app.py

import os
import tiger
import cas9on
import cas9off
import pandas as pd
import streamlit as st
from pygenomeviz import Genbank, GenomeViz
import numpy as np
from pathlib import Path


# title and documentation
st.markdown(Path('crisprTool.md').read_text(), unsafe_allow_html=True)
st.divider()

CRISPR_MODELS = ['Cas9', 'Cas12', 'Cas13d']

selected_model = st.selectbox('Select CRISPR model:', CRISPR_MODELS, key='selected_model')
cas9on_path = 'cas9_model/on-cla.h5'

@st.cache_data
def convert_df(df):
            # IMPORTANT: Cache the conversion to prevent computation on every rerun
            return df.to_csv().encode('utf-8')


def mode_change_callback():
        if st.session_state.mode in {tiger.RUN_MODES['all'], tiger.RUN_MODES['titration']}:  # TODO: support titration
            st.session_state.check_off_targets = False
            st.session_state.disable_off_target_checkbox = True
        else:
            st.session_state.disable_off_target_checkbox = False


def progress_update(update_text, percent_complete):
        with progress.container():
            st.write(update_text)
            st.progress(percent_complete / 100)


def initiate_run():
        # initialize state variables
        st.session_state.transcripts = None
        st.session_state.input_error = None
        st.session_state.on_target = None
        st.session_state.titration = None
        st.session_state.off_target = None

        # initialize transcript DataFrame
        transcripts = pd.DataFrame(columns=[tiger.ID_COL, tiger.SEQ_COL])

        # manual entry
        if st.session_state.entry_method == ENTRY_METHODS['manual']:
            transcripts = pd.DataFrame({
                tiger.ID_COL: ['ManualEntry'],
                tiger.SEQ_COL: [st.session_state.manual_entry]
            }).set_index(tiger.ID_COL)

        # fasta file upload
        elif st.session_state.entry_method == ENTRY_METHODS['fasta']:
            if st.session_state.fasta_entry is not None:
                fasta_path = st.session_state.fasta_entry.name
                with open(fasta_path, 'w') as f:
                    f.write(st.session_state.fasta_entry.getvalue().decode('utf-8'))
                transcripts = tiger.load_transcripts([fasta_path], enforce_unique_ids=False)
                os.remove(fasta_path)

        # convert to upper case as used by tokenizer
        transcripts[tiger.SEQ_COL] = transcripts[tiger.SEQ_COL].apply(lambda s: s.upper().replace('U', 'T'))

        # ensure all transcripts have unique identifiers
        if transcripts.index.has_duplicates:
                st.session_state.input_error = "Duplicate transcript ID's detected in fasta file"

        # ensure all transcripts only contain nucleotides A, C, G, T, and wildcard N
        elif not all(transcripts[tiger.SEQ_COL].apply(lambda s: set(s).issubset(tiger.NUCLEOTIDE_TOKENS.keys()))):
            st.session_state.input_error = 'Transcript(s) must only contain upper or lower case A, C, G, and Ts or Us'

        # ensure all transcripts satisfy length requirements
        elif any(transcripts[tiger.SEQ_COL].apply(lambda s: len(s) < tiger.TARGET_LEN)):
            st.session_state.input_error = 'Transcript(s) must be at least {:d} bases.'.format(tiger.TARGET_LEN)

        # run model if we have any transcripts
        elif len(transcripts) > 0:
            st.session_state.transcripts = transcripts

# Check if the selected model is Cas9
if selected_model == 'Cas9':
    # Use a radio button to select enzymes, making sure only one can be selected at a time
    target_selection = st.radio(
        "Select either on-target or off-target:",
        ('on-target', 'off-target'),
        key='target_selection'
    )

    if target_selection == 'on-target':
        # Gene symbol entry
        gene_symbol = st.text_input('Enter a Gene Symbol:', key='gene_symbol')

        # Prediction button
        predict_button = st.button('Predict on-target')

        # Process predictions
        if predict_button and gene_symbol:
            predictions, gene_sequence = cas9on.process_gene(gene_symbol, cas9on_path)
            sorted_predictions = sorted(predictions, key=lambda x: x[-1], reverse=True)[:10]
            st.session_state['on_target_results'] = sorted_predictions

            if 'on_target_results' in st.session_state and st.session_state['on_target_results']:
                df = pd.DataFrame(st.session_state['on_target_results'],
                                  columns=["Gene ID", "Start Pos", "End Pos", "Strand", "gRNA", "Prediction"])

                if gene_sequence:  # Ensure gene_sequence is not empty
                    genbank_file_path = f"{gene_symbol}_crispr_targets.gb"
                    cas9on.generate_genbank_file_from_df(df, gene_sequence, gene_symbol, genbank_file_path)
                    st.write('Top on-target predictions:')
                    st.dataframe(df)

                    # Add a download button for the GenBank file
                    with open(genbank_file_path, "rb") as file:
                        st.download_button(
                            label="Download GenBank File",
                            data=file,
                            file_name=genbank_file_path,
                            mime="text/x-genbank"
                        )

                    # Visualize the GenBank file using pyGenomeViz
                    gv = GenomeViz(
                        feature_track_ratio=0.3,
                        tick_track_ratio=0.5,
                        tick_style="axis",
                    )

                    gbk = Genbank(genbank_file_path)
                    track = gv.add_feature_track(gbk.name, gbk.range_size)
                    # Make sure you are adding features of the type that are present in your GenBank file
                    track.add_genbank_features(gbk, feature_types=["misc_feature"])  # Specify feature types if needed

                    fig = gv.plotfig()
                    st.pyplot(fig)

                    # Clean up the GenBank file after visualization
                    os.remove(genbank_file_path)

    elif target_selection == 'off-target':
        ENTRY_METHODS = dict(
            manual='Manual entry of target sequence',
            txt="txt file upload"
        )
        if __name__ == '__main__':
            # app initialization for Cas9 off-target
            if 'target_sequence' not in st.session_state:
                st.session_state.target_sequence = None
            if 'input_error' not in st.session_state:
                st.session_state.input_error = None
            if 'off_target_results' not in st.session_state:
                st.session_state.off_target_results = None

            # target sequence entry
            st.selectbox(
                label='How would you like to provide target sequences?',
                options=ENTRY_METHODS.values(),
                key='entry_method',
                disabled=st.session_state.target_sequence is not None
            )
            if st.session_state.entry_method == ENTRY_METHODS['manual']:
                st.text_input(
                    label='Enter on/off sequences:',
                    key='manual_entry',
                    placeholder='Enter on/off sequences like:GGGTGGGGGGAGTTTGCTCCAGG,AGGTGGGGTGA_TTTGCTCCAGG',
                    disabled=st.session_state.target_sequence is not None
                )
            elif st.session_state.entry_method == ENTRY_METHODS['txt']:
                st.file_uploader(
                    label='Upload a txt file:',
                    key='txt_entry',
                    disabled=st.session_state.target_sequence is not None
                )

            # prediction button
            if st.button('Predict off-target'):
                if st.session_state.entry_method == ENTRY_METHODS['manual']:
                    user_input = st.session_state.manual_entry
                    if user_input:  # Check if user_input is not empty
                        predictions = cas9off.process_input_and_predict(user_input, input_type='manual')
                elif st.session_state.entry_method == ENTRY_METHODS['txt']:
                    uploaded_file = st.session_state.txt_entry
                    if uploaded_file is not None:
                        # Read the uploaded file content
                        file_content = uploaded_file.getvalue().decode("utf-8")
                        predictions = cas9off.process_input_and_predict(file_content, input_type='manual')

                st.session_state.off_target_results = predictions
            else:
                predictions = None
            progress = st.empty()

            # input error display
            error = st.empty()
            if st.session_state.input_error is not None:
                error.error(st.session_state.input_error, icon="🚨")
            else:
                error.empty()

            # off-target results display
            off_target_results = st.empty()
            if st.session_state.off_target_results is not None:
                with off_target_results.container():
                    if len(st.session_state.off_target_results) > 0:
                        st.write('Off-target predictions:', st.session_state.off_target_results)
                        st.download_button(
                            label='Download off-target predictions',
                            data=convert_df(st.session_state.off_target_results),
                            file_name='off_target_results.csv',
                            mime='text/csv'
                        )
                    else:
                        st.write('No significant off-target effects detected!')
            else:
                off_target_results.empty()

            # running the CRISPR-Net model for off-target predictions
            if st.session_state.target_sequence is not None:
                st.session_state.off_target_results = cas9off.predict_off_targets(
                    target_sequence=st.session_state.target_sequence,
                    status_update_fn=progress_update
                )
                st.session_state.target_sequence = None
                st.experimental_rerun()

elif selected_model == 'Cas12':
        # Placeholder for Cas12 model loading
        # TODO: Implement Cas12 model loading logic
        raise NotImplementedError("Cas12 model loading not implemented yet.")
elif selected_model == 'Cas13d':
        ENTRY_METHODS = dict(
        manual='Manual entry of single transcript',
        fasta="Fasta file upload (supports multiple transcripts if they have unique ID's)"
        )

        if __name__ == '__main__':
            # app initialization
            if 'mode' not in st.session_state:
                st.session_state.mode = tiger.RUN_MODES['all']
                st.session_state.disable_off_target_checkbox = True
            if 'entry_method' not in st.session_state:
                st.session_state.entry_method = ENTRY_METHODS['manual']
            if 'transcripts' not in st.session_state:
                st.session_state.transcripts = None
            if 'input_error' not in st.session_state:
                st.session_state.input_error = None
            if 'on_target' not in st.session_state:
                st.session_state.on_target = None
            if 'titration' not in st.session_state:
                st.session_state.titration = None
            if 'off_target' not in st.session_state:
                st.session_state.off_target = None

            # mode selection
            col1, col2 = st.columns([0.65, 0.35])
            with col1:
                st.radio(
                    label='What do you want to predict?',
                    options=tuple(tiger.RUN_MODES.values()),
                    key='mode',
                    on_change=mode_change_callback,
                    disabled=st.session_state.transcripts is not None,
                )
            with col2:
                st.checkbox(
                    label='Find off-target effects (slow)',
                    key='check_off_targets',
                    disabled=st.session_state.disable_off_target_checkbox or st.session_state.transcripts is not None
                )

            # transcript entry
            st.selectbox(
                label='How would you like to provide transcript(s) of interest?',
                options=ENTRY_METHODS.values(),
                key='entry_method',
                disabled=st.session_state.transcripts is not None
            )
            if st.session_state.entry_method == ENTRY_METHODS['manual']:
                st.text_input(
                    label='Enter a target transcript:',
                    key='manual_entry',
                    placeholder='Upper or lower case',
                    disabled=st.session_state.transcripts is not None
                )
            elif st.session_state.entry_method == ENTRY_METHODS['fasta']:
                st.file_uploader(
                    label='Upload a fasta file:',
                    key='fasta_entry',
                    disabled=st.session_state.transcripts is not None
                )

            # let's go!
            st.button(label='Get predictions!', on_click=initiate_run, disabled=st.session_state.transcripts is not None)
            progress = st.empty()

            # input error
            error = st.empty()
            if st.session_state.input_error is not None:
                error.error(st.session_state.input_error, icon="🚨")
            else:
                error.empty()

            # on-target results
            on_target_results = st.empty()
            if st.session_state.on_target is not None:
                with on_target_results.container():
                    st.write('On-target predictions:', st.session_state.on_target)
                    st.download_button(
                        label='Download on-target predictions',
                        data=convert_df(st.session_state.on_target),
                        file_name='on_target.csv',
                        mime='text/csv'
                    )
            else:
                on_target_results.empty()

            # titration results
            titration_results = st.empty()
            if st.session_state.titration is not None:
                with titration_results.container():
                    st.write('Titration predictions:', st.session_state.titration)
                    st.download_button(
                        label='Download titration predictions',
                        data=convert_df(st.session_state.titration),
                        file_name='titration.csv',
                        mime='text/csv'
                    )
            else:
                titration_results.empty()

            # off-target results
            off_target_results = st.empty()
            if st.session_state.off_target is not None:
                with off_target_results.container():
                    if len(st.session_state.off_target) > 0:
                        st.write('Off-target predictions:', st.session_state.off_target)
                        st.download_button(
                            label='Download off-target predictions',
                            data=convert_df(st.session_state.off_target),
                            file_name='off_target.csv',
                            mime='text/csv'
                        )
                    else:
                        st.write('We did not find any off-target effects!')
            else:
                off_target_results.empty()

            # keep trying to run model until we clear inputs (streamlit UI changes can induce race-condition reruns)
            if st.session_state.transcripts is not None:
                st.session_state.on_target, st.session_state.titration, st.session_state.off_target = tiger.tiger_exhibit(
                    transcripts=st.session_state.transcripts,
                    mode={v: k for k, v in tiger.RUN_MODES.items()}[st.session_state.mode],
                    check_off_targets=st.session_state.check_off_targets,
                    status_update_fn=progress_update
                )
                st.session_state.transcripts = None
                st.experimental_rerun()