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import json
from bs4 import BeautifulSoup
import re
from tqdm import tqdm # Import tqdm for progress tracking
import sys
import question_categorizer as qc
import numpy as np
from question_categorizer import TextClassificationModel
qc_model = qc.TextClassificationModel.load_model("models/categorizer")
categories = ['Geography', 'Religion', 'Philosophy', 'Trash','Mythology', 'Literature','Science', 'Social Science', 'History', 'Current Events', 'Fine Arts']
def remove_newline(string):
return re.sub('\n+', ' ', string)
def clean_text(text, answer):
# Remove HTML tags
text = re.sub(r'<.*?>', '', text)
#text = re.sub(r'?','.',text)
text = text.replace('?','.')
# Clean the text further
text = re.sub(r'[^a-zA-Z.\s-]', '', text)
# Remove answer from text
try:
# Preprocess the answer to replace underscores with spaces
processed_answer = answer.replace('_', ' ')
# Remove parentheses from the processed answer
processed_answer = re.sub(r'\([^)]*\)', '', processed_answer)
# Replace all instances of the processed answer with an empty string, ignoring case
text = re.sub(re.escape(processed_answer), '', text, flags=re.IGNORECASE)
except Exception as e:
print("An error occurred during text cleaning:", e)
print("Text:", text)
print("Answer:", answer)
# Remove extra whitespaces
text = re.sub(r'\s+', ' ', text)
return text.strip()
def process_data():
#with open("data/JEOPARDY_QUESTIONS1.json", "r") as f:
# jeopardy_data = json.load(f)
jeopardy_data = []
wiki_files = [
]
question_files = [
"qadata.json"]
wiki_data = []
question_data = []
for file_path in wiki_files:
with open('data/' + file_path, "r") as f:
wiki_data.extend(json.load(f))
for file_path in question_files:
with open('data/' + file_path, "r") as f:
question_data.extend(json.load(f))
#print(question_data)
with open("data/training_data.json", "w") as f:
training_data = []
# Process Jeopardy data
print("Processing Jeopardy data...")
for entry in tqdm(jeopardy_data):
question = entry["question"]
answer = str(entry["answer"])
# Preprocess the text
soup = BeautifulSoup(question, 'html.parser')
clean_question = ''.join(soup.findAll(text=True, recursive=False))
question_category = []
# Get category from qc_model
prediction = qc_model.predict(question)
predictions = np.argwhere(prediction >= 1.5)[1]
for prediction_ind in predictions:
# Store data in array with respective index
question_category.append(categories[prediction_ind])
question_category.append('ALL')
training_entry = {
"text": clean_question,
"answer": answer,#,
# Mohit, put categorizing code here
"category": question_category
}
training_data.append(training_entry)
# Process Wikipedia data
print("Processing Wikipedia data...")
for entry in tqdm(wiki_data):
page = str(entry["page"])
text = entry["text"]
if(text == ""):
continue
text = remove_newline(text)
text = clean_text(text, page)
question_category = []
# Get category from qc_model
prediction = qc_model.predict(text)
predictions = np.argwhere(prediction >= 1.5)[1]
for prediction_ind in predictions:
# Store data in array with respective index
question_category.append(categories[prediction_ind])
question_category.append('ALL')
training_entry = {
"text": text,
"answer": page,
# Mohit, put categorizing code here
"category": question_category
}
training_data.append(training_entry)
print("Processing Misc data...")
for entry in tqdm(question_data):
answer = str(entry["answer"])
text = entry["text"]
if(text == "" or answer == ""):
continue
text = remove_newline(text)
text = clean_text(text, answer)
question_category = []
# Get category from qc_model
try:
prediction = qc_model.predict(text)
predictions = np.argwhere(prediction >= 1.5)[1]
except:
print("answer: " + str(answer))
print("text:" + str(text))
continue
for prediction_ind in predictions:
# Store data in array with respective index
question_category.append(categories[prediction_ind])
question_category.append('ALL')
training_entry = {
"text": text,
"answer": answer,
# Mohit, put categorizing code here
"category": question_category
}
training_data.append(training_entry)
json.dump(training_data, f, indent=4)
process_data()
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