File size: 3,504 Bytes
80dcd43 09035e3 9e10878 09035e3 80dcd43 7798d42 80dcd43 7798d42 80dcd43 656f70b 7798d42 80dcd43 7798d42 656f70b 7798d42 80dcd43 c79a37e 80dcd43 c14636f 80dcd43 c79a37e |
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 |
#https://www.freecodecamp.org/news/how-to-setup-virtual-environments-in-python/
#https://www.youtube.com/watch?v=qbLc5a9jdXo&ab_channel=CalebCurry
#https://stackoverflow.com/questions/26368306/export-is-not-recognized-as-an-internal-or-external-command
#python3 -m venv .venv
#source .venv/bin/activate
#
#pip freeze > requirements.txt
#$env:FLASK_APP="application.py" #set FLASK_APP=application.py # export FLASK_APP=application.py
#set FLASK_ENV=development #export FLASK_ENV=production
#flask run #flask run --host=0.0.0.0
#pip install torchvision
from flask import Flask, request, jsonify
from flask_cors import CORS
import pandas
import threading
import uuid
import time
from human_text_detect import detect_human_text
app = Flask(__name__)
CORS(app)
task_results = {}
@app.route('/')
def index():
return 'Hello'
def process_analysis(task_id, text, model_name, topic):
print(f"Processing task: {task_id}")
# Validate data
print('Validate data')
answer = validate_data(text, model_name, topic)
if answer != '':
task_results[task_id] = {'status': 'error', 'error': answer}
return
topic = check_topic(topic)
hcRelativeToThreshold, df_sentences = detect_human_text(model_name, topic, text)
message = 'Edits found in the text' if hcRelativeToThreshold >= 0 else 'We couldn\'t find edits in the text'
sentences = [
{
"sentence": row["sentence"],
"lppt": row["response"],
"pvalue": row["pvalue"],
"color": "#f5aca4" if row["pvalue"] < 0.05 else ""
}
for _, row in df_sentences.iterrows()
]
# Store the result
task_results[task_id] = {'status': 'completed', 'message': message, 'hcRelativeToThreshold': hcRelativeToThreshold, 'sentences': sentences}
@app.route('/detectHumanInAIText/checkText', methods=['POST'])
def check_text():
# Get data
print('Get data')
data = request.get_json()
text = data.get('text')
model_name = data.get('model')
topic = data.get('topic')
# Generate a unique taskId
task_id = str(uuid.uuid4())
# Start processing in a separate thread
thread = threading.Thread(target=process_analysis, args=(task_id, text, model_name, topic))
thread.start()
# Return taskId immediately
return jsonify({'taskId': task_id}), 202
@app.route('/detectHumanInAIText/getAnalyzeResults', methods=['GET'])
def get_results():
task_id = request.args.get('taskId')
if not task_id:
return jsonify({'error': 'Missing taskId parameter'}), 400
if task_id not in task_results:
return jsonify({'status': 'pending'}), 202
return jsonify(task_results.pop(task_id)), 200
def validate_data(text, model_name, topic):
if text is None or text == '':
return 'Text is missing'
if model_name is None or model_name == '':
return 'Model name is missing'
if topic is None or topic == '':
return 'Topic is missing'
if model_name not in ['GPT2XL', 'PHI2']:
return f'Model {model_name} not supported'
if check_topic(topic) == None:
return f'Topic {topic} not supported'
return ''
def check_topic(topic):
topic_dict = {
'empirical': 'empirical',
'figures': 'characters',
'landmarks': 'locations',
'nature': 'nature',
'games': 'video_games_series_movies',
'wars': 'war'
}
return topic_dict[topic] if topic in topic_dict else None |