add enzyme buttons

app.py

@@ -15,16 +15,16 @@ selected_model = st.selectbox('Select CRISPR model:', CRISPR_MODELS, key='select
 # Check if the selected model is Cas9
 if selected_model == 'Cas9':
     # Display buttons for the Cas9 model
-    if st.button('SPCas9_U6'):
+    if st.checkbox('SPCas9_U6'):
         # Placeholder for action when SPCas9_U6 is clicked
         pass
-    if st.button('SPCas9_t7'):
+    if st.checkbox('SPCas9_t7'):
         # Placeholder for action when SPCas9_t7 is clicked
         pass
-    if st.button('eSPCas9'):
+    if st.checkbox('eSPCas9'):
         # Placeholder for action when eSPCas9 is clicked
         pass
-    if st.button('SPCas9_HF1'):
+    if st.checkbox('SPCas9_HF1'):
         # Placeholder for action when SPCas9_HF1 is clicked
         pass
 elif selected_model == 'Cas12':
@@ -32,210 +32,204 @@ elif selected_model == 'Cas12':
         # TODO: Implement Cas12 model loading logic
         raise NotImplementedError("Cas12 model loading not implemented yet.")
 elif selected_model == 'Cas13d':
-        # Assuming tiger module is for Cas13
-        tiger.load_model()  # Assuming there's a load_model function in tiger.py
-else:
-        raise ValueError(f"Unknown model: {model_name}")
-
-
-@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():
-
-
-    # Placeholder for dynamic module import based on selected_model
-    # model_module = get_model_module(selected_model)
-    # You will need to implement get_model_module function to import the correct module (cas9, cas12, cas13)
-
-    # ... rest of the initiate_run function ...
-    # 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
-
-
-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
-
-    # title and documentation
-    st.markdown(Path('tiger.md').read_text(), unsafe_allow_html=True)
-    st.divider()
-
-    # 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
+        ENTRY_METHODS = dict(
+        manual='Manual entry of single transcript',
+        fasta="Fasta file upload (supports multiple transcripts if they have unique ID's)"
         )
+        @st.cache_data
+        def convert_df(df):
+            # IMPORTANT: Cache the conversion to prevent computation on every rerun
+            return df.to_csv().encode('utf-8')
 
-    # 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'
+        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
+
+
+        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
+
+            # title and documentation
+            st.markdown(Path('tiger.md').read_text(), unsafe_allow_html=True)
+            st.divider()
+
+            # 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
             )
-    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'
+            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:
-                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()
+                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()
+else:
+    raise ValueError(f"Unknown model: {model_name}")