summaryrefslogtreecommitdiffstats
path: root/g4f/Provider/ReplicateHome.py
blob: c4e52ad6f3bcfa13398c4c7dde3517b71704a977 (plain) (blame)
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
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
from __future__ import annotations
from typing import Generator, Optional, Dict, Any, Union, List
import random
import asyncio
import base64

from .base_provider import AsyncGeneratorProvider, ProviderModelMixin
from ..typing import AsyncResult, Messages
from ..requests import StreamSession, raise_for_status
from ..errors import ResponseError
from ..image import ImageResponse

class ReplicateHome(AsyncGeneratorProvider, ProviderModelMixin):
    url = "https://replicate.com"
    parent = "Replicate"
    working = True
    default_model = 'meta/meta-llama-3-70b-instruct'
    models = [
        # Models for image generation
        'stability-ai/stable-diffusion-3',
        'bytedance/sdxl-lightning-4step',
        'playgroundai/playground-v2.5-1024px-aesthetic',
        
        # Models for image generation
        'meta/meta-llama-3-70b-instruct',
        'mistralai/mixtral-8x7b-instruct-v0.1',
        'google-deepmind/gemma-2b-it',
    ]

    versions = {
        # Model versions for generating images
        'stability-ai/stable-diffusion-3': [
            "527d2a6296facb8e47ba1eaf17f142c240c19a30894f437feee9b91cc29d8e4f"
        ],
        'bytedance/sdxl-lightning-4step': [
            "5f24084160c9089501c1b3545d9be3c27883ae2239b6f412990e82d4a6210f8f"
        ],
        'playgroundai/playground-v2.5-1024px-aesthetic': [
            "a45f82a1382bed5c7aeb861dac7c7d191b0fdf74d8d57c4a0e6ed7d4d0bf7d24"
        ],
        
        # Model versions for text generation
        'meta/meta-llama-3-70b-instruct': [
            "dp-cf04fe09351e25db628e8b6181276547"
        ],
        'mistralai/mixtral-8x7b-instruct-v0.1': [
            "dp-89e00f489d498885048e94f9809fbc76"
        ],
        'google-deepmind/gemma-2b-it': [
            "dff94eaf770e1fc211e425a50b51baa8e4cac6c39ef074681f9e39d778773626"
        ]
    }

    image_models = {"stability-ai/stable-diffusion-3", "bytedance/sdxl-lightning-4step", "playgroundai/playground-v2.5-1024px-aesthetic"}
    text_models = {"meta/meta-llama-3-70b-instruct", "mistralai/mixtral-8x7b-instruct-v0.1", "google-deepmind/gemma-2b-it"}

    model_aliases = {
        "sd-3": "stability-ai/stable-diffusion-3",
        "sdxl": "bytedance/sdxl-lightning-4step",
        "playground-v2.5": "playgroundai/playground-v2.5-1024px-aesthetic",
        "llama-3-70b": "meta/meta-llama-3-70b-instruct",
        "mixtral-8x7b": "mistralai/mixtral-8x7b-instruct-v0.1",
        "gemma-2b": "google-deepmind/gemma-2b-it",
    }

    @classmethod
    def get_model(cls, model: str) -> str:
        if model in cls.models:
            return model
        elif model in cls.model_aliases:
            return cls.model_aliases[model]
        else:
            return cls.default_model

    @classmethod
    async def create_async_generator(
        cls,
        model: str,
        messages: Messages,
        **kwargs: Any
    ) -> Generator[Union[str, ImageResponse], None, None]:
        yield await cls.create_async(messages[-1]["content"], model, **kwargs)

    @classmethod
    async def create_async(
        cls,
        prompt: str,
        model: str,
        api_key: Optional[str] = None,
        proxy: Optional[str] = None,
        timeout: int = 180,
        version: Optional[str] = None,
        extra_data: Dict[str, Any] = {},
        **kwargs: Any
    ) -> Union[str, ImageResponse]:
        model = cls.get_model(model)  # Use the get_model method to resolve model name
        headers = {
            'Accept-Encoding': 'gzip, deflate, br',
            'Accept-Language': 'en-US',
            'Connection': 'keep-alive',
            'Origin': cls.url,
            'Referer': f'{cls.url}/',
            'Sec-Fetch-Dest': 'empty',
            'Sec-Fetch-Mode': 'cors',
            'Sec-Fetch-Site': 'same-site',
            'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36',
            'sec-ch-ua': '"Google Chrome";v="119", "Chromium";v="119", "Not?A_Brand";v="24"',
            'sec-ch-ua-mobile': '?0',
            'sec-ch-ua-platform': '"macOS"',
        }

        if version is None:
            version = random.choice(cls.versions.get(model, []))
        if api_key is not None:
            headers["Authorization"] = f"Bearer {api_key}"

        async with StreamSession(
            proxies={"all": proxy},
            headers=headers,
            timeout=timeout
        ) as session:
            data = {
                "input": {
                    "prompt": prompt,
                    **extra_data
                },
                "version": version
            }
            if api_key is None:
                data["model"] = model
                url = "https://homepage.replicate.com/api/prediction"
            else:
                url = "https://api.replicate.com/v1/predictions"
            async with session.post(url, json=data) as response:
                await raise_for_status(response)
                result = await response.json()
            if "id" not in result:
                raise ResponseError(f"Invalid response: {result}")

            while True:
                if api_key is None:
                    url = f"https://homepage.replicate.com/api/poll?id={result['id']}"
                else:
                    url = f"https://api.replicate.com/v1/predictions/{result['id']}"
                async with session.get(url) as response:
                    await raise_for_status(response)
                    result = await response.json()
                    if "status" not in result:
                        raise ResponseError(f"Invalid response: {result}")
                    if result["status"] == "succeeded":
                        output = result['output']
                        if model in cls.text_models:
                            return ''.join(output) if isinstance(output, list) else output
                        elif model in cls.image_models:
                            images: List[Any] = output
                            images = images[0] if len(images) == 1 else images
                            return ImageResponse(images, prompt)
                    elif result["status"] == "failed":
                        raise ResponseError(f"Prediction failed: {result}")
                    await asyncio.sleep(0.5)