Spaces:
Sleeping
Sleeping
File size: 8,077 Bytes
081077e f0a94b0 081077e f0a94b0 081077e f0a94b0 081077e 191079a 081077e 191079a 081077e f0a94b0 081077e 191079a 081077e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 |
import gradio as gr
import openpyxl
import csv
from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np
import tempfile
import os
import pandas as pd
import re
# Load the sentence transformer model
model = SentenceTransformer('BAAI/bge-small-en-v1.5')
def filter_excel1(excel_path, min_row, max_row):
try:
excel = openpyxl.load_workbook(excel_path)
sheet_0 = excel.worksheets[0]
data = [["category", "diagnostic_statement"]]
prev_category = ""
for row in sheet_0.iter_rows(min_row=min_row, max_row=max_row):
category = row[1].value
diagnostic_statement = row[5].value
if prev_category == "":
prev_category = category
if not category:
category = prev_category
else:
prev_category = category
data.append([category, diagnostic_statement])
return data
except Exception as e:
raise gr.Error(f"Error processing Excel 1: {str(e)}")
def filter_excel2(excel_path, min_row, max_row, sheetname):
try:
excel = openpyxl.load_workbook(excel_path)
sheet_0 = excel[sheetname]
data = [["description", "category"]]
for row in sheet_0.iter_rows(min_row=min_row, max_row=max_row):
description = row[0].value
category = row[6].value
# filtering out the categories
if isinstance(category, str) and category!="#N/A":
pass
elif isinstance(category, int):
category="#N/A"
else:
category="#N/A"
if description:
data.append([description, category])
return data
except Exception as e:
raise gr.Error(f"Error processing Excel 2: {str(e)}")
def sheet_lookup(current_sheet_name, excel_file_path):
# Read the Excel file
xl = pd.ExcelFile(excel_file_path)
# Determine the previous quarter sheet name
match = re.match(r'(\d)Q(\d{4})', current_sheet_name)
if match:
quarter, year = map(int, match.groups())
prev_quarter = 4 if quarter == 1 else quarter - 1
prev_year = year - 1 if quarter == 1 else year
prev_sheet_name = f"{prev_quarter}Q{prev_year}"
else:
raise ValueError("Invalid sheet name format")
# Read the current sheet
current_df = xl.parse(current_sheet_name)
# Check if previous sheet exists
if prev_sheet_name in xl.sheet_names:
# Read the previous quarter sheet
prev_df = xl.parse(prev_sheet_name)
# Perform the lookup
lookup_col = 'Monitoring Tool Instance ID-AU'
current_df.drop_duplicates(subset=[lookup_col], keep='first', inplace=True)
prev_df.drop_duplicates(subset=[lookup_col], keep='first', inplace=True)
value_col = f"{prev_quarter}q CRI Profile Mapping"
result_col = f"{quarter}q CRI Profile Mapping"
# Create a dictionary for faster lookup
lookup_dict = dict(zip(prev_df[lookup_col], prev_df[value_col]))
# Perform the lookup and fill the result column
current_df[result_col] = current_df[lookup_col].map(lookup_dict).fillna('#N/A')
else:
# If previous sheet doesn't exist, fill the result column with '#N/A'
result_col = f"{quarter}q CRI Profile Mapping"
current_df[result_col] = '#N/A'
print(f"Warning: Previous sheet {prev_sheet_name} not found. Filling {result_col} with '#N/A'")
# Save the results back to the Excel file
with pd.ExcelWriter(excel_file_path, mode='a', if_sheet_exists='replace') as writer:
current_df.to_excel(writer, sheet_name=current_sheet_name, index=False)
print(f"Processing complete for sheet {current_sheet_name}")
def get_embeddings(texts):
return model.encode(texts)
def get_top_n_categories(query_embedding, statement_embeddings, categories, n=3):
similarities = cosine_similarity([query_embedding], statement_embeddings)[0]
top_indices = np.argsort(similarities)[-n:][::-1]
return [categories[i] for i in top_indices]
def process_data(csv1_data, csv2_data):
try:
diagnostic_statements = [row[1] for row in csv1_data[1:]]
statement_embeddings = get_embeddings(diagnostic_statements)
categories = [row[0] for row in csv1_data[1:]]
processed_descriptions = []
processed_categories = []
for row in csv2_data[1:]:
description = row[0]
if description in processed_descriptions:
row[1] = processed_categories[processed_descriptions.index(description)]
continue
if row[1] != "#N/A":
processed_categories.append(row[1])
processed_descriptions.append(description)
continue
description_embedding = get_embeddings([description])[0]
top_categories = get_top_n_categories(description_embedding, statement_embeddings, categories)
row[1] = ', '.join(top_categories)
processed_descriptions.append(description)
processed_categories.append(', '.join(top_categories))
return csv2_data
except Exception as e:
raise gr.Error(f"Error processing data: {str(e)}")
def update_excel(excel_path, processed_data, sheetname):
try:
excel = openpyxl.load_workbook(excel_path)
sheet_0 = excel[sheetname]
idx = 0
for row in sheet_0.iter_rows(min_row=2):
description = row[0]
category = row[6]
if not description.value:
continue
try:
sheet_0.cell(row=category.row, column=category.col_idx, value=processed_data[idx][1])
idx += 1
except IndexError:
print(f"Warning: Not enough processed data for row {category.row}")
return excel
except Exception as e:
raise gr.Error(f"Error updating Excel: {str(e)}")
def process_files(excel1, excel2, min_row1, max_row1, min_row2, max_row2, sheetname):
try:
gr.Info("Starting processing...")
gr.Info("Doing lookup...")
sheet_lookup(sheetname, excel2)
# Process Excel 1
gr.Info("Processing Excel 1...")
csv1_data = filter_excel1(excel1, min_row1, max_row1)
# Process Excel 2
gr.Info("Processing Excel 2...")
csv2_data = filter_excel2(excel2, min_row2, max_row2, sheetname)
# Process data
gr.Info("Running similarity search...")
processed_data = process_data(csv1_data, csv2_data)
# Update Excel 2
gr.Info("Updating Excel file...")
updated_excel = update_excel(excel2, processed_data[1:], sheetname)
# Save the updated Excel file
gr.Info("Saving updated Excel file...")
with tempfile.NamedTemporaryFile(delete=False, suffix='.xlsx') as tmp:
updated_excel.save(tmp.name)
gr.Info("Processing complete!")
return tmp.name
except gr.Error as e:
# Re-raise Gradio errors to display them in the interface
raise e
except Exception as e:
# Catch any other unexpected errors
raise gr.Error(f"An unexpected error occurred: {str(e)}")
# Gradio interface
iface = gr.Interface(
fn=process_files,
inputs=[
gr.File(label="Upload Source Excel (Excel 1)"),
gr.File(label="Upload Excel to be Filled (Excel 2)"),
gr.Number(label="Min Row for Excel 1", value=2),
gr.Number(label="Max Row for Excel 1", value=1000),
gr.Number(label="Min Row for Excel 2", value=2),
gr.Number(label="Max Row for Excel 2", value=3009),
gr.Textbox(label="Sheet Name for Excel 2")
],
outputs=gr.File(label="Download Updated Excel"),
title="Excel Processor",
description="Upload two Excel files, specify row ranges, and download the processed Excel file."
)
iface.launch() |