Update app.py

app.py

@@ -1,174 +1,174 @@
-import argparse
-import os
-import pandas as pd
-import json
-from tree_search_icd import get_icd_codes
-from tqdm import tqdm
-import csv
-import streamlit as st
-import tempfile
-from pathlib import Path
-from io import StringIO
-
-# def process_medical_notes(file_path,model_name):
-# def process_medical_notes(input_dir, output_file, model_name):
-    
-#     code_map = {}
-    
-#     if not os.path.isdir(input_dir):
-#         raise ValueError("The specified input directory does not exist.")
-
-#     # Process each file in the input directory
-#     for files in tqdm(os.listdir(input_dir)):
-#         file_path = os.path.join(input_dir, files)
-#         print(file_path)
-#         with open(file_path, "r", encoding="utf-8") as file:
-#             medical_note = file.read()
-    
-#     if not os.path.isfile(file_path):
-#         print(f"File does not exist: {file_path}")
-#         return None
-    
-  
-#     # if os.path.isfile(file_path):
-#     #     st.write(f"File exists: {file_path}")
-    
-#     # try:
-        
-#     #     with open(file_path, "r",encoding="utf-8") as txtfile:
-#     #         st.write(file_path)
-#     #         medical_note = txtfile.read()
-            
-#     #         st.write(f"Content of the file: {medical_note[:1000]}")  # Print the first 1000 characters
-#     # except Exception as e:
-#     #     print(f"Error reading file: {e}")
-#     #     return None
-    
-#     # print(f"File read successfully. Content length: {len(medical_note)}")
-
-#     #print(medical_note)   
-#     icd_codes = get_icd_codes(medical_note, model_name)
-#     print(icd_codes)
-#     # return icd_codes
-# #     print(icd_codes)
-# #     code_map[files] = icd_codes
-
-#     with open(output_file, "w") as f:
-#         json.dump(code_map, f, indent=4)
-   
-
-# if __name__ == "__main__":
-#     parser = argparse.ArgumentParser(description="Process medical notes to extract ICD codes using a specified model.")
-#     parser.add_argument("--input_dir", help="Directory containing the medical text files")
-#     parser.add_argument("--output_file", help="File to save the extracted ICD codes in JSON format")
-#     parser.add_argument("--model_name", default="llama3-70b-8192", help="Model name to use for ICD code extraction")
-
-#     args = parser.parse_args()
-#     process_medical_notes(args.input_dir, args.output_file, args.model_name)
-
-def process_medical_notes(filepath, model_name):
-    
-    
-    try:
-        for txtfile in filepath:
-           with open(filepath, "r",encoding="utf-8") as txtfile:
-               medical_note = txtfile.read()
-            
-          
-    except Exception as e:
-        # print(f"Error reading file: {e}")
-        return None
-    
-   
-    icd_codes = get_icd_codes(medical_note, model_name)
-    return icd_codes
-
-
-
-def add_custom_css():
-    st.markdown(
-        """
-        <style>
-        /* Remove padding around the main block */
-        .block-container {
-            padding: 1rem;
-        }
-        /* Remove padding around the top */
-        header, footer, .reportview-container .main .block-container {
-            padding: 5;
-        }
-        /* Fullscreen layout adjustments */
-        .css-1d391kg { 
-            padding: 5; 
-        }
-
-        h1 {
-            text-align: center;
-        }
-         .table-wrapper {
-            text-align: center;
-        }
-
-
-
-
-        </style>
-        """,
-        unsafe_allow_html=True,
-    )
-def main():
-    st.set_page_config(layout="wide",page_icon='🔎',page_title='ICD Identifier') 
-    add_custom_css()
-    st.title("ICD Code Extractor From Medical Notes")
-  
-    col1, col2 = st.columns([1, 5])
-    with col2:
-        
-        file_uploads=st.file_uploader('Choose Medical Note File',type='txt', accept_multiple_files=True)
-     
-        submit = st.button("Submit")
-       
-    
-    with col1:
-         model_name = st.selectbox(
-            "Select Model",
-            ["llama3-70b-8192", "mixtral-8x7b-32768"],
-            index=0  # Default model selected
-        )
-
-    if submit :
-        
-        for file_input in file_uploads:
-            file_name = Path(file_input.name).name
-            with tempfile.NamedTemporaryFile(delete=False, suffix='.txt') as temp_file:
-        
-                temp_file.write(file_input.getbuffer())
-                temp_file.flush()
-                file_paths = temp_file.name
-                response=process_medical_notes(file_paths, model_name)
-                res_data=pd.DataFrame(response,columns=['ICD Code','Code Description','Evidence From Notes'])
-                with col2:
-                    
-
-                #     st.markdown(f"""
-                    
-                         
-                #     <div class="custom-table-container" >
-                #         <h4>Case Id: {file_name}</h4>
-                 
-                #         <div class="table-wrapper"  >
-                #             {res_data.to_html(classes='table-wrapper', index=False)}
-                #         </div>
-                #     </div>
-                  
-                   
-                # """, unsafe_allow_html=True)
-                    st.markdown(f"""
-                    <h5>Case Id: {file_name}</h5>
-                    """, unsafe_allow_html=True)
-                    st.markdown(res_data.style.hide(axis="index").to_html(), unsafe_allow_html=True)
-
-                # st.write(response)
-
-if __name__=="__main__":
+import argparse
+import os
+import pandas as pd
+import json
+from tree_search_icd import get_icd_codes
+from tqdm import tqdm
+import csv
+import streamlit as st
+import tempfile
+from pathlib import Path
+from io import StringIO
+
+# def process_medical_notes(file_path,model_name):
+# def process_medical_notes(input_dir, output_file, model_name):
+    
+#     code_map = {}
+    
+#     if not os.path.isdir(input_dir):
+#         raise ValueError("The specified input directory does not exist.")
+
+#     # Process each file in the input directory
+#     for files in tqdm(os.listdir(input_dir)):
+#         file_path = os.path.join(input_dir, files)
+#         print(file_path)
+#         with open(file_path, "r", encoding="utf-8") as file:
+#             medical_note = file.read()
+    
+#     if not os.path.isfile(file_path):
+#         print(f"File does not exist: {file_path}")
+#         return None
+    
+  
+#     # if os.path.isfile(file_path):
+#     #     st.write(f"File exists: {file_path}")
+    
+#     # try:
+        
+#     #     with open(file_path, "r",encoding="utf-8") as txtfile:
+#     #         st.write(file_path)
+#     #         medical_note = txtfile.read()
+            
+#     #         st.write(f"Content of the file: {medical_note[:1000]}")  # Print the first 1000 characters
+#     # except Exception as e:
+#     #     print(f"Error reading file: {e}")
+#     #     return None
+    
+#     # print(f"File read successfully. Content length: {len(medical_note)}")
+
+#     #print(medical_note)   
+#     icd_codes = get_icd_codes(medical_note, model_name)
+#     print(icd_codes)
+#     # return icd_codes
+# #     print(icd_codes)
+# #     code_map[files] = icd_codes
+
+#     with open(output_file, "w") as f:
+#         json.dump(code_map, f, indent=4)
+   
+
+# if __name__ == "__main__":
+#     parser = argparse.ArgumentParser(description="Process medical notes to extract ICD codes using a specified model.")
+#     parser.add_argument("--input_dir", help="Directory containing the medical text files")
+#     parser.add_argument("--output_file", help="File to save the extracted ICD codes in JSON format")
+#     parser.add_argument("--model_name", default="llama3-70b-8192", help="Model name to use for ICD code extraction")
+
+#     args = parser.parse_args()
+#     process_medical_notes(args.input_dir, args.output_file, args.model_name)
+
+def process_medical_notes(filepath, model_name):
+    
+    
+    try:
+        for txtfile in filepath:
+           with open(filepath, "r",encoding="utf-8") as txtfile:
+               medical_note = txtfile.read()
+            
+          
+    except Exception as e:
+        # print(f"Error reading file: {e}")
+        return None
+    
+   
+    icd_codes = get_icd_codes(medical_note, model_name)
+    return icd_codes
+
+
+
+def add_custom_css():
+    st.markdown(
+        """
+        <style>
+        /* Remove padding around the main block */
+        .block-container {
+            padding: 1rem;
+        }
+        /* Remove padding around the top */
+        header, footer, .reportview-container .main .block-container {
+            padding: 5;
+        }
+        /* Fullscreen layout adjustments */
+        .css-1d391kg { 
+            padding: 5; 
+        }
+
+        h1 {
+            text-align: center;
+        }
+         .table-wrapper {
+            text-align: center;
+        }
+
+
+
+
+        </style>
+        """,
+        unsafe_allow_html=True,
+    )
+def main():
+    st.set_page_config(layout="wide",page_icon='🔎',page_title='ICD Identifier') 
+    add_custom_css()
+    st.title("ICD Code Identifier From Medical Notes")
+  
+    col1, col2 = st.columns([1, 5])
+    with col2:
+        
+        file_uploads=st.file_uploader('Choose Medical Note File',type='txt', accept_multiple_files=True)
+     
+        submit = st.button("Submit")
+       
+    
+    with col1:
+         model_name = st.selectbox(
+            "Select Model",
+            ["llama3-70b-8192", "mixtral-8x7b-32768"],
+            index=0  # Default model selected
+        )
+
+    if submit :
+        
+        for file_input in file_uploads:
+            file_name = Path(file_input.name).name
+            with tempfile.NamedTemporaryFile(delete=False, suffix='.txt') as temp_file:
+        
+                temp_file.write(file_input.getbuffer())
+                temp_file.flush()
+                file_paths = temp_file.name
+                response=process_medical_notes(file_paths, model_name)
+                res_data=pd.DataFrame(response,columns=['ICD Code','Code Description','Evidence From Notes'])
+                with col2:
+                    
+
+                #     st.markdown(f"""
+                    
+                         
+                #     <div class="custom-table-container" >
+                #         <h4>Case Id: {file_name}</h4>
+                 
+                #         <div class="table-wrapper"  >
+                #             {res_data.to_html(classes='table-wrapper', index=False)}
+                #         </div>
+                #     </div>
+                  
+                   
+                # """, unsafe_allow_html=True)
+                    st.markdown(f"""
+                    <h5>Case Id: {file_name}</h5>
+                    """, unsafe_allow_html=True)
+                    st.markdown(res_data.style.hide(axis="index").to_html(), unsafe_allow_html=True)
+
+                # st.write(response)
+
+if __name__=="__main__":
     main()