luulinh90s's picture
add materials
fa53be0
raw
history blame
8.63 kB
from flask import Flask, render_template, request, redirect, url_for, send_from_directory, session
import json
import random
import os
import string
from flask_session import Session
app = Flask(__name__)
app.config['SECRET_KEY'] = 'supersecretkey' # Change this to a random secret key
app.config['SESSION_TYPE'] = 'filesystem'
Session(app)
# Directories for visualizations
VISUALIZATION_DIRS_PLAN_OF_SQLS = {
"TP": "visualizations/TP",
"TN": "visualizations/TN",
"FP": "visualizations/FP",
"FN": "visualizations/FN"
}
VISUALIZATION_DIRS_CHAIN_OF_TABLE = {
"TP": "htmls_COT/TP",
"TN": "htmls_COT/TN",
"FP": "htmls_COT/FP",
"FN": "htmls_COT/FN"
}
# Load all sample files from the directories based on the selected method
def load_samples(method):
if method == "Chain-of-Table":
visualization_dirs = VISUALIZATION_DIRS_CHAIN_OF_TABLE
else:
visualization_dirs = VISUALIZATION_DIRS_PLAN_OF_SQLS
samples = {"TP": [], "TN": [], "FP": [], "FN": []}
for category, dir_path in visualization_dirs.items():
for filename in os.listdir(dir_path):
if filename.endswith(".html"):
samples[category].append(filename)
return samples
# Randomly select balanced samples
def select_balanced_samples(samples):
tp_fp_samples = random.sample(samples["TP"] + samples["FP"], 5)
tn_fn_samples = random.sample(samples["TN"] + samples["FN"], 5)
return tp_fp_samples + tn_fn_samples
def generate_random_string(length=8):
return ''.join(random.choices(string.ascii_letters + string.digits, k=length))
@app.route('/', methods=['GET', 'POST'])
def index():
if request.method == 'POST':
username = request.form.get('username')
seed = request.form.get('seed')
method = request.form.get('method')
if not username or not seed or not method:
return "Missing username, seed, or method", 400
seed = int(seed)
random.seed(seed)
all_samples = load_samples(method)
selected_samples = select_balanced_samples(all_samples)
random_string = generate_random_string()
filename = f'{username}_{seed}_{method}_{random_string}.json' # Append method to filename
session['selected_samples'] = selected_samples
session['responses'] = [] # Initialize responses list
session['method'] = method # Store the selected method
return redirect(url_for('experiment', username=username, sample_index=0, seed=seed, filename=filename))
return render_template('index.html')
@app.route('/experiment/<username>/<sample_index>/<seed>/<filename>', methods=['GET'])
def experiment(username, sample_index, seed, filename):
sample_index = int(sample_index)
selected_samples = session.get('selected_samples', [])
method = session.get('method') # Retrieve the selected method
if sample_index >= len(selected_samples):
return redirect(url_for('completed', filename=filename))
visualization_file = selected_samples[sample_index]
visualization_path = None
# Determine the correct visualization directory based on the method
if method == "Chain-of-Table":
visualization_dirs = VISUALIZATION_DIRS_CHAIN_OF_TABLE
else:
visualization_dirs = VISUALIZATION_DIRS_PLAN_OF_SQLS
# Find the correct visualization path
for category, dir_path in visualization_dirs.items():
if visualization_file in os.listdir(dir_path):
visualization_path = f"{category}/{visualization_file}"
break
if not visualization_path:
return "Visualization file not found", 404
statement = "Please make a decision to Accept/Reject the AI prediction based on the explanation."
return render_template('experiment.html',
sample_id=sample_index,
statement=statement,
visualization=visualization_path,
username=username,
seed=seed,
sample_index=sample_index,
filename=filename)
@app.route('/visualizations/<path:path>')
def send_visualization(path):
# Determine which visualization folder to use based on the selected method
method = session.get('method')
if method == "Chain-of-Table":
visualization_dir = 'htmls_COT'
else: # Default to Plan-of-SQLs
visualization_dir = 'visualizations'
# Serve the file from the appropriate directory
return send_from_directory(visualization_dir, path)
@app.route('/feedback', methods=['POST'])
def feedback():
sample_id = request.form['sample_id']
feedback = request.form['feedback']
username = request.form['username']
seed = request.form['seed']
sample_index = int(request.form['sample_index'])
filename = request.form['filename']
selected_samples = session.get('selected_samples', [])
responses = session.get('responses', [])
# Store the feedback
responses.append({
'sample_id': sample_id,
'feedback': feedback
})
session['responses'] = responses
# Create the result directory if it doesn't exist
result_dir = 'human_study'
os.makedirs(result_dir, exist_ok=True)
# Load existing data if the JSON file exists
filepath = os.path.join(result_dir, filename)
if os.path.exists(filepath):
with open(filepath, 'r') as f:
data = json.load(f)
else:
data = {}
# Update data with the current feedback
data[sample_index] = {
'Username': username,
'Seed': seed,
'Sample ID': sample_id,
'Task': f"Please make a decision to Accept/Reject the AI prediction based on the explanation.",
'User Feedback': feedback
}
# Save updated data to the file
with open(filepath, 'w') as f:
json.dump(data, f, indent=4)
next_sample_index = sample_index + 1
if next_sample_index >= len(selected_samples):
return redirect(url_for('completed', filename=filename))
return redirect(
url_for('experiment', username=username, sample_index=next_sample_index, seed=seed, filename=filename))
@app.route('/completed/<filename>')
def completed(filename):
# Load responses from the session
responses = session.get('responses', [])
# Determine which JSON file to load based on the method
method = session.get('method')
if method == "Chain-of-Table":
json_file = 'Tabular_LLMs_human_study_vis_6_COT.json'
else: # Default to Plan-of-SQLs
json_file = 'Tabular_LLMs_human_study_vis_6.json'
# Load the ground truth data from the appropriate JSON file
with open(json_file, 'r') as f:
ground_truth = json.load(f)
# Initialize counters
correct_responses = 0
accept_count = 0
reject_count = 0
for response in responses:
sample_id = response['sample_id']
feedback = response['feedback']
index = sample_id.split('-')[1].split('.')[0] # Extract index from filename
# Count the feedback
if feedback.upper() == "TRUE":
accept_count += 1
elif feedback.upper() == "FALSE":
reject_count += 1
# Construct the ground truth key
if method == "Chain-of-Table":
ground_truth_key = f"COT_test-{index}.html" # Adjust this based on your actual key format in the CoTable JSON
else:
ground_truth_key = f"POS_test-{index}.html"
# Check if the key exists in the ground truth data
if ground_truth_key in ground_truth and ground_truth[ground_truth_key]['answer'].upper() == feedback.upper():
correct_responses += 1
else:
print(f"Missing or mismatched key: {ground_truth_key}")
# Calculate accuracy
accuracy = (correct_responses / len(responses)) * 100 if responses else 0
accuracy = round(accuracy, 2)
# Calculate percentages
total_responses = len(responses)
accept_percentage = (accept_count / total_responses) * 100 if total_responses else 0
reject_percentage = (reject_count / total_responses) * 100 if total_responses else 0
# Round percentages
accept_percentage = round(accept_percentage, 2)
reject_percentage = round(reject_percentage, 2)
return render_template('completed.html',
accuracy=accuracy,
accept_percentage=accept_percentage,
reject_percentage=reject_percentage)
if __name__ == '__main__':
app.run(debug=True, port=8080)