Spaces:
Sleeping
Sleeping
File size: 3,486 Bytes
44a025a 11aa943 44a025a 11aa943 44a025a 11aa943 44a025a 11aa943 44a025a 11aa943 44a025a |
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 |
import pandas as pd
import json
import re
import numpy as np
import os
from typing import List, Dict, Tuple, Any
from app.services.model_service import get_model, reload_embeddings
# Define data directory path
DATA_DIR = "/app/data"
# Ensure data directory exists
os.makedirs(DATA_DIR, exist_ok=True)
def remove_prefix(text: str, prefix_pattern: str) -> str:
"""
Removes the prefix matching the given pattern from the text.
"""
return re.sub(prefix_pattern, "", text).strip()
def process_file(file_path: str, file_type: str) -> List[Dict[str, str]]:
"""
Process Excel or CSV file and extract question-answer pairs.
"""
if file_type == "excel":
df = pd.read_excel(file_path)
elif file_type == "csv":
df = pd.read_csv(file_path)
else:
raise ValueError("Unsupported file type. Use 'excel' or 'csv'.")
# Check if the necessary columns exist
if "θ³ͺε" not in df.columns or "εη" not in df.columns:
raise ValueError("The file must contain 'θ³ͺε' and 'εη' columns.")
# Initialize the list to store processed data
qa_list = []
df.dropna(inplace=True)
# Iterate over each row in the DataFrame
for index, row in df.iterrows():
raw_question = str(row["θ³ͺε"])
raw_answer = str(row["εη"])
# Remove prefixes using regex patterns
question = remove_prefix(raw_question, r"^Q\d+\.\s*")
answer = remove_prefix(raw_answer, r"^A\.\s*")
qa_list.append({"question": question, "answer": answer})
# print(qa_list)
return qa_list
def save_raw_data(qa_list: List[Dict[str, str]]) -> None:
"""
Save the raw question-answer pairs to a JSON file.
"""
raw_path = os.path.join(DATA_DIR, "raw.json")
with open(raw_path, "w", encoding="utf-8") as json_file:
json.dump(qa_list, json_file, ensure_ascii=False, indent=2)
def create_and_save_embeddings(qa_list: List[Dict[str, str]]) -> None:
"""
Create embeddings for questions and answers and save them.
"""
questions = [item["question"] for item in qa_list]
answers = [item["answer"] for item in qa_list]
# Use the global model
model = get_model()
# Create embeddings for questions and answers
question_embeddings = model.encode(questions, convert_to_numpy=True)
answer_embeddings = model.encode(answers, convert_to_numpy=True)
# Save embeddings as numpy arrays
q_emb_path = os.path.join(DATA_DIR, "question_embeddings.npy")
a_emb_path = os.path.join(DATA_DIR, "answer_embeddings.npy")
qa_data_path = os.path.join(DATA_DIR, "qa_data.json")
np.save(q_emb_path, question_embeddings)
np.save(a_emb_path, answer_embeddings)
# Save the original data
with open(qa_data_path, "w", encoding="utf-8") as f:
json.dump(qa_list, f, ensure_ascii=False, indent=2)
def process_and_create_embeddings(file_path: str, file_type: str) -> Dict[str, Any]:
"""
Process the input file and create embeddings.
"""
try:
qa_list = process_file(file_path, file_type)
save_raw_data(qa_list)
create_and_save_embeddings(qa_list)
# Reload embeddings into memory
reload_embeddings()
return {
"status": "success",
"message": "Embeddings created successfully",
"data": {"total_qa_pairs": len(qa_list)},
}
except Exception as e:
return {"status": "error", "message": str(e)}
|