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import os |
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import time |
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import json |
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import random |
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import string |
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import socket |
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import requests |
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from flask import Flask, request, jsonify, Response, stream_with_context |
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from flask_cors import CORS |
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from threading import Thread |
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import logging |
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from .embedding_processing import embedding_processing |
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import g4f |
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from g4f import ChatCompletion, Provider, BaseProvider, models |
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from g4f.models import ModelUtils |
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app = Flask(__name__) |
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CORS(app) |
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LOG = logging.getLogger(__name__) |
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embedding_proc = embedding_processing() |
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@app.route("/chat/completions", methods=['POST']) |
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@app.route("/v1/chat/completions", methods=['POST']) |
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@app.route("/", methods=['POST']) |
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def chat_completions(): |
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request_data = request.get_json() |
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model = request_data.get('model', 'gpt-3.5-turbo').replace("neuro-", "") |
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messages = request_data.get('messages') |
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stream = request_data.get('stream', False) |
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streaming_ = request_data.get('stream', False) |
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temperature = request_data.get('temperature', 1.0) |
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top_p = request_data.get('top_p', 1.0) |
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max_tokens = request_data.get('max_tokens', 1024) |
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response = ChatCompletion.create(model=model, stream=stream, messages=messages, temperature=temperature, top_p=top_p, max_tokens=max_tokens, system_prompt="") |
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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 streaming_: |
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completion_timestamp = int(time.time()) |
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completion_id = ''.join(random.choices( |
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'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789', k=28)) |
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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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{ |
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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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} |
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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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}, |
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} |
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def stream(): |
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nonlocal response |
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for token in response: |
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completion_timestamp = int(time.time()) |
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completion_id = ''.join(random.choices( |
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'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789', k=28)) |
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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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'choices': [ |
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{ |
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'delta': { |
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'content': token |
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}, |
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'index': 0, |
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'finish_reason': None |
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} |
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] |
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} |
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yield 'data: %s\n\n' % json.dumps(completion_data, separators=(',' ':')) |
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time.sleep(0.02) |
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print('===Start Streaming===') |
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return app.response_class(stream(), mimetype='text/event-stream') |
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@app.route('/v1/embeddings', methods=['POST']) |
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@app.route('/embeddings', methods=['POST']) |
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def create_embedding(): |
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j_input = request.get_json() |
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embedding = embedding_proc.embedding(text_list=j_input['input']) |
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log_event() |
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return jsonify( |
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embedding |
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) |
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def log_event(): |
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LOG.info('served') |
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@app.route("/v1/dashboard/billing/subscription", methods=['GET']) |
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@app.route("/dashboard/billing/subscription", methods=['GET']) |
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def billing_subscription(): |
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return jsonify({ |
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"object": "billing_subscription", |
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"has_payment_method": True, |
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"canceled": False, |
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"canceled_at": None, |
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"delinquent": None, |
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"access_until": 2556028800, |
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"soft_limit": 6944500, |
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"hard_limit": 166666666, |
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"system_hard_limit": 166666666, |
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"soft_limit_usd": 416.67, |
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"hard_limit_usd": 9999.99996, |
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"system_hard_limit_usd": 9999.99996, |
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"plan": { |
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"title": "Pay-as-you-go", |
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"id": "payg" |
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}, |
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"primary": True, |
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"account_name": "OpenAI", |
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"po_number": None, |
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"billing_email": None, |
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"tax_ids": None, |
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"billing_address": { |
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"city": "New York", |
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"line1": "OpenAI", |
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"country": "US", |
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"postal_code": "NY10031" |
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}, |
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"business_address": None |
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} |
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) |
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@app.route("/v1/dashboard/billing/usage", methods=['GET']) |
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@app.route("/dashboard/billing/usage", methods=['GET']) |
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def billing_usage(): |
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return jsonify({ |
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"object": "list", |
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"daily_costs": [ |
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{ |
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"timestamp": time.time(), |
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"line_items": [ |
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{ |
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"name": "GPT-4", |
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"cost": 0.0 |
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}, |
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{ |
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"name": "Chat models", |
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"cost": 1.01 |
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}, |
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{ |
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"name": "InstructGPT", |
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"cost": 0.0 |
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}, |
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{ |
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"name": "Fine-tuning models", |
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"cost": 0.0 |
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}, |
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{ |
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"name": "Embedding models", |
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"cost": 0.0 |
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}, |
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{ |
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"name": "Image models", |
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"cost": 16.0 |
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}, |
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{ |
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"name": "Audio models", |
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"cost": 0.0 |
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} |
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] |
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} |
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], |
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"total_usage": 1.01 |
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} |
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) |
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@app.route("/v1/models", methods=['GET']) |
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@app.route("/models", methods=['GET']) |
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def models(): |
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import g4f.models |
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model = {"data":[]} |
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for i in g4f.models.ModelUtils.convert: |
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model['data'].append({ |
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"id": i, |
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"object": "model", |
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"owned_by": g4f.models.ModelUtils.convert[i].base_provider, |
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"tokens": 99999, |
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"fallbacks": None, |
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"endpoints": [ |
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"/v1/chat/completions" |
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], |
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"limits": None, |
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"permission": [] |
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}) |
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return jsonify(model) |
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@app.route("/v1/providers", methods=['GET']) |
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@app.route("/providers", methods=['GET']) |
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def providers(): |
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files = os.listdir("g4f/Provider/Providers") |
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files = [f for f in files if os.path.isfile(os.path.join("g4f/Provider/Providers", f))] |
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files.sort(key=str.lower) |
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providers_data = {"data":[]} |
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for file in files: |
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if file.endswith(".py"): |
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name = file[:-3] |
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try: |
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p = getattr(g4f.Provider,name) |
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providers_data["data"].append({ |
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"provider": str(name), |
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"model": list(p.model), |
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"url": str(p.url), |
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"working": bool(p.working), |
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"supports_stream": bool(p.supports_stream) |
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}) |
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except: |
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pass |
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return jsonify(providers_data) |
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