Spaces:
Sleeping
Sleeping
File size: 4,725 Bytes
97208ad 8e73170 97208ad 8e73170 |
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 |
from flask import Flask, request, jsonify, send_file
from flask_cors import CORS
import pickle
from modules.google_cloud_ocr.google_cloud_ocr import google_cloud_ocr
from modules.deed_preprocessing.spellcheck import correct_spelling
from modules.deed_preprocessing.preprocessor import preprocess_text
from modules.openai.racist_chatgpt_analysis import racist_chatgpt_analysis
from modules.model_experimentation.bag_of_words_logistic_regression import predict
import pandas as pd
import xlsxwriter
import re
app = Flask(__name__)
# CORS(app, resources={r"/*": {"origins": "*"}})
CORS(app, supports_credentials=True, origins="*")
with open('modules/model_experimentation/vectorizer.pkl', 'rb') as vec_file:
vectorizer = pickle.load(vec_file)
with open('modules/model_experimentation/logistic_model.pkl', 'rb') as model_file:
logistic_model = pickle.load(model_file)
# Helper to look for the book and page numbers
def extract_book_and_page(text):
book_numbers = re.findall(r"book\s+(\d+)", text, re.IGNORECASE)
page_numbers = re.findall(r"page\s+(\d+)", text, re.IGNORECASE)
return book_numbers, page_numbers
@app.route('/api/upload', methods=['POST'])
def upload_file():
if 'file' not in request.files:
return jsonify({'error': 'No file part in the request'}), 400
file = request.files['file']
if file.filename == '':
return jsonify({'error': 'No selected file'}), 400
ocr_engine = request.form.get('ocr_engine', 'google')
analysis_method = request.form.get('analysis_method', 'chatgpt')
try:
if ocr_engine == 'google':
# Step 1: Get text using Google OCR
google_text = google_cloud_ocr(file)
# Step 2: Pass text through the spell checker
spellchecked_text = correct_spelling(google_text)
# Step 3: Pass text through the preprocessor
processed_text = preprocess_text(spellchecked_text)
# Extract book and page numbers right after spellchecking
book_numbers, page_numbers = extract_book_and_page(spellchecked_text)
# Step 4: Get the names and locations
extracted_info = {
"names": processed_text.get("names", []),
"locations": processed_text.get("locations", []),
"book_numbers": book_numbers,
"page_numbers": page_numbers
}
# Step 5: Choose analysis method
if analysis_method == 'chatgpt':
analysis_result = racist_chatgpt_analysis(processed_text['original_text'])
return jsonify({
'status': 'success',
'ocr_engine': 'google',
'analysis_method': 'chatgpt',
'original_text': google_text,
'spellchecked_text': spellchecked_text,
'processed_text': processed_text,
'extracted_info': extracted_info,
'result': analysis_result
}), 200
elif analysis_method == 'logistic_regression':
lr_result = predict(processed_text, vectorizer, logistic_model)['is_racist']
return jsonify({
'status': 'success',
'ocr_engine': 'google',
'analysis_method': 'logistic_regression',
'original_text': google_text,
'spellchecked_text': spellchecked_text,
'processed_text': processed_text,
'extracted_info': extracted_info,
'result': lr_result
}), 200
else:
return jsonify({'error': 'Unsupported analysis method selected'}), 400
elif ocr_engine == 'azure':
return jsonify({'status': 'success', 'ocr_engine': 'azure', 'text': "fill"}), 200
else:
return jsonify({'error': 'Unsupported OCR engine selected'}), 400
except Exception as e:
return jsonify({'error': str(e)}), 500
@app.route('/api/download_excel', methods=['POST'])
def download_excel():
try:
data = request.get_json()
if not data:
return jsonify({'error': 'No data provided'}), 400
df = pd.DataFrame(data)
excel_path = 'output.xlsx'
with pd.ExcelWriter(excel_path, engine='xlsxwriter') as writer:
df.to_excel(writer, index=False, sheet_name='Sheet1')
return send_file(excel_path, as_attachment=True, download_name='analysis_results.xlsx')
except Exception as e:
return jsonify({'error': str(e)}), 500
if __name__ == '__main__':
app.run(debug=True, host="0.0.0.0", port=7860)
|