Handle fasta file input seperately

app.py

@@ -1,6 +1,7 @@
 import pandas as pd
 import streamlit as st
-from tiger import tiger_exhibit, TARGET_LEN, NUCLEOTIDE_TOKENS
+import os
+from tiger import tiger_exhibit, load_transcripts, TARGET_LEN, NUCLEOTIDE_TOKENS
 
 
 @st.cache
@@ -8,25 +9,66 @@ def convert_df(df):
     # IMPORTANT: Cache the conversion to prevent computation on every rerun
     return df.to_csv().encode('utf-8')
 
-
 # title and instructions
 st.title('TIGER Cas13 Efficacy Prediction')
-st.session_state['userInput'] = ''
-st.session_state['userInput'] = st.text_input(
+st.session_state["fasta_seq"] = ""
+st.session_state["text_seq"] = ""
+status_bar, status_text = None, None
+
+# UserInput Form from text input
+text_form = st.form("text")
+text_input = text_form.text_input(
     label='Enter a target transcript:',
-    # value='ATGCAGGACGCGGAGAACGTGGCGGTGCCCGAGGCGGCCGAGGAGCGCGC',
+    #value='ATGCAGGACGCGGAGAACGTGGCGGTGCCCGAGGCGGCCGAGGAGCGCGC',
     placeholder='Upper or lower case')
+if text_input:
+    # input is too short
+    if len(text_input) < TARGET_LEN:
+        transcript_len = len(text_input)
+        text_form.write('Transcript length ({:d}) must be at least {:d} bases.'.format(transcript_len, TARGET_LEN))
+    else:
+        st.session_state["text_seq"] = text_input
+text_calc = text_form.form_submit_button(label="calculate")
 #status bar
-status_text = st.empty()
-status_bar = st.progress(0)
-# input is too short
-if len(st.session_state['userInput']) < TARGET_LEN:
-    transcript_len = len(st.session_state['userInput'])
-    st.write('Transcript length ({:d}) must be at least {:d} bases.'.format(transcript_len, TARGET_LEN))
+status_text_textform = text_form.empty()
+status_bar_textform = text_form.progress(0)
+
+
+# UserInput Form from file
+fasta_form = st.form("fasta")
+fasta = fasta_form.file_uploader(label="upload fasta file") 
+if fasta:
+    fname = fasta.name 
+    st.write(fname)
+    with open(fname,"w") as f:
+        f.write(fasta.getvalue().decode("utf-8"))
+    transcript_tbl = load_transcripts([fname])
+    fasta_form.text("fasta file contents")
+    fasta_form.write(transcript_tbl)
+    seq = transcript_tbl['seq'][0]
+    st.session_state["fasta_seq"] = seq
+fasta_calc = fasta_form.form_submit_button(label="calculate")
+status_text_fastaform = fasta_form.empty()
+status_bar_fastaform = fasta_form.progress(0)
+#st.write(text_calc)
+#st.write(fasta_calc)
+
+#Calculation
+if text_calc:
+    src_seq = st.session_state["text_seq"]
+    status_text = status_text_textform
+    status_bar= status_bar_textform
+elif fasta_calc:
+    src_seq = st.session_state["fasta_seq"]
+    status_text = status_text_fastaform
+    status_bar= status_bar_fastaform
+else:
+    src_seq = ""
+#st.write(src_seq)
 
 # valid input
-elif all([True if nt.upper() in NUCLEOTIDE_TOKENS.keys() else False for nt in st.session_state['userInput']]):
-    on_target, off_target = tiger_exhibit(pd.DataFrame(dict(id=['ManualEntry'], seq=[st.session_state['userInput']])),
+if src_seq and all([True if nt.upper() in NUCLEOTIDE_TOKENS.keys() else False for nt in src_seq]):
+    on_target, off_target = tiger_exhibit(pd.DataFrame(dict(id=['ManualEntry'], seq=[src_seq])),
             status_bar, status_text)
     st.write('On-target predictions: ', on_target)
     st.download_button(label='Download', data=convert_df(on_target), file_name='on_target.csv', mime='text/csv')
@@ -37,5 +79,5 @@ elif all([True if nt.upper() in NUCLEOTIDE_TOKENS.keys() else False for nt in st
         st.write('We did not find any off-target effects!')
 
 # invalid input
-else:
-    st.write('Nucleotides other than ACGT detected!')
+#else:
+#    st.write('Nucleotides other than ACGT detected!')