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import requests
import json
import os
from .moderation import check_moderation_text
from openai import OpenAI
from dotenv import load_dotenv
load_dotenv()
client = OpenAI()
def auto_suggest_normalize(text):
try:
# make post request
texts = text.split('\n')
for text in texts:
text = text.strip()
if text == "": continue
yield text
except Exception as e:
print(e)
return texts
headers = {"Authorization": f'Bearer {os.environ["HF_ACCESS_TOKEN"]}'}
def query(payload):
response = requests.post(os.environ["LLAMA2_INFERENCE_API_URL"], headers=headers, json=payload)
return response.json()
def query_openai(prompt):
if check_moderation_text(prompt): return None
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": prompt}]
)
return response.choices[0].message.content.strip()
def remove_extras(text):
return text.replace('\\', '').replace('"', '').replace('1.', '').replace('2.', '').replace('3.', '').strip()
def auto_suggest_ask_llama2(prompt):
try:
answer = query({
"inputs": prompt,
})
print(answer)
return answer
except Exception as e:
print(e)
return False
def auto_suggest_ask_gpt(prompt):
try:
output = query_openai(prompt)
print(output, 'output')
return output
except Exception as e:
print(e)
return False
def auto_suggest_normalize_llama2(text):
text_list = []
try:
# make post request
texts = text.split('\n')
for text in texts:
if ("1." in text.strip()[:2]) or ("2." in text.strip()[:2]) or ("3." in text.strip()[:2]) or ("4." in text.strip()[:2]) or ("5." in text.strip()[:2]):
text = remove_extras(text)
text_list.append(text)
except Exception as e:
print(e)
return text_list if len(text_list) > 0 else []
def auto_suggest_ask(prompt):
try:
# make post request
response = requests.post('http://localhost:11434/api/generate', json={
"model": "mistral:v0.2",
"prompt": prompt
})
responses = response.text.split('\n')
text = ""
for response in responses:
dict = json.loads(response)
if dict["done"] == True: break
text += dict["response"]
return text
except Exception as e:
print(e)
return False
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