--- a
+++ b/moline_sheet_src/main.py
@@ -0,0 +1,91 @@
+import sys
+import os
+sys.path.append(os.path.abspath("./"))
+
+# import argparse
+# from enum import Enum
+import io
+# from PIL import Image, ImageDraw
+import os
+# import tempfile
+# from pdf2image import convert_from_path, convert_from_bytes
+from request_handling_aws import get_text, get_all_text
+from patient import Patient
+import re
+import pandas as pd
+
+block_markers = ['<START>', 'ENCOUNTER', 'PATIENT', 'GUARANTOR', 'COVERAGE']
+breaking_phrase = 'QUAD CITIES'
+
+def get_patients(text_block, block_markers,breaking_phrase):
+
+    patient_list = []
+
+    text_block = ['<START>'] + text_block
+    blocks = {x:[] for x in block_markers}
+    curr_marker = ''
+    curr_patient = Patient(block_markers=block_markers)
+
+    for line in text_block:
+
+        if breaking_phrase in line.strip():
+            for key,val in blocks.items():
+                print("\n\n" + key)
+                print(val)
+            curr_patient.process_gen_info(blocks)
+            patient_list.append(curr_patient)
+            curr_marker = '<START>'
+            blocks = {x:[] for x in block_markers}
+            curr_patient = Patient(block_markers=block_markers)
+
+
+        marker_change = 0
+        for block_marker in block_markers:
+            if block_marker in line.strip():
+                    curr_marker = block_marker
+                    marker_change = 1
+                    break
+
+        if marker_change == 1:
+            continue
+
+        blocks[curr_marker].append(line)
+
+    curr_patient.process_gen_info(blocks)
+    patient_list.append(curr_patient)
+    return patient_list
+
+def compile_dataframe(patient_list):
+    pat_df = pd.DataFrame()
+    for patient in patient_list:
+        to_concat_df = patient.csv_rep()
+        pat_df = pd.concat([pat_df,pd.DataFrame([to_concat_df], columns=patient.csv_rep().keys())],axis=0,join='outer').reset_index(drop=True)
+    pat_df = pat_df.dropna(axis=1, how='all')
+
+    dob_cols = [col for col in pat_df.columns if 'dob' in col]
+    print("DOB COLS:", dob_cols)
+    for col in dob_cols:
+        pat_df[col] = pat_df[col].apply(lambda x:  x.split(" ")[0] if (x != None) else x)
+    return pat_df.iloc[1:]
+
+def run_pipeline(full_body, form_data=None):
+    print("RUNNING MOLINE PIPELINE........")
+    block_markers = ['<START>', 'ENCOUNTER', 'PATIENT', 'GUARANTOR', 'COVERAGE']
+    breaking_phrase = 'QUAD CITIES'
+    patient_list = get_patients(full_body,block_markers,breaking_phrase)
+    fin_df = compile_dataframe(patient_list)
+    return fin_df
+
+if __name__ == "__main__":
+    full_body, ids = get_all_text("facesheet-ap","facesheet_moline/")
+    print(full_body)
+    record = []
+    pat_fl = 0
+    nam_nl = 0
+    block_markers = ['<START>', 'ENCOUNTER', 'PATIENT', 'GUARANTOR', 'COVERAGE']
+    breaking_phrase = 'QUAD CITIES'
+    patient_list = get_patients(full_body,block_markers,breaking_phrase)
+    fin_df = compile_dataframe(patient_list)
+    fin_df.to_excel("./output_fin.xlsx")
+    with pd.option_context('display.max_rows', None, 'display.max_columns', None):  # more options can be specified also
+        print(fin_df)