Spaces:
Sleeping
Sleeping
File size: 6,875 Bytes
3702f2a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 |
# api_key_manager.py
from dotenv import load_dotenv; load_dotenv()
import os
import firebase_admin
from firebase_admin import credentials, firestore
import datetime
from pytz import UTC
# Initialize Firebase Admin SDK
cred = credentials.Certificate({
"type": "service_account",
"project_id": os.environ.get("FIREBASE_PROJECT_ID"),
"private_key_id": os.environ.get("FIREBASE_PRIVATE_KEY_ID"),
"private_key": os.environ.get("FIREBASE_PRIVATE_KEY").replace("\\n", "\n"),
"client_email": os.environ.get("FIREBASE_CLIENT_EMAIL"),
"client_id": os.environ.get("FIREBASE_CLIENT_ID"),
"auth_uri": "https://accounts.google.com/o/oauth2/auth",
"token_uri": "https://oauth2.googleapis.com/token",
"auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
"client_x509_cert_url": os.environ.get("FIREBASE_CLIENT_X509_CERT_URL")
})
try:
firebase_admin.get_app()
except ValueError:
firebase_admin.initialize_app(cred)
db = firestore.client()
def get_user_info(api_key):
doc = db.collection('Users').document(api_key).get(field_paths=[
'subscription_plan', 'requests_remaining', 'paid_models', 'ai_engine_secret_key'
])
if doc.exists:
return doc.to_dict()
else:
return None
def check_api_key_validity(api_key, check_rate_limit=True):
user_data = get_user_info(api_key)
if not user_data:
return False, "Invalid API key"
if check_rate_limit:
if user_data.get('requests_remaining', 0) <= 0:
return False, "Rate limit exceeded. Kindly Upgrade to Premium for using the Devs Do Code API"
return True, ""
def update_request_count(api_key, credits_used):
user_ref = db.collection('Users').document(api_key)
user_doc = user_ref.get(field_paths=['requests_remaining'])
if user_doc.exists:
requests_remaining = user_doc.to_dict().get('requests_remaining', 0)
new_requests_remaining = requests_remaining - credits_used # Subtract credits used
user_ref.update({'requests_remaining': new_requests_remaining})
return True
else:
return False
def get_rate_limit_status(api_key):
user_doc = db.collection('Users').document(api_key).get(field_paths=['requests_remaining'])
if not user_doc.exists:
return {"error": "Invalid API key"}
requests_remaining = user_doc.to_dict().get('requests_remaining', 0)
return {"requests_remaining": requests_remaining}
def get_subscription_status(api_key):
user_doc = db.collection('Users').document(api_key).get(field_paths=['subscription_plan'])
if not user_doc.exists:
return {"error": "Invalid API key"}
subscription_plan = user_doc.to_dict().get('subscription_plan', 'Free')
return {"subscription_plan": subscription_plan}
def get_available_models():
models = {
"gpt-4o": {
"name": "gpt-4o-2024-08-06",
"description": "Our high-intelligence flagship model for complex, multi-step tasks. GPT-4o is multimodal, processing text, audio, and images, and is faster and 50% cheaper than its predecessor. It has a maximum output of 16,384 tokens, if o1-mini is used",
"max_tokens": 4096,
"context_window": 128000,
"training_data": "Up to October 2023"
},
"o1-preview": {
"name": "o1-preview-2024-09-12",
"description": "OpenAI o1 is a new large language model optimized for complex reasoning tasks, utilizing reinforcement learning to enhance its problem-solving capabilities. It excels in STEM subjects and demonstrates impressive performance on challenging benchmarks, including achieving 83% accuracy on the AIME mathematics competition, significantly outperforming previous models like GPT-4o.",
"max_tokens": 32768,
"context_window": 128000,
"training_data": "Up to September 2024"
},
"claude-3.5-sonnet": {
"name": "claude-3.5-sonnet-latest",
"description": "Claude 3.5 Sonnet is a highly capable model developed by Anthropic, optimized for complex reasoning tasks and demonstrating significant advancements in performance compared to its predecessors. It excels in areas such as coding, multi-step workflows, and interpreting visual data.",
"context_window": 8192,
"max_tokens": 200000,
"training_data": "Up to April 2024"
},
"gemini-1.5-pro": {
"name": "Gemini-1.5-Pro",
"description": "Gemini is an AI chatbot developed by Google. Formerly known as Bard, it was released to the public in 2023. Gemini-1.5-Pro is an improved version of the model, offering more accurate and informative responses.",
"max_tokens": 4096,
"context_window": 131072,
"training_data": "Up to 2023"
},
"gemini-1-5-flash": {
"name": "Gemini 1.5 Flash",
"description": "A lighter-weight model than 1.5 Pro, designed to be fast and efficient to serve at scale. It's optimized for high-volume, high-frequency tasks and features a breakthrough long context window, making it highly capable of multimodal reasoning across vast amounts of information.",
"max_tokens": 4096,
"context_window": 131072,
"training_data": "Up to 2024"
},
"o1-mini": {
"name": "o1-mini",
"description": "The o1-mini model is a cost-effective and efficient AI model designed for quick reasoning tasks, particularly in STEM fields such as coding and mathematics. It is optimized for speed and lower resource consumption, making it an ideal choice for applications requiring fast responses without the need for extensive world knowledge.",
"max_tokens": 65536,
"context_window": 128000,
"training_data": "Up to October 2023"
},
"meta-llama-405b-turbo": {
"name": "Meta-Llama-3.1-405B-Instruct-Turbo",
"description": "Meta-Llama-3.1-405B-Instruct-Turbo is a state-of-the-art AI model designed for instruction-based tasks. It excels in generating coherent and contextually relevant responses, making it suitable for applications in conversational AI, content creation, and more. This model leverages advanced techniques in natural language processing to provide high-quality outputs.",
"max_tokens": 4096,
"context_window": 128000,
"training_data": "Up to October 2023"
}
}
return models
def get_model_info(model_name):
models = get_available_models()
return models.get(model_name)
if __name__ == "__main__":
print(get_model_info("gpt4o")) |