe[dataframe["model_name"] == model_three]
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row[categories_display] = row[categories_display]*100
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model_list.append(row.to_dict("records")[0])
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-
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subdata = dataframe.head(1)
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if len(subdata) > 0:
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model_name = subdata["model_name"].values[0]
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@@ -124,7 +123,9 @@ def display_app():
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with column2:
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if grid_response['selected_rows'] is not None and len(grid_response['selected_rows']) > 0:
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figure = None
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-
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model_list = model_list[:3]
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model_list = sorted(model_list, key = lambda x: x["Average"], reverse = True)
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@@ -201,4 +202,6 @@ def display_app():
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st.markdown("For more details, hover over the radar chart.")
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else:
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st.markdown("**Model name:** %s" % model_name)
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st.markdown("For more details, select the first model in the list/leaderboard.")
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# Dynamic search boxes
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def search_model(model_name: str):
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model_list = None
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if model_name is not None or model_name != "":
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models = dataframe["model_name"].str.contains(model_name)
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model_list = dataframe["model_name"][models]
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else:
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model_list = dataframe["model_name"]
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return model_list
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model_list = []
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row = dataframe[dataframe["model_name"] == model_three]
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row[categories_display] = row[categories_display]*100
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model_list.append(row.to_dict("records")[0])
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subdata = dataframe.head(1)
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if len(subdata) > 0:
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model_name = subdata["model_name"].values[0]
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with column2:
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if grid_response['selected_rows'] is not None and len(grid_response['selected_rows']) > 0:
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figure = None
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#grid_response is now a Pandas dataframe, we need to
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# convert to dict in order to merge with the searchboxes' results
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model_list += grid_response['selected_rows'].to_dict("records")
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model_list = model_list[:3]
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model_list = sorted(model_list, key = lambda x: x["Average"], reverse = True)
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st.markdown("For more details, hover over the radar chart.")
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else:
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st.markdown("**Model name:** %s" % model_name)
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st.markdown("For more details, select the first model in the list/leaderboard.")
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