1 files changed, 8 insertions, 109 deletions

diff --git a/g4f/Provider/Providers/Vercel.py b/g4f/Provider/Providers/Vercel.py
index e1e84f9a..03d9be17 100644
--- a/g4f/Provider/Providers/Vercel.py
+++ b/g4f/Provider/Providers/Vercel.py
@@ -42,120 +42,19 @@ vercel_models = {'anthropic:claude-instant-v1': {'id': 'anthropic:claude-instant
     'id': 'huggingface:bigcode/santacoder', 'provider': 'huggingface', 'providerHumanName': 'HuggingFace', 'makerHumanName': 'BigCode', 'instructions': 'The model was trained on GitHub code. As such it is not an instruction model and commands like "Write a function that computes the square root." do not work well. You should phrase commands like they occur in source code such as comments (e.g. # the following function computes the sqrt) or write a function signature and docstring and let the model complete the function body.', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 0.95, 'range': [0.01, 0.99]}, 'topK': {'value': 4, 'range': [1, 500]}, 'repetitionPenalty': {'value': 1.03, 'range': [0.1, 2]}}, 'name': 'santacoder'}, 'cohere:command-medium-nightly': {'id': 'cohere:command-medium-nightly', 'provider': 'cohere', 'providerHumanName': 'Cohere', 'makerHumanName': 'Cohere', 'name': 'command-medium-nightly', 'parameters': {'temperature': {'value': 0.9, 'range': [0, 2]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0, 1]}, 'topK': {'value': 0, 'range': [0, 500]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'cohere:command-xlarge-nightly': {'id': 'cohere:command-xlarge-nightly', 'provider': 'cohere', 'providerHumanName': 'Cohere', 'makerHumanName': 'Cohere', 'name': 'command-xlarge-nightly', 'parameters': {'temperature': {'value': 0.9, 'range': [0, 2]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0, 1]}, 'topK': {'value': 0, 'range': [0, 500]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:gpt-4': {'id': 'openai:gpt-4', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'gpt-4', 'minBillingTier': 'pro', 'parameters': {'temperature': {'value': 0.7, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:code-cushman-001': {'id': 'openai:code-cushman-001', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}, 'name': 'code-cushman-001'}, 'openai:code-davinci-002': {'id': 'openai:code-davinci-002', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}, 'name': 'code-davinci-002'}, 'openai:gpt-3.5-turbo': {'id': 'openai:gpt-3.5-turbo', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'parameters': {'temperature': {'value': 0.7, 'range': [0, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'topK': {'value': 1, 'range': [1, 500]}, 'presencePenalty': {'value': 1, 'range': [0, 1]}, 'frequencyPenalty': {'value': 1, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}, 'name': 'gpt-3.5-turbo'}, 'openai:text-ada-001': {'id': 'openai:text-ada-001', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-ada-001', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:text-babbage-001': {'id': 'openai:text-babbage-001', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-babbage-001', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:text-curie-001': {'id': 'openai:text-curie-001', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-curie-001', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:text-davinci-002': {'id': 'openai:text-davinci-002', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-davinci-002', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}, 'openai:text-davinci-003': {'id': 'openai:text-davinci-003', 'provider': 'openai', 'providerHumanName': 'OpenAI', 'makerHumanName': 'OpenAI', 'name': 'text-davinci-003', 'parameters': {'temperature': {'value': 0.5, 'range': [0.1, 1]}, 'maximumLength': {'value': 200, 'range': [50, 1024]}, 'topP': {'value': 1, 'range': [0.1, 1]}, 'presencePenalty': {'value': 0, 'range': [0, 1]}, 'frequencyPenalty': {'value': 0, 'range': [0, 1]}, 'stopSequences': {'value': [], 'range': []}}}}
 
 
-# based on https://github.com/ading2210/vercel-llm-api // modified
-class Client:
-    def __init__(self):
-        self.session = requests.Session()
-        self.headers = {
-            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/110 Safari/537.36',
-            'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,*/*;q=0.8',
-            'Accept-Encoding': 'gzip, deflate, br',
-            'Accept-Language': 'en-US,en;q=0.5',
-            'Te': 'trailers',
-            'Upgrade-Insecure-Requests': '1'
-        }
-        self.session.headers.update(self.headers)
-
-    def get_token(self):
-            b64 = self.session.get('https://sdk.vercel.ai/openai.jpeg').text
-            data = json.loads(base64.b64decode(b64))
-
-            code = 'const globalThis = {data: `sentinel`}; function token() {return (%s)(%s)}' % (
-                data['c'], data['a'])
-
-            token_string = json.dumps(separators=(',', ':'),
-                                    obj={'r': execjs.compile(code).call('token'), 't': data['t']})
-
-            return base64.b64encode(token_string.encode()).decode()
-
-    def get_default_params(self, model_id):
-        return {key: param['value'] for key, param in vercel_models[model_id]['parameters'].items()}
-
-    def generate(self, model_id: str, prompt: str, params: dict = {}):
-        if not ':' in model_id:
-            model_id = models[model_id]
-
-        defaults = self.get_default_params(model_id)
-
-        payload = defaults | params | {
-            'prompt': prompt,
-            'model': model_id,
-        }
-
-        headers = self.headers | {
-            'Accept-Encoding': 'gzip, deflate, br',
-            'Custom-Encoding': self.get_token(),
-            'Host': 'sdk.vercel.ai',
-            'Origin': 'https://sdk.vercel.ai',
-            'Referrer': 'https://sdk.vercel.ai',
-            'Sec-Fetch-Dest': 'empty',
-            'Sec-Fetch-Mode': 'cors',
-            'Sec-Fetch-Site': 'same-origin',
-        }
-
-        chunks_queue = queue.Queue()
-        error = None
-        response = None
-
-        def callback(data):
-            chunks_queue.put(data.decode())
-
-        def request_thread():
-            nonlocal response, error
-            for _ in range(3):
-                try:
-                    response = self.session.post('https://sdk.vercel.ai/api/generate',
-                                                 json=payload, headers=headers, content_callback=callback)
-                    response.raise_for_status()
-
-                except Exception as e:
-                    if _ == 2:
-                        error = e
-
-                    else:
-                        continue
-
-        thread = threading.Thread(target=request_thread, daemon=True)
-        thread.start()
-
-        text = ''
-        index = 0
-        while True:
-            try:
-                chunk = chunks_queue.get(block=True, timeout=0.1)
-
-            except queue.Empty:
-                if error:
-                    raise error
-
-                elif response:
-                    break
-
-                else:
-                    continue
-
-            text += chunk
-            lines = text.split('\n')
-
-            if len(lines) - 1 > index:
-                new = lines[index:-1]
-                for word in new:
-                    yield json.loads(word)
-                index = len(lines) - 1
-
 def _create_completion(model: str, messages: list, stream: bool, **kwargs):
+    return
+    # conversation = 'This is a conversation between a human and a language model, respond to the last message accordingly, referring to the past history of messages if needed.\n'
     
-    conversation = 'This is a conversation between a human and a language model, respond to the last message accordingly, referring to the past history of messages if needed.\n'
-    
-    for message in messages:
-        conversation += '%s: %s\n' % (message['role'], message['content'])
+    # for message in messages:
+    #     conversation += '%s: %s\n' % (message['role'], message['content'])
     
-    conversation += 'assistant: '
+    # conversation += 'assistant: '
     
-    completion = Client().generate(model, conversation)
+    # completion = Client().generate(model, conversation)
 
-    for token in completion:
-        yield token
+    # for token in completion:
+    #     yield token
 
 params = f'g4f.Providers.{os.path.basename(__file__)[:-3]} supports: ' + \
     '(%s)' % ', '.join([f"{name}: {get_type_hints(_create_completion)[name].__name__}" for name in _create_completion.__code__.co_varnames[:_create_completion.__code__.co_argcount]])
\ No newline at end of file