import json
import random
import string
import time
# import requests
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("/")
def index():
return "interference api, url: http://127.0.0.1:1337"
@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 = {
"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 run_api():
app.run(host="0.0.0.0", port=1337)
