~ | new folder inluding ./tooland ./testing

etc/interference/app.py

@@ -0,0 +1,163 @@
+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()

etc/interference/requirements.txt

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+flask_cors
+watchdog~=3.0.0
+transformers
+tensorflow
+torch

etc/testing/log_time.py

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+from time import time
+
+
+async def log_time_async(method: callable, **kwargs):
+    start = time()
+    result = await method(**kwargs)
+    secs = f"{round(time() - start, 2)} secs"
+    if result:
+        return " ".join([result, secs])
+    return secs
+
+
+def log_time_yield(method: callable, **kwargs):
+    start = time()
+    result = yield from method(**kwargs)
+    yield f" {round(time() - start, 2)} secs"
+
+
+def log_time(method: callable, **kwargs):
+    start = time()
+    result = method(**kwargs)
+    secs = f"{round(time() - start, 2)} secs"
+    if result:
+        return " ".join([result, secs])
+    return secs

etc/testing/test_async.py

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+import sys
+from pathlib import Path
+import asyncio
+
+sys.path.append(str(Path(__file__).parent.parent))
+
+import g4f
+from testing.test_providers import get_providers
+from testing.log_time import log_time_async
+
+async def create_async(provider):
+    try:
+        response = await log_time_async(
+            provider.create_async,
+            model=g4f.models.default.name,
+            messages=[{"role": "user", "content": "Hello, are you GPT 3.5?"}]
+        )
+        print(f"{provider.__name__}:", response)
+    except Exception as e:
+        print(f"{provider.__name__}: {e.__class__.__name__}: {e}")
+
+async def run_async():
+  responses: list = [
+      create_async(provider)
+      for provider in get_providers()
+      if provider.working
+  ]
+  await asyncio.gather(*responses)
+
+print("Total:", asyncio.run(log_time_async(run_async)))

etc/testing/test_chat_completion.py

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+import sys
+from pathlib import Path
+
+sys.path.append(str(Path(__file__).parent.parent))
+
+import g4f, asyncio
+
+print("create:", end=" ", flush=True)
+for response in g4f.ChatCompletion.create(
+    model=g4f.models.gpt_4_32k_0613,
+    provider=g4f.Provider.Aivvm,
+    messages=[{"role": "user", "content": "send a bunch of emojis. i want to test something"}],
+    temperature=0.0,
+    stream=True
+):
+    print(response, end="", flush=True)
+print()
+
+async def run_async():
+    response = await g4f.ChatCompletion.create_async(
+        model=g4f.models.gpt_35_turbo_16k_0613,
+        provider=g4f.Provider.Aivvm,
+        messages=[{"role": "user", "content": "hello!"}],
+    )
+    print("create_async:", response)
+
+# asyncio.run(run_async())

etc/testing/test_interference.py

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+# type: ignore
+import openai
+
+openai.api_key = ""
+openai.api_base = "http://localhost:1337"
+
+
+def main():
+    chat_completion = openai.ChatCompletion.create(
+        model="gpt-3.5-turbo",
+        messages=[{"role": "user", "content": "write a poem about a tree"}],
+        stream=True,
+    )
+
+    if isinstance(chat_completion, dict):
+        # not stream
+        print(chat_completion.choices[0].message.content)
+    else:
+        # stream
+        for token in chat_completion:
+            content = token["choices"][0]["delta"].get("content")
+            if content != None:
+                print(content, end="", flush=True)
+
+
+if __name__ == "__main__":
+    main()

etc/testing/test_needs_auth.py

@@ -0,0 +1,96 @@
+import sys
+from pathlib import Path
+import asyncio
+
+sys.path.append(str(Path(__file__).parent.parent))
+
+import g4f
+from  testing.log_time import log_time, log_time_async, log_time_yield
+
+
+_providers = [
+    g4f.Provider.H2o,
+    g4f.Provider.You,
+    g4f.Provider.HuggingChat,
+    g4f.Provider.OpenAssistant,
+    g4f.Provider.Bing,
+    g4f.Provider.Bard
+]
+
+_instruct = "Hello, are you GPT 4?."
+
+_example = """
+OpenaiChat: Hello! How can I assist you today? 2.0 secs
+Bard: Hello! How can I help you today? 3.44 secs
+Bing: Hello, this is Bing. How can I help? 😊 4.14 secs
+Async Total: 4.25 secs
+
+OpenaiChat: Hello! How can I assist you today? 1.85 secs
+Bard: Hello! How can I help you today? 3.38 secs
+Bing: Hello, this is Bing. How can I help? 😊 6.14 secs
+Stream Total: 11.37 secs
+
+OpenaiChat: Hello! How can I help you today? 3.28 secs
+Bard: Hello there! How can I help you today? 3.58 secs
+Bing: Hello! How can I help you today? 3.28 secs
+No Stream Total: 10.14 secs
+"""
+
+print("Bing: ", end="")
+for response in log_time_yield(
+    g4f.ChatCompletion.create,
+    model=g4f.models.default,
+    messages=[{"role": "user", "content": _instruct}],
+    provider=g4f.Provider.Bing,
+    #cookies=g4f.get_cookies(".huggingface.co"),
+    stream=True,
+    auth=True
+):
+    print(response, end="", flush=True)
+print()
+print()
+
+
+async def run_async():
+    responses = [
+        log_time_async(
+            provider.create_async, 
+            model=None,
+            messages=[{"role": "user", "content": _instruct}],
+        )
+        for provider in _providers
+    ]
+    responses = await asyncio.gather(*responses)
+    for idx, provider in enumerate(_providers):
+        print(f"{provider.__name__}:", responses[idx])
+print("Async Total:", asyncio.run(log_time_async(run_async)))
+print()
+
+
+def run_stream():
+    for provider in _providers:
+        print(f"{provider.__name__}: ", end="")
+        for response in log_time_yield(
+            provider.create_completion,
+            model=None,
+            messages=[{"role": "user", "content": _instruct}],
+        ):
+            print(response, end="", flush=True)
+        print()
+print("Stream Total:", log_time(run_stream))
+print()
+
+
+def create_no_stream():
+    for provider in _providers:
+        print(f"{provider.__name__}:", end=" ")
+        for response in log_time_yield(
+            provider.create_completion,
+            model=None,
+            messages=[{"role": "user", "content": _instruct}],
+            stream=False
+        ):
+            print(response, end="")
+        print()
+print("No Stream Total:", log_time(create_no_stream))
+print()

etc/testing/test_providers.py

@@ -0,0 +1,66 @@
+import sys
+from pathlib import Path
+from colorama import Fore, Style
+
+sys.path.append(str(Path(__file__).parent.parent))
+
+from g4f import BaseProvider, models, Provider
+
+logging = False
+
+
+def main():
+    providers = get_providers()
+    failed_providers = []
+
+    for _provider in providers:
+        if _provider.needs_auth:
+            continue
+        print("Provider:", _provider.__name__)
+        result = test(_provider)
+        print("Result:", result)
+        if _provider.working and not result:
+            failed_providers.append(_provider)
+
+    print()
+
+    if failed_providers:
+        print(f"{Fore.RED + Style.BRIGHT}Failed providers:{Style.RESET_ALL}")
+        for _provider in failed_providers:
+            print(f"{Fore.RED}{_provider.__name__}")
+    else:
+        print(f"{Fore.GREEN + Style.BRIGHT}All providers are working")
+
+
+def get_providers() -> list[type[BaseProvider]]:
+    providers = dir(Provider)
+    providers = [getattr(Provider, provider) for provider in providers if provider != "RetryProvider"]
+    providers = [provider for provider in providers if isinstance(provider, type)]
+    return [provider for provider in providers if issubclass(provider, BaseProvider)]
+
+
+def create_response(_provider: type[BaseProvider]) -> str:
+    model = models.gpt_35_turbo.name if _provider.supports_gpt_35_turbo else models.default.name
+    response = _provider.create_completion(
+        model=model,
+        messages=[{"role": "user", "content": "Hello, who are you? Answer in detail much as possible."}],
+        stream=False,
+    )
+    return "".join(response)
+
+    
+def test(_provider: type[BaseProvider]) -> bool:
+    try:
+        response = create_response(_provider)
+        assert type(response) is str
+        assert len(response) > 0
+        return response
+    except Exception as e:
+        if logging:
+            print(e)
+        return False
+
+
+if __name__ == "__main__":
+    main()
+    

etc/tool/create_provider.py

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+
+import sys, re
+from pathlib import Path
+from os import path
+
+sys.path.append(str(Path(__file__).parent.parent))
+
+import g4f
+
+def read_code(text):
+    match = re.search(r"```(python|py|)\n(?P<code>[\S\s]+?)\n```", text)
+    if match:
+        return match.group("code")
+
+def read_result(result):
+    lines = []
+    for line in result.split("\n"):
+        if (line.startswith("```")):
+            break
+        if (line):
+            lines.append(line)
+    explanation = "\n".join(lines) if lines else ""
+    return explanation, read_code(result)
+
+def input_command():
+    print("Enter/Paste the cURL command. Ctrl-D or Ctrl-Z ( windows ) to save it.")
+    contents = []
+    while True:
+        try:
+            line = input()
+        except:
+            break
+        contents.append(line)
+    return "\n".join(contents)
+
+name = input("Name: ")
+provider_path = f"g4f/Provider/{name}.py"
+
+example = """
+from __future__ import annotations
+
+from aiohttp import ClientSession
+
+from ..typing       import AsyncGenerator
+from .base_provider import AsyncGeneratorProvider
+from .helper        import format_prompt
+
+
+class ChatgptDuo(AsyncGeneratorProvider):
+    url                   = "https://chat-gpt.com"
+    supports_gpt_35_turbo = True
+    working               = True
+
+    @classmethod
+    async def create_async_generator(
+        cls,
+        model: str,
+        messages: list[dict[str, str]],
+        **kwargs
+    ) -> AsyncGenerator:
+        headers = {
+            "authority": "chat-gpt.com",
+            "accept": "application/json",
+            "origin": cls.url,
+            "referer": f"{cls.url}/chat",
+        }
+        async with ClientSession(headers=headers) as session:
+            prompt = format_prompt(messages),
+            data = {
+                "prompt": prompt,
+                "purpose": "ask",
+            }
+            async with session.post(cls.url + "/api/chat", json=data) as response:
+                response.raise_for_status()
+                async for stream in response.content:
+                    if stream:
+                        yield stream.decode()
+"""
+
+if not path.isfile(provider_path):
+    command = input_command()
+
+    prompt = f"""
+Create a provider from a cURL command. The command is:
+```bash
+{command}
+```
+A example for a provider:
+```py
+{example}
+```
+The name for the provider class:
+{name}
+Replace "hello" with `format_prompt(messages)`.
+And replace "gpt-3.5-turbo" with `model`.
+"""
+
+    print("Create code...")
+    response = g4f.ChatCompletion.create(
+        model=g4f.models.gpt_35_long,
+        messages=[{"role": "user", "content": prompt}],
+        auth=True,
+        timeout=120,
+    )
+    print(response)
+    explanation, code = read_result(response)
+    if code:
+        with open(provider_path, "w") as file:
+            file.write(code)
+        with open(f"g4f/Provider/__init__.py", "a") as file:
+            file.write(f"\nfrom .{name} import {name}")
+else:
+    with open(provider_path, "r") as file:
+        code = file.read()

etc/tool/provider_init.py

@@ -0,0 +1,33 @@
+from pathlib import Path
+
+
+def main():
+    content = create_content()
+    with open("g4f/provider/__init__.py", "w", encoding="utf-8") as f:
+        f.write(content)
+
+
+def create_content():
+    path = Path()
+    paths = path.glob("g4f/provider/*.py")
+    paths = [p for p in paths if p.name not in ["__init__.py", "base_provider.py"]]
+    classnames = [p.stem for p in paths]
+
+    import_lines = [f"from .{name} import {name}" for name in classnames]
+    import_content = "\n".join(import_lines)
+
+    classnames.insert(0, "BaseProvider")
+    all_content = [f'    "{name}"' for name in classnames]
+    all_content = ",\n".join(all_content)
+    all_content = f"__all__ = [\n{all_content},\n]"
+
+    return f"""from .base_provider import BaseProvider
+{import_content}
+
+
+{all_content}
+"""
+
+
+if __name__ == "__main__":
+    main()

etc/tool/readme_table.py

@@ -0,0 +1,158 @@
+import re
+import sys
+from pathlib import Path
+from urllib.parse import urlparse
+
+sys.path.append(str(Path(__file__).parent.parent))
+
+import asyncio
+from g4f import models
+from g4f.Provider.base_provider import AsyncProvider, BaseProvider
+from g4f.Provider.retry_provider import RetryProvider
+from testing.test_providers import get_providers
+
+logging = False
+
+
+def print_imports():
+    print("##### Providers:")
+    print("```py")
+    print("from g4f.Provider import (")
+    for _provider in get_providers():
+        if _provider.working:
+            print(f"    {_provider.__name__},")
+                
+    print(")")
+    print("# Usage:")
+    print("response = g4f.ChatCompletion.create(..., provider=ProviderName)")
+    print("```")
+    print()
+    print()
+
+def print_async():
+    print("##### Async support:")
+    print("```py")
+    print("_providers = [")
+    for _provider in get_providers():
+        if _provider.working and issubclass(_provider, AsyncProvider):
+            print(f"      g4f.Provider.{_provider.__name__},")
+    print("]")
+    print("```")
+    print()
+    print()
+
+
+async def test_async(provider: type[BaseProvider]):
+    if not provider.working:
+        return False
+    model = models.gpt_35_turbo.name if provider.supports_gpt_35_turbo else models.default.name
+    messages = [{"role": "user", "content": "Hello Assistant!"}]
+    try:
+        if issubclass(provider, AsyncProvider):
+            response = await provider.create_async(model=model, messages=messages)
+        else:
+            response = provider.create_completion(model=model, messages=messages, stream=False)
+        return True if response else False
+    except Exception as e:
+        if logging:
+            print(f"{provider.__name__}: {e.__class__.__name__}: {e}")
+        return False
+
+
+async def test_async_list(providers: list[type[BaseProvider]]):
+    responses: list = [
+        test_async(_provider)
+        for _provider in providers
+    ]
+    return await asyncio.gather(*responses)
+
+
+def print_providers():
+    lines = [
+        "| Website| Provider| gpt-3.5 | gpt-4 | Streaming | Asynchron | Status | Auth |",
+        "| ------ | ------- | ------- | ----- | --------- | --------- | ------ | ---- |",
+    ]
+
+    providers = get_providers()
+    responses = asyncio.run(test_async_list(providers))
+
+    for is_working in (True, False):
+        for idx, _provider in enumerate(providers):
+            if is_working != _provider.working:
+                continue
+            if _provider == RetryProvider:
+                continue
+            
+            netloc = urlparse(_provider.url).netloc
+            website = f"[{netloc}]({_provider.url})"
+
+            provider_name = f"`g4f.Provider.{_provider.__name__}`"
+
+            has_gpt_35 = "✔️" if _provider.supports_gpt_35_turbo else "❌"
+            has_gpt_4 = "✔️" if _provider.supports_gpt_4 else "❌"
+            stream = "✔️" if _provider.supports_stream else "❌"
+            can_async = "✔️" if issubclass(_provider, AsyncProvider) else "❌"
+            if _provider.working:
+                status = '![Active](https://img.shields.io/badge/Active-brightgreen)'
+                if responses[idx]:
+                    status = '![Active](https://img.shields.io/badge/Active-brightgreen)'
+                else:
+                    status = '![Unknown](https://img.shields.io/badge/Unknown-grey)'
+            else:
+                status = '![Inactive](https://img.shields.io/badge/Inactive-red)'
+            auth = "✔️" if _provider.needs_auth else "❌"
+
+            lines.append(
+                f"| {website} | {provider_name} | {has_gpt_35} | {has_gpt_4} | {stream} | {can_async} | {status} | {auth} |"
+            )
+    print("\n".join(lines))
+
+def print_models():
+    base_provider_names = {
+        "cohere": "Cohere",
+        "google": "Google",
+        "openai": "OpenAI",
+        "anthropic": "Anthropic",
+        "replicate": "Replicate",
+        "huggingface": "Huggingface",
+    }
+    provider_urls = {
+        "Bard": "https://bard.google.com/",
+        "H2o": "https://www.h2o.ai/",
+        "Vercel": "https://sdk.vercel.ai/",
+    }
+
+    lines = [
+        "| Model | Base Provider | Provider | Website |",
+        "| ----- | ------------- | -------- | ------- |",
+    ]
+
+    _models = get_models()
+    for model in _models:
+        if not model.best_provider or model.best_provider.__name__ not in provider_urls:
+            continue
+
+        name = re.split(r":|/", model.name)[-1]
+        base_provider = base_provider_names[model.base_provider]
+        provider_name = f"g4f.provider.{model.best_provider.__name__}"
+        provider_url = provider_urls[model.best_provider.__name__]
+        netloc = urlparse(provider_url).netloc
+        website = f"[{netloc}]({provider_url})"
+
+        lines.append(f"| {name} | {base_provider} | {provider_name} | {website} |")
+
+    print("\n".join(lines))
+
+
+def get_models():
+    _models = [item[1] for item in models.__dict__.items()]
+    _models = [model for model in _models if type(model) is models.Model]
+    return [model for model in _models if model.name not in ["gpt-3.5-turbo", "gpt-4"]]
+
+
+if __name__ == "__main__":
+    print_imports()
+    print_async()
+    print_providers()
+    print("\n", "-" * 50, "\n")
+    print_models()

etc/tool/vercel.py

@@ -0,0 +1,103 @@
+import json
+import re
+from typing import Any
+
+import quickjs
+from curl_cffi import requests
+
+session = requests.Session(impersonate="chrome107")
+
+
+def get_model_info() -> dict[str, Any]:
+    url = "https://sdk.vercel.ai"
+    response = session.get(url)
+    html = response.text
+    paths_regex = r"static\/chunks.+?\.js"
+    separator_regex = r'"\]\)<\/script><script>self\.__next_f\.push\(\[.,"'
+
+    paths = re.findall(paths_regex, html)
+    paths = [re.sub(separator_regex, "", path) for path in paths]
+    paths = list(set(paths))
+
+    urls = [f"{url}/_next/{path}" for path in paths]
+    scripts = [session.get(url).text for url in urls]
+
+    for script in scripts:
+        models_regex = r'let .="\\n\\nHuman:\",r=(.+?),.='
+        matches = re.findall(models_regex, script)
+
+        if matches:
+            models_str = matches[0]
+            stop_sequences_regex = r"(?<=stopSequences:{value:\[)\D(?<!\])"
+            models_str = re.sub(
+                stop_sequences_regex, re.escape('"\\n\\nHuman:"'), models_str
+            )
+
+            context = quickjs.Context()  # type: ignore
+            json_str: str = context.eval(f"({models_str})").json()  # type: ignore
+            return json.loads(json_str)  # type: ignore
+
+    return {}
+
+
+def convert_model_info(models: dict[str, Any]) -> dict[str, Any]:
+    model_info: dict[str, Any] = {}
+    for model_name, params in models.items():
+        default_params = params_to_default_params(params["parameters"])
+        model_info[model_name] = {"id": params["id"], "default_params": default_params}
+    return model_info
+
+
+def params_to_default_params(parameters: dict[str, Any]):
+    defaults: dict[str, Any] = {}
+    for key, parameter in parameters.items():
+        if key == "maximumLength":
+            key = "maxTokens"
+        defaults[key] = parameter["value"]
+    return defaults
+
+
+def get_model_names(model_info: dict[str, Any]):
+    model_names = model_info.keys()
+    model_names = [
+        name
+        for name in model_names
+        if name not in ["openai:gpt-4", "openai:gpt-3.5-turbo"]
+    ]
+    model_names.sort()
+    return model_names
+
+
+def print_providers(model_names: list[str]):
+    for name in model_names:
+        split_name = re.split(r":|/", name)
+        base_provider = split_name[0]
+        variable_name = split_name[-1].replace("-", "_").replace(".", "")
+        line = f'{variable_name} = Model(name="{name}", base_provider="{base_provider}", best_provider=Vercel,)\n'
+        print(line)
+
+
+def print_convert(model_names: list[str]):
+    for name in model_names:
+        split_name = re.split(r":|/", name)
+        key = split_name[-1]
+        variable_name = split_name[-1].replace("-", "_").replace(".", "")
+        # "claude-instant-v1": claude_instant_v1,
+        line = f'        "{key}": {variable_name},'
+        print(line)
+
+
+def main():
+    model_info = get_model_info()
+    model_info = convert_model_info(model_info)
+    print(json.dumps(model_info, indent=2))
+
+    model_names = get_model_names(model_info)
+    print("-------" * 40)
+    print_providers(model_names)
+    print("-------" * 40)
+    print_convert(model_names)
+
+
+if __name__ == "__main__":
+    main()