Files changed (1) hide show
  1. app.py +3 -1
app.py CHANGED
@@ -439,12 +439,14 @@ elif selected_model == 'Cas12':
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  predictions, gene_sequence, exons = cas12.process_gene(gene_symbol,cas12_path)
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  sorted_predictions = sorted(predictions, key=lambda x: x[-1], reverse=True)[:10]
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  st.session_state['on_target_results'] = sorted_predictions
 
 
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  st.success('Prediction completed!')
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  # Visualization and file generation
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  if 'on_target_results' in st.session_state and st.session_state['on_target_results']:
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  df = pd.DataFrame(st.session_state['on_target_results'],
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- columns=["Gene ID", "Start Pos", "End Pos", "Strand", "Target", "gRNA", "Prediction"])
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  st.dataframe(df)
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  # Now create a Plotly plot with the sorted_predictions
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  fig = go.Figure()
 
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  predictions, gene_sequence, exons = cas12.process_gene(gene_symbol,cas12_path)
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  sorted_predictions = sorted(predictions, key=lambda x: x[-1], reverse=True)[:10]
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  st.session_state['on_target_results'] = sorted_predictions
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+ st.session_state['gene_sequence'] = gene_sequence # Save gene sequence in session state
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+ st.session_state['exons'] = exons # Store exon data
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  st.success('Prediction completed!')
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  # Visualization and file generation
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  if 'on_target_results' in st.session_state and st.session_state['on_target_results']:
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  df = pd.DataFrame(st.session_state['on_target_results'],
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+ columns=["Chr", "Start Pos", "End Pos", "Strand", "Transcript", "Exon", "Target", "gRNA", "Prediction"])
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  st.dataframe(df)
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  # Now create a Plotly plot with the sorted_predictions
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  fig = go.Figure()