import json
import time
import random
import string
import requests
from typing import Any
from flask import Flask, request
from flask_cors import CORS
from transformers import AutoTokenizer
from g4f import ChatCompletion
app = Flask(__name__)
CORS(app)
@app.route('/chat/completions', methods=['POST'])
def chat_completions():
model = request.get_json().get('model', 'gpt-3.5-turbo')
stream = request.get_json().get('stream', False)
messages = request.get_json().get('messages')
response = ChatCompletion.create(model = model,
stream = stream, messages = messages)
completion_id = ''.join(random.choices(string.ascii_letters + string.digits, k=28))
completion_timestamp = int(time.time())
if not stream:
return {
'id': f'chatcmpl-{completion_id}',
'object': 'chat.completion',
'created': completion_timestamp,
'model': model,
'choices': [
{
'index': 0,
'message': {
'role': 'assistant',
'content': response,
},
'finish_reason': 'stop',
}
],
'usage': {
'prompt_tokens': None,
'completion_tokens': None,
'total_tokens': None,
},
}
def streaming():
for chunk in response:
completion_data = {
'id': f'chatcmpl-{completion_id}',
'object': 'chat.completion.chunk',
'created': completion_timestamp,
'model': model,
'choices': [
{
'index': 0,
'delta': {
'content': chunk,
},
'finish_reason': None,
}
],
}
content = json.dumps(completion_data, separators=(',', ':'))
yield f'data: {content}\n\n'
time.sleep(0.1)
end_completion_data: dict[str, Any] = {
'id': f'chatcmpl-{completion_id}',
'object': 'chat.completion.chunk',
'created': completion_timestamp,
'model': model,
'choices': [
{
'index': 0,
'delta': {},
'finish_reason': 'stop',
}
],
}
content = json.dumps(end_completion_data, separators=(',', ':'))
yield f'data: {content}\n\n'
return app.response_class(streaming(), mimetype='text/event-stream')
# Get the embedding from huggingface
def get_embedding(input_text, token):
huggingface_token = token
embedding_model = 'sentence-transformers/all-mpnet-base-v2'
max_token_length = 500
# Load the tokenizer for the 'all-mpnet-base-v2' model
tokenizer = AutoTokenizer.from_pretrained(embedding_model)
# Tokenize the text and split the tokens into chunks of 500 tokens each
tokens = tokenizer.tokenize(input_text)
token_chunks = [tokens[i:i + max_token_length]
for i in range(0, len(tokens), max_token_length)]
# Initialize an empty list
embeddings = []
# Create embeddings for each chunk
for chunk in token_chunks:
# Convert the chunk tokens back to text
chunk_text = tokenizer.convert_tokens_to_string(chunk)
# Use the Hugging Face API to get embeddings for the chunk
api_url = f'https://api-inference.huggingface.co/pipeline/feature-extraction/{embedding_model}'
headers = {'Authorization': f'Bearer {huggingface_token}'}
chunk_text = chunk_text.replace('\n', ' ')
# Make a POST request to get the chunk's embedding
response = requests.post(api_url, headers=headers, json={
'inputs': chunk_text, 'options': {'wait_for_model': True}})
# Parse the response and extract the embedding
chunk_embedding = response.json()
# Append the embedding to the list
embeddings.append(chunk_embedding)
# averaging all the embeddings
# this isn't very effective
# someone a better idea?
num_embeddings = len(embeddings)
average_embedding = [sum(x) / num_embeddings for x in zip(*embeddings)]
embedding = average_embedding
return embedding
@app.route('/embeddings', methods=['POST'])
def embeddings():
input_text_list = request.get_json().get('input')
input_text = ' '.join(map(str, input_text_list))
token = request.headers.get('Authorization').replace('Bearer ', '')
embedding = get_embedding(input_text, token)
return {
'data': [
{
'embedding': embedding,
'index': 0,
'object': 'embedding'
}
],
'model': 'text-embedding-ada-002',
'object': 'list',
'usage': {
'prompt_tokens': None,
'total_tokens': None
}
}
def main():
app.run(host='0.0.0.0', port=1337, debug=True)
if __name__ == '__main__':
main()
