New TwitterBio provider with support for gpt-3.5-turbo and mixtral-8x7b models

g4f/Provider/TwitterBio.py

@@ -0,0 +1,103 @@
+from __future__ import annotations
+
+import json
+import re
+from aiohttp import ClientSession
+
+from ..typing import AsyncResult, Messages
+from .base_provider import AsyncGeneratorProvider, ProviderModelMixin
+from .helper import format_prompt
+
+class TwitterBio(AsyncGeneratorProvider, ProviderModelMixin):
+    url = "https://www.twitterbio.io"
+    api_endpoint_mistral = "https://www.twitterbio.io/api/mistral"
+    api_endpoint_openai = "https://www.twitterbio.io/api/openai"
+    working = True
+    supports_gpt_35_turbo = True
+    
+    default_model = 'gpt-3.5-turbo'
+    models = [
+        'mistralai/Mixtral-8x7B-Instruct-v0.1',
+        'gpt-3.5-turbo',
+    ]
+    
+    model_aliases = {
+        "mixtral-8x7b": "mistralai/Mixtral-8x7B-Instruct-v0.1",
+    }
+
+    @classmethod
+    def get_model(cls, model: str) -> str:
+        if model in cls.models:
+            return model
+        return cls.default_model
+
+    @staticmethod
+    def format_text(text: str) -> str:
+        text = re.sub(r'\s+', ' ', text.strip())
+        text = re.sub(r'\s+([,.!?])', r'\1', text)
+        return text
+
+    @classmethod
+    async def create_async_generator(
+        cls,
+        model: str,
+        messages: Messages,
+        proxy: str = None,
+        **kwargs
+    ) -> AsyncResult:
+        model = cls.get_model(model)
+        
+        headers = {
+            "accept": "*/*",
+            "accept-language": "en-US,en;q=0.9",
+            "cache-control": "no-cache",
+            "content-type": "application/json",
+            "origin": cls.url,
+            "pragma": "no-cache",
+            "priority": "u=1, i",
+            "referer": f"{cls.url}/",
+            "sec-ch-ua": '"Chromium";v="127", "Not)A;Brand";v="99"',
+            "sec-ch-ua-mobile": "?0",
+            "sec-ch-ua-platform": '"Linux"',
+            "sec-fetch-dest": "empty",
+            "sec-fetch-mode": "cors",
+            "sec-fetch-site": "same-origin",
+            "user-agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/127.0.0.0 Safari/537.36"
+        }
+        async with ClientSession(headers=headers) as session:
+            prompt = format_prompt(messages)
+            data = {
+                "prompt": f'{prompt}.'
+            }
+            
+            if model == 'mistralai/Mixtral-8x7B-Instruct-v0.1':
+                api_endpoint = cls.api_endpoint_mistral
+            elif model == 'gpt-3.5-turbo':
+                api_endpoint = cls.api_endpoint_openai
+            else:
+                raise ValueError(f"Unsupported model: {model}")
+            
+            async with session.post(api_endpoint, json=data, proxy=proxy) as response:
+                response.raise_for_status()
+                buffer = ""
+                async for line in response.content:
+                    line = line.decode('utf-8').strip()
+                    if line.startswith('data: '):
+                        try:
+                            json_data = json.loads(line[6:])
+                            if model == 'mistralai/Mixtral-8x7B-Instruct-v0.1':
+                                if 'choices' in json_data and len(json_data['choices']) > 0:
+                                    text = json_data['choices'][0].get('text', '')
+                                    if text:
+                                        buffer += text
+                            elif model == 'gpt-3.5-turbo':
+                                text = json_data.get('text', '')
+                                if text:
+                                    buffer += text
+                        except json.JSONDecodeError:
+                            continue
+                    elif line == 'data: [DONE]':
+                        break
+                
+                if buffer:
+                    yield cls.format_text(buffer)

g4f/Provider/__init__.py

@@ -51,6 +51,7 @@ from .Replicate        import Replicate
 from .ReplicateHome    import ReplicateHome
 from .Rocks            import Rocks
 from .TeachAnything    import TeachAnything
+from .TwitterBio       import TwitterBio
 from .Upstage          import Upstage
 from .Vercel           import Vercel
 from .WhiteRabbitNeo   import WhiteRabbitNeo

g4f/models.py

@@ -37,6 +37,7 @@ from .Provider import (
     Replicate,
     ReplicateHome,
     TeachAnything,
+    TwitterBio,
     Upstage,
     You,
 )
@@ -91,7 +92,7 @@ gpt_35_turbo = Model(
     name          = 'gpt-3.5-turbo',
     base_provider = 'openai',
     best_provider = IterListProvider([
-        Allyfy,
+        Allyfy, TwitterBio,
     ])
 )
 
@@ -140,50 +141,50 @@ gigachat = Model(
 ### Meta ###
 meta = Model(
     name          = "meta-ai",
-    base_provider = "meta",
+    base_provider = "Meta",
     best_provider = MetaAI
 )
 
 llama_3_8b = Model(
     name          = "llama-3-8b",
-    base_provider = "meta",
+    base_provider = "Meta",
     best_provider = IterListProvider([DeepInfra, Replicate])
 )
 
 llama_3_70b = Model(
     name          = "llama-3-70b",
-    base_provider = "meta",
+    base_provider = "Meta",
     best_provider = IterListProvider([ReplicateHome, DeepInfra, PerplexityLabs, Replicate])
 )
 
 llama_3_1_8b = Model(
     name          = "llama-3.1-8b",
-    base_provider = "meta",
+    base_provider = "Meta",
     best_provider = IterListProvider([Blackbox])
 )
 
 llama_3_1_70b = Model(
     name          = "llama-3.1-70b",
-    base_provider = "meta",
+    base_provider = "Meta",
     best_provider = IterListProvider([DDG, HuggingChat, FreeGpt, Blackbox, TeachAnything, HuggingFace])
 )
 
 llama_3_1_405b = Model(
     name          = "llama-3.1-405b",
-    base_provider = "meta",
+    base_provider = "Meta",
     best_provider = IterListProvider([HuggingChat, Blackbox, HuggingFace])
 )
 
 ### Mistral ###
 mixtral_8x7b = Model(
     name          = "mixtral-8x7b",
-    base_provider = "huggingface",
-    best_provider = IterListProvider([HuggingChat, DDG, ReplicateHome, DeepInfra, HuggingFace,])
+    base_provider = "Mistral",
+    best_provider = IterListProvider([HuggingChat, DDG, ReplicateHome, TwitterBio, DeepInfra, HuggingFace,])
 )
 
 mistral_7b = Model(
     name          = "mistral-7b",
-    base_provider = "huggingface",
+    base_provider = "Mistral",
     best_provider = IterListProvider([HuggingChat, HuggingFace, DeepInfra])
 )