0.2.3.1

1 files changed, 75 insertions, 0 deletions

diff --git a/g4f/Provider/HuggingFace.py b/g4f/Provider/HuggingFace.py
new file mode 100644
index 00000000..a73411ce
--- /dev/null
+++ b/g4f/Provider/HuggingFace.py
@@ -0,0 +1,75 @@
+from __future__ import annotations
+
+import json
+from aiohttp import ClientSession, BaseConnector
+
+from ..typing import AsyncResult, Messages
+from .base_provider import AsyncGeneratorProvider, ProviderModelMixin
+from .helper import get_connector
+from ..errors import RateLimitError, ModelNotFoundError
+
+class HuggingFace(AsyncGeneratorProvider, ProviderModelMixin):
+    url = "https://huggingface.co/chat"
+    working = True
+    supports_message_history = True
+    default_model = "mistralai/Mixtral-8x7B-Instruct-v0.1"
+
+    @classmethod
+    async def create_async_generator(
+        cls,
+        model: str,
+        messages: Messages,
+        stream: bool = True,
+        proxy: str = None,
+        connector: BaseConnector = None,
+        api_base: str = "https://api-inference.huggingface.co",
+        api_key: str = None,
+        max_new_tokens: int = 1024,
+        temperature: float = 0.7,
+        **kwargs
+    ) -> AsyncResult:
+        model = cls.get_model(model)
+        headers = {}
+        if api_key is not None:
+            headers["Authorization"] = f"Bearer {api_key}"
+        params = {
+            "return_full_text": False,
+            "max_new_tokens": max_new_tokens,
+            "temperature": temperature,
+            **kwargs
+        }
+        payload = {"inputs": format_prompt(messages), "parameters": params, "stream": stream}
+        async with ClientSession(
+            headers=headers,
+            connector=get_connector(connector, proxy)
+        ) as session:
+            async with session.post(f"{api_base.rstrip('/')}/models/{model}", json=payload) as response:
+                if response.status == 429:
+                    raise RateLimitError("Rate limit reached. Set a api_key")
+                elif response.status == 404:
+                    raise ModelNotFoundError(f"Model is not supported: {model}")
+                elif response.status != 200:
+                    raise RuntimeError(f"Response {response.status}: {await response.text()}")
+                if stream:
+                    first = True
+                    async for line in response.content:
+                        if line.startswith(b"data:"):
+                            data = json.loads(line[5:])
+                            if not data["token"]["special"]:
+                                chunk = data["token"]["text"]
+                                if first:
+                                    first = False
+                                    chunk = chunk.lstrip()
+                                yield chunk
+                else:
+                    yield (await response.json())[0]["generated_text"].strip()
+
+def format_prompt(messages: Messages) -> str:
+    system_messages = [message["content"] for message in messages if message["role"] == "system"]
+    question = " ".join([messages[-1]["content"], *system_messages])
+    history = "".join([
+        f"<s>[INST]{messages[idx-1]['content']} [/INST] {message}</s>"
+        for idx, message in enumerate(messages)
+        if message["role"] == "assistant"
+    ])
+    return f"{history}<s>[INST] {question} [/INST]"
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