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@ -1,104 +1,106 @@
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import json
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import time
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import random
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import string
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import time
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from typing import Any
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import requests
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from flask import Flask, request
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from flask_cors import CORS
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from typing import Any
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from flask import Flask, request
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from flask_cors import CORS
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from transformers import AutoTokenizer
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from g4f import ChatCompletion
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from g4f import ChatCompletion
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app = Flask(__name__)
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CORS(app)
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@app.route("/chat/completions", methods=["POST"])
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@app.route('/chat/completions', methods=['POST'])
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def chat_completions():
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model = request.get_json().get("model", "gpt-3.5-turbo")
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stream = request.get_json().get("stream", False)
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messages = request.get_json().get("messages")
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model = request.get_json().get('model', 'gpt-3.5-turbo')
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stream = request.get_json().get('stream', False)
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messages = request.get_json().get('messages')
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response = ChatCompletion.create(model=model, stream=stream, messages=messages)
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response = ChatCompletion.create(model = model,
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stream = stream, messages = messages)
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completion_id = "".join(random.choices(string.ascii_letters + string.digits, k=28))
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completion_id = ''.join(random.choices(string.ascii_letters + string.digits, k=28))
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completion_timestamp = int(time.time())
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if not stream:
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return {
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"id": f"chatcmpl-{completion_id}",
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"object": "chat.completion",
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"created": completion_timestamp,
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"model": model,
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"choices": [
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'id': f'chatcmpl-{completion_id}',
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'object': 'chat.completion',
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'created': completion_timestamp,
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'model': model,
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'choices': [
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{
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"index": 0,
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"message": {
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"role": "assistant",
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"content": response,
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'index': 0,
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'message': {
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'role': 'assistant',
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'content': response,
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},
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"finish_reason": "stop",
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'finish_reason': 'stop',
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}
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],
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"usage": {
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"prompt_tokens": None,
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"completion_tokens": None,
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"total_tokens": None,
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'usage': {
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'prompt_tokens': None,
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'completion_tokens': None,
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'total_tokens': None,
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},
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}
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def streaming():
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for chunk in response:
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completion_data = {
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"id": f"chatcmpl-{completion_id}",
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"object": "chat.completion.chunk",
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"created": completion_timestamp,
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"model": model,
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"choices": [
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'id': f'chatcmpl-{completion_id}',
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'object': 'chat.completion.chunk',
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'created': completion_timestamp,
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'model': model,
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'choices': [
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{
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"index": 0,
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"delta": {
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"content": chunk,
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'index': 0,
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'delta': {
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'content': chunk,
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},
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"finish_reason": None,
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'finish_reason': None,
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}
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],
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}
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content = json.dumps(completion_data, separators=(",", ":"))
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yield f"data: {content}\n\n"
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content = json.dumps(completion_data, separators=(',', ':'))
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yield f'data: {content}\n\n'
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time.sleep(0.1)
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end_completion_data: dict[str, Any] = {
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"id": f"chatcmpl-{completion_id}",
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"object": "chat.completion.chunk",
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"created": completion_timestamp,
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"model": model,
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"choices": [
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'id': f'chatcmpl-{completion_id}',
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'object': 'chat.completion.chunk',
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'created': completion_timestamp,
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'model': model,
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'choices': [
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{
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"index": 0,
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"delta": {},
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"finish_reason": "stop",
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'index': 0,
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'delta': {},
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'finish_reason': 'stop',
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}
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],
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}
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content = json.dumps(end_completion_data, separators=(",", ":"))
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yield f"data: {content}\n\n"
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content = json.dumps(end_completion_data, separators=(',', ':'))
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yield f'data: {content}\n\n'
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return app.response_class(streaming(), mimetype="text/event-stream")
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return app.response_class(streaming(), mimetype='text/event-stream')
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#Get the embedding from huggingface
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# Get the embedding from huggingface
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def get_embedding(input_text, token):
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huggingface_token = token
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embedding_model = "sentence-transformers/all-mpnet-base-v2"
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embedding_model = 'sentence-transformers/all-mpnet-base-v2'
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max_token_length = 500
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# Load the tokenizer for the "all-mpnet-base-v2" model
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# Load the tokenizer for the 'all-mpnet-base-v2' model
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tokenizer = AutoTokenizer.from_pretrained(embedding_model)
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# Tokenize the text and split the tokens into chunks of 500 tokens each
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tokens = tokenizer.tokenize(input_text)
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token_chunks = [tokens[i:i + max_token_length] for i in range(0, len(tokens), max_token_length)]
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token_chunks = [tokens[i:i + max_token_length]
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for i in range(0, len(tokens), max_token_length)]
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# Initialize an empty list
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embeddings = []
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@ -109,52 +111,53 @@ def get_embedding(input_text, token):
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chunk_text = tokenizer.convert_tokens_to_string(chunk)
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# Use the Hugging Face API to get embeddings for the chunk
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api_url = f"https://api-inference.huggingface.co/pipeline/feature-extraction/{embedding_model}"
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headers = {"Authorization": f"Bearer {huggingface_token}"}
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chunk_text = chunk_text.replace("\n", " ")
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api_url = f'https://api-inference.huggingface.co/pipeline/feature-extraction/{embedding_model}'
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headers = {'Authorization': f'Bearer {huggingface_token}'}
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chunk_text = chunk_text.replace('\n', ' ')
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# Make a POST request to get the chunk's embedding
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response = requests.post(api_url, headers=headers, json={"inputs": chunk_text, "options": {"wait_for_model": True}})
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response = requests.post(api_url, headers=headers, json={
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'inputs': chunk_text, 'options': {'wait_for_model': True}})
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# Parse the response and extract the embedding
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chunk_embedding = response.json()
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# Append the embedding to the list
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embeddings.append(chunk_embedding)
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#averaging all the embeddings
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#this isn't very effective
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#someone a better idea?
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# averaging all the embeddings
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# this isn't very effective
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# someone a better idea?
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num_embeddings = len(embeddings)
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average_embedding = [sum(x) / num_embeddings for x in zip(*embeddings)]
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embedding = average_embedding
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return embedding
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@app.route("/embeddings", methods=["POST"])
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@app.route('/embeddings', methods=['POST'])
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def embeddings():
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input_text_list = request.get_json().get("input")
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input_text = ' '.join(map(str, input_text_list))
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token = request.headers.get('Authorization').replace("Bearer ", "")
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embedding = get_embedding(input_text, token)
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input_text_list = request.get_json().get('input')
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input_text = ' '.join(map(str, input_text_list))
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token = request.headers.get('Authorization').replace('Bearer ', '')
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embedding = get_embedding(input_text, token)
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return {
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"data": [
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{
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"embedding": embedding,
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"index": 0,
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"object": "embedding"
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}
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],
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"model": "text-embedding-ada-002",
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"object": "list",
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"usage": {
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"prompt_tokens": None,
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"total_tokens": None
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}
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}
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'data': [
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{
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'embedding': embedding,
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'index': 0,
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'object': 'embedding'
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}
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],
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'model': 'text-embedding-ada-002',
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'object': 'list',
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'usage': {
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'prompt_tokens': None,
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'total_tokens': None
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}
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}
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def main():
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app.run(host="0.0.0.0", port=1337, debug=True)
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app.run(host='0.0.0.0', port=1337, debug=True)
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if __name__ == "__main__":
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if __name__ == '__main__':
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main()
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