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import os
import sys
import time
import pickle
import openai
import configparser
from flask import Flask, render_template, request, redirect, url_for
dir_path = os.path.abspath(os.getcwd())
src_path = dir_path + "/src"
sys.path.append(src_path)
COMPLETIONS_MODEL = "gpt-3.5-turbo"
EMBEDDING_MODEL = "text-embedding-ada-002"
config_dir = dir_path + "/src/utils"
config = configparser.ConfigParser()
config.read(os.path.join(config_dir, 'gpt_local_config.cfg'))
# openai.api_key = config.get('token', 'GPT_TOKEN')
openai.api_key = os.environ.get("GPT_TOKEN")
import embedding_qa as emq
# Specify the path to your pickle file
pickle_file_path = 'caNano_embedding_pack_5_14.pickle'
# Load the pickle file
with open(pickle_file_path, 'rb') as file:
loaded_data = pickle.load(file)
document_df = loaded_data['df']
document_embedding = loaded_data['embedding']
COMPLETIONS_API_PARAMS = {
# We use temperature of 0.0 because it gives the
# most predictable, factual answer.
"temperature": 0.0,
"max_tokens": 4000,
"model": "gpt-3.5-turbo"
}
app = Flask("caNanoWiki_AI")
# Set the passcode for authentication
PASSCODE_auth = ""
# Define a variable to track if the user is authenticated
authenticated = False
last_activity_time = 0
# Timeout duration in seconds
timeout_duration = 5 * 60
# Session Length
session_duration = 30 * 60
@app.template_filter('nl2br')
def nl2br_filter(s):
return s.replace('\n', '<br>')
@app.route('/', methods=['GET', 'POST'])
def index():
user_input = ""
processed_input = None
if request.method == 'POST':
user_input = request.form['user_input']
processed_input, chosen_sec_idxes = emq.answer_query_with_context(
user_input,
document_df,
document_embedding
)
return render_template(
'index.html',
processed_input=processed_input,
source_sections=chosen_sec_idxes,
user_input=user_input)
return render_template('index.html')
if __name__ == '__main__':
app.run(host='0.0.0.0', port=7860)
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