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Runtime error
Runtime error
Update app.py
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app.py
CHANGED
@@ -174,37 +174,53 @@ with tab1:
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if st.button("Load Data", key='load_data'):
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if site_var1 == 'Draftkings':
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if 'working_seed' in st.session_state:
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st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
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else:
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st.session_state.working_seed = DK_seed.copy()
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st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
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elif site_var1 == 'Fanduel':
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if 'working_seed' in st.session_state:
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st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
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else:
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st.session_state.working_seed = FD_seed.copy()
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st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
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with st.container():
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if 'data_export_display' in st.session_state:
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if st.button("Load Data", key='load_data'):
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if site_var1 == 'Draftkings':
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if 'working_seed' in st.session_state:
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column_indices = [12, 13]
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filter_mask = np.logical_or(
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np.isin(st.session_state.working_seed[:, column_indices[0]], team_var2),
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np.isin(st.session_state.working_seed[:, column_indices[1]], stack_var2)
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)
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st.session_state.working_seed = st.session_state.working_seed[filter_mask]
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st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
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if 'data_export_display' in st.session_state:
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st.session_state.data_export_freq = pd.DataFrame(calculate_DK_value_frequencies(st.session_state.working_seed), columns=['Player', 'Exposure'])
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st.session_state.data_export_freq = st.session_state.data_export_freq.sort_values(by='Exposure', ascending=False)
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else:
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st.session_state.working_seed = DK_seed.copy()
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column_indices = [12, 13]
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filter_mask = np.logical_or(
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np.isin(st.session_state.working_seed[:, column_indices[0]], team_var2),
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np.isin(st.session_state.working_seed[:, column_indices[1]], stack_var2)
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)
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st.session_state.working_seed = st.session_state.working_seed[filter_mask]
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st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
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if 'data_export_display' in st.session_state:
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st.session_state.data_export_freq = pd.DataFrame(calculate_DK_value_frequencies(st.session_state.working_seed), columns=['Player', 'Exposure'])
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st.session_state.data_export_freq = st.session_state.data_export_freq.sort_values(by='Exposure', ascending=False)
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elif site_var1 == 'Fanduel':
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if 'working_seed' in st.session_state:
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column_indices = [11, 12]
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filter_mask = np.logical_or(
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np.isin(st.session_state.working_seed[:, column_indices[0]], team_var2),
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np.isin(st.session_state.working_seed[:, column_indices[1]], stack_var2)
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)
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st.session_state.working_seed = st.session_state.working_seed[filter_mask]
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st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
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if 'data_export_display' in st.session_state:
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st.session_state.data_export_freq = pd.DataFrame(calculate_FD_value_frequencies(st.session_state.working_seed), columns=['Player', 'Exposure'])
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st.session_state.data_export_freq = st.session_state.data_export_freq.sort_values(by='Exposure', ascending=False)
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else:
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st.session_state.working_seed = FD_seed.copy()
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column_indices = [11, 12]
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filter_mask = np.logical_or(
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np.isin(st.session_state.working_seed[:, column_indices[0]], team_var2),
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np.isin(st.session_state.working_seed[:, column_indices[1]], stack_var2)
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)
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st.session_state.working_seed = st.session_state.working_seed[filter_mask]
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st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
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if 'data_export_display' in st.session_state:
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st.session_state.data_export_freq = pd.DataFrame(calculate_FD_value_frequencies(st.session_state.working_seed), columns=['Player', 'Exposure'])
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st.session_state.data_export_freq = st.session_state.data_export_freq.sort_values(by='Exposure', ascending=False)
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with st.container():
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if 'data_export_display' in st.session_state:
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