Commit new images and usage data

README.md

@@ -5,4 +5,8 @@ colorFrom: green
 colorTo: purple
 sdk: docker
 pinned: false
+
+hf_oauth: true
+hf_oauth_scopes:
+ - inference-api
 ---

app.py

@@ -1,221 +1,23 @@
 import os
-import json
-from g4f.providers.response import Reasoning, JsonConversation, FinishReason
-from g4f.typing import AsyncResult, Messages
-import json
-import re
-import time
-import logging
-from urllib.parse import quote_plus
 from fastapi import FastAPI, Response, Request
 from fastapi.responses import RedirectResponse
 
-from g4f.image import images_dir, copy_images
-
+from g4f.image import images_dir
 import g4f.api
 import g4f.Provider
 
-from g4f.Provider.base_provider import AsyncGeneratorProvider, ProviderModelMixin
-from g4f.typing import AsyncResult, Messages
-from g4f.requests import StreamSession
-from g4f.providers.response import ProviderInfo, JsonConversation, PreviewResponse, SynthesizeData, TitleGeneration, RequestLogin
-from g4f.providers.response import Parameters, FinishReason, Usage, Reasoning
-from g4f.errors import ModelNotSupportedError
-from g4f import debug
-
 import demo
-
-class BackendApi(AsyncGeneratorProvider, ProviderModelMixin):
-    url = "https://ahe.hopto.org"
-    working = True
-    ssl = False
-
-    models = [
-        *g4f.Provider.OpenaiAccount.get_models(),
-        *g4f.Provider.PerplexityLabs.get_models(),
-        "flux",
-        "flux-pro",
-        "MiniMax-01",
-        "Microsoft Copilot",
-    ]
-
-    @classmethod
-    def get_model(cls, model):
-        if "MiniMax" in model:
-            model = "MiniMax"
-        elif "Copilot" in model:
-            model = "Copilot"
-        elif "FLUX" in model:
-            model = f"flux-{model.split('-')[-1]}"
-        elif "flux" in model:
-            model = model.split(' ')[-1]
-        elif model in g4f.Provider.OpenaiAccount.get_models():
-            pass
-        elif model in g4f.Provider.PerplexityLabs.get_models():
-            pass
-        else:
-            raise ModelNotSupportedError(f"Model: {model}")
-        return model
-
-    @classmethod
-    def get_provider(cls, model):
-        if model.startswith("MiniMax"):
-            return "HailuoAI"
-        elif model == "Copilot" or "dall-e" in model:
-            return "CopilotAccount"
-        elif model in g4f.Provider.OpenaiAccount.get_models():
-            return "OpenaiAccount"
-        elif model in g4f.Provider.PerplexityLabs.get_models():
-            return "PerplexityLabs"
-        return None
-
-    @classmethod
-    async def create_async_generator(
-        cls,
-        model: str,
-        messages: Messages,
-        api_key: str = None,
-        proxy: str = None,
-        timeout: int = 0,
-        **kwargs
-    ) -> AsyncResult:
-        debug.log(f"{__name__}: {api_key}")
-        if "dall-e" in model and "prompt" not in kwargs:
-            kwargs["prompt"] = messages[-1]["content"]
-            messages[-1]["content"] = f"Generate a image: {kwargs['prompt']}"
-
-        async with StreamSession(
-            proxy=proxy,
-            headers={"Accept": "text/event-stream", **demo.headers},
-            timeout=timeout
-        ) as session:
-            model = cls.get_model(model)
-            provider = cls.get_provider(model)
-            async with session.post(f"{cls.url}/backend-api/v2/conversation", json={
-                **kwargs,
-                "model": model,
-                "messages": messages,
-                "provider": provider
-            }, ssl=cls.ssl) as response:
-                is_thinking = 0
-                async for line in response.iter_lines():
-                    response.raise_for_status()
-                    data = json.loads(line)
-                    data_type = data.pop("type")
-                    if data_type == "provider":
-                        yield ProviderInfo(**data[data_type])
-                        provider = data[data_type]["name"]
-                    elif data_type == "conversation":
-                        yield JsonConversation(**data[data_type][provider] if provider in data[data_type] else data[data_type][""])
-                    elif data_type == "conversation_id":
-                        pass
-                    elif data_type == "message":
-                        yield Exception(data)
-                    elif data_type == "preview":
-                        yield PreviewResponse(data[data_type])
-                    elif data_type == "content":
-                        def on_image(match):
-                            extension = match.group(3).split(".")[-1].split("?")[0]
-                            extension = "" if not extension or len(extension) > 4 else f".{extension}"
-                            filename = f"{int(time.time())}_{quote_plus(match.group(1)[:100], '')}{extension}"
-                            download_url = f"/download/{filename}?url={cls.url}{match.group(3)}"
-                            return f"[![{match.group(1)}]({download_url})](/images/{filename})"
-                        if "<think>" in data[data_type]:
-                            data[data_type] = data[data_type].split("<think>", 1)
-                            yield data[data_type][0]
-                            yield Reasoning(data[data_type][1])
-                            yield Reasoning(None, "Is thinking...")
-                            is_thinking = time.time()
-                        if "</think>" in data[data_type][1]:
-                            data[data_type][1] = data[data_type].split("</think>", 1)
-                            yield Reasoning(data[data_type][0])
-                            yield Reasoning(None, f"Finished in {round(time.time()-is_thinking, 2)} seconds")
-                            yield data[data_type][1]
-                            is_thinking = 0
-                        elif is_thinking:
-                            yield Reasoning(data[data_type])
-                        else:
-                            yield re.sub(r'\[\!\[(.+?)\]\(([^)]+?)\)\]\(([^)]+?)\)', on_image, data[data_type])
-                    elif data_type =="synthesize":
-                        yield SynthesizeData(**data[data_type])
-                    elif data_type == "parameters":
-                        yield Parameters(**data[data_type])
-                    elif data_type == "usage":
-                        yield Usage(**data[data_type])
-                    elif data_type == "reasoning":
-                        yield Reasoning(**data)
-                    elif data_type == "login":
-                        pass
-                    elif data_type == "title":
-                        yield TitleGeneration(data[data_type])
-                    elif data_type == "finish":
-                        yield FinishReason(data[data_type]["reason"])
-                    elif data_type == "log":
-                        debug.log(data[data_type])
-                    else:
-                        debug.log(f"Unknown data: ({data_type}) {data}")
-
+from demo.BackendApi import BackendApi
 g4f.Provider.__map__["Feature"] = BackendApi
 
-import asyncio
-import uuid
-from aiohttp import ClientSession, ClientError
-from g4f.typing import Optional, Cookies
-from g4f.image import is_accepted_format
 
-async def copy_images(
-    images: list[str],
-    cookies: Optional[Cookies] = None,
-    headers: dict = None,
-    proxy: Optional[str] = None,
-    add_url: bool = True,
-    target: str = None,
-    ssl: bool = None
-) -> list[str]:
-    if add_url:
-        add_url = not cookies
-    #ensure_images_dir()
-    async with ClientSession(
-        #connector=get_connector(proxy=proxy),
-        cookies=cookies,
-        headers=headers,
-    ) as session:
-        async def copy_image(image: str, target: str = None) -> str:
-            if target is None or len(images) > 1:
-                target = os.path.join(images_dir, f"{int(time.time())}_{str(uuid.uuid4())}")
-            try:
-                if image.startswith("data:"):
-                    pass
-                    #with open(target, "wb") as f:
-                    #    f.write(extract_data_uri(image))
-                else:
-                    try:
-                        async with session.get(image, proxy=proxy, ssl=ssl) as response:
-                            response.raise_for_status()
-                            with open(target, "wb") as f:
-                                async for chunk in response.content.iter_chunked(4096):
-                                    f.write(chunk)
-                    except ClientError as e:
-                        debug.log(f"copy_images failed: {e.__class__.__name__}: {e}")
-                        return image
-                if "." not in target:
-                    with open(target, "rb") as f:
-                        extension = is_accepted_format(f.read(12)).split("/")[-1]
-                        extension = "jpg" if extension == "jpeg" else extension
-                        new_target = f"{target}.{extension}"
-                        os.rename(target, new_target)
-                        target = new_target
-            finally:
-                if "." not in target and os.path.exists(target):
-                    os.unlink(target)
-            return f"/images/{os.path.basename(target)}{'?url=' + image if add_url and not image.startswith('data:') else ''}"
-
-        return await asyncio.gather(*[copy_image(image, target) for image in images])
+g4f.models.demo_models
+from g4f.image import copy_images
 
 def create_app():
     g4f.debug.logging = True
     g4f.api.AppConfig.gui = True
-    g4f.api.AppConfig.demo = False
+    g4f.api.AppConfig.demo = True
 
     app = FastAPI()
 
@@ -259,14 +61,4 @@ def create_app():
 
     return app
 
-
-class NoHomeFilter(logging.Filter):
-    def filter(self, record):
-        if '"GET / HTTP/1.1" 200 OK' in record.getMessage():
-            return False
-        if '"GET /static/' in record.getMessage():
-            return False
-        return True
-logging.getLogger("uvicorn.access").addFilter(NoHomeFilter())
-
 app = create_app()

demo/BackendApi.py

@@ -0,0 +1,146 @@
+import json
+import re
+import time
+from urllib.parse import quote_plus
+
+import g4f.api
+import g4f.Provider
+
+from g4f.Provider.base_provider import AsyncGeneratorProvider, ProviderModelMixin
+from g4f.typing import AsyncResult, Messages
+from g4f.requests import StreamSession
+from g4f.providers.response import *
+from g4f.errors import ModelNotSupportedError
+from g4f import debug
+from . import headers
+
+class BackendApi(AsyncGeneratorProvider, ProviderModelMixin):
+    url = "https://ahe.hopto.org"
+    working = True
+    ssl = False
+
+    models = [
+        *g4f.Provider.OpenaiAccount.get_models(),
+        *g4f.Provider.PerplexityLabs.get_models(),
+        "flux",
+        "flux-pro",
+        "MiniMax-01",
+        "Microsoft Copilot",
+    ]
+
+    @classmethod
+    def get_model(cls, model):
+        if "MiniMax" in model:
+            model = "MiniMax"
+        elif "Copilot" in model:
+            model = "Copilot"
+        elif "FLUX" in model:
+            model = f"flux-{model.split('-')[-1]}"
+        elif "flux" in model:
+            model = model.split(' ')[-1]
+        elif model in g4f.Provider.OpenaiAccount.get_models():
+            pass
+        elif model in g4f.Provider.PerplexityLabs.get_models():
+            pass
+        else:
+            raise ModelNotSupportedError(f"Model: {model}")
+        return model
+
+    @classmethod
+    def get_provider(cls, model):
+        if model.startswith("MiniMax"):
+            return "HailuoAI"
+        elif model == "Copilot" or "dall-e" in model:
+            return "CopilotAccount"
+        elif model in g4f.Provider.OpenaiAccount.get_models():
+            return "OpenaiAccount"
+        elif model in g4f.Provider.PerplexityLabs.get_models():
+            return "PerplexityLabs"
+        return None
+
+    @classmethod
+    async def create_async_generator(
+        cls,
+        model: str,
+        messages: Messages,
+        api_key: str = None,
+        proxy: str = None,
+        timeout: int = 0,
+        **kwargs
+    ) -> AsyncResult:
+        debug.log(f"{cls.__name__}: {api_key}")
+    
+        if "dall-e" in model and "prompt" not in kwargs:
+            kwargs["prompt"] = messages[-1]["content"]
+            messages[-1]["content"] = f"Generate a image: {kwargs['prompt']}"
+
+        async with StreamSession(
+            proxy=proxy,
+            headers={"Accept": "text/event-stream", **headers},
+            timeout=timeout
+        ) as session:
+            model = cls.get_model(model)
+            provider = cls.get_provider(model)
+            async with session.post(f"{cls.url}/backend-api/v2/conversation", json={
+                **kwargs,
+                "model": model,
+                "messages": messages,
+                "provider": provider
+            }, ssl=cls.ssl) as response:
+                is_thinking = 0
+                async for line in response.iter_lines():
+                    response.raise_for_status()
+                    data = json.loads(line)
+                    data_type = data.pop("type")
+                    if data_type == "provider":
+                        yield ProviderInfo(**data[data_type])
+                        provider = data[data_type]["name"]
+                    elif data_type == "conversation":
+                        yield JsonConversation(**data[data_type][provider] if provider in data[data_type] else data[data_type][""])
+                    elif data_type == "conversation_id":
+                        pass
+                    elif data_type == "message":
+                        yield Exception(data)
+                    elif data_type == "preview":
+                        yield PreviewResponse(data[data_type])
+                    elif data_type == "content":
+                        def on_image(match):
+                            extension = match.group(3).split(".")[-1].replace("%3F", "?").split("?")[0]
+                            extension = ".jpg" if not extension or len(extension) > 4 else f".{extension}"
+                            filename = f"{int(time.time())}_{quote_plus(model, '')}.{quote_plus(match.group(1)[:100], '')}{extension}"
+                            download_url = f"/download/{filename}?url={cls.url}{match.group(3)}"
+                            return f"[![{match.group(1)}]({download_url})](/images/{filename})"
+                        if "<think>" in data[data_type]:
+                            data[data_type] = data[data_type].split("<think>", 1)
+                            yield data[data_type][0]
+                            yield Reasoning(data[data_type][1])
+                            yield Reasoning(None, "Is thinking...")
+                            is_thinking = time.time()
+                        if "</think>" in data[data_type]:
+                            data[data_type][1] = data[data_type].split("</think>", 1)
+                            yield Reasoning(data[data_type][0])
+                            yield Reasoning(None, f"Finished in {round(time.time()-is_thinking, 2)} seconds")
+                            yield data[data_type][1]
+                            is_thinking = 0
+                        elif is_thinking:
+                            yield Reasoning(data[data_type])
+                        else:
+                            yield re.sub(r'\[\!\[(.+?)\]\(([^)]+?)\)\]\(([^)]+?)\)', on_image, data[data_type])
+                    elif data_type =="synthesize":
+                        yield SynthesizeData(**data[data_type])
+                    elif data_type == "parameters":
+                        yield Parameters(**data[data_type])
+                    elif data_type == "usage":
+                        yield Usage(**data[data_type])
+                    elif data_type == "reasoning":
+                        yield Reasoning(**data)
+                    elif data_type == "login":
+                        pass
+                    elif data_type == "title":
+                        yield TitleGeneration(data[data_type])
+                    elif data_type == "finish":
+                        yield FinishReason(data[data_type]["reason"])
+                    elif data_type == "log":
+                        debug.log(data[data_type])
+                    else:
+                        debug.log(f"Unknown data: ({data_type}) {data}")

har_and_cookies/.usage/2025-01-30.jsonl

@@ -0,0 +1,53 @@
+{"user": "roxky", "model": "flux-pro", "provider": "PollinationsAI", "prompt_tokens": 8, "completion_tokens": 115, "total_tokens": 123}
+{"user": "roxky", "model": "sonar-reasoning", "provider": "PerplexityLabs", "prompt_tokens": 18, "completion_tokens": 10, "total_tokens": 28}
+{"user": "roxky", "model": "sonar-reasoning", "provider": "PerplexityLabs", "prompt_tokens": 38, "completion_tokens": 10, "total_tokens": 48}
+{"user": "roxky", "model": "deepseek-r1", "provider": "HuggingFaceAPI", "prompt_tokens": 58, "completion_tokens": 12, "total_tokens": 70}
+{"user": "roxky", "model": "flux-schnell", "provider": "HuggingFace", "prompt_tokens": 8, "completion_tokens": 85, "total_tokens": 93}
+{"user": "roxky", "model": "deepseek-r1", "provider": "HuggingFace", "prompt_tokens": 10, "completion_tokens": 68, "total_tokens": 78}
+{"user": "roxky", "model": "auto", "provider": "OpenaiAccount", "prompt_tokens": 8, "completion_tokens": 11, "total_tokens": 19}
+{"user": "roxky", "model": "gpt-4o", "provider": "OpenaiAccount", "prompt_tokens": 9, "completion_tokens": 10, "total_tokens": 19}
+{"user": "roxky", "model": "flux-schnell", "provider": "HuggingFace", "prompt_tokens": 9, "completion_tokens": 76, "total_tokens": 85}
+{"user": "RubielLabarta", "model": "flux-dev", "provider": "HuggingSpace", "prompt_tokens": 17, "completion_tokens": 220, "total_tokens": 237}
+{"user": "RubielLabarta", "model": "llama-3.2-11b", "provider": "HuggingFaceAPI", "prompt_tokens": 8, "completion_tokens": 8, "total_tokens": 16}
+{"user": "RubielLabarta", "model": "llama-3.2-11b", "provider": "HuggingFaceAPI", "prompt_tokens": 28, "completion_tokens": 25, "total_tokens": 53}
+{"user": "RubielLabarta", "model": "gpt-4o", "provider": "Blackbox", "prompt_tokens": 60, "completion_tokens": 28, "total_tokens": 88}
+{"user": "RubielLabarta", "model": "gpt-4o", "provider": "Blackbox", "prompt_tokens": 60, "completion_tokens": 28, "total_tokens": 88}
+{"user": "RubielLabarta", "model": "deepseek-r1", "provider": "HuggingFaceAPI", "prompt_tokens": 60, "completion_tokens": 83, "total_tokens": 143}
+{"user": "roxky", "model": "sonar-reasoning", "provider": "PerplexityLabs", "prompt_tokens": 8, "completion_tokens": 10, "total_tokens": 18}
+{"user": "roxky", "model": "gpt-4o", "provider": "OpenaiAccount", "prompt_tokens": 8, "completion_tokens": 236, "total_tokens": 244}
+{"user": "roxky", "model": "flux", "provider": "PollinationsAI", "prompt_tokens": 8, "completion_tokens": 117, "total_tokens": 125}
+{"user": "roxky", "model": "flux-pro", "provider": "PollinationsAI", "prompt_tokens": 8, "completion_tokens": 123, "total_tokens": 131}
+{"user": "roxky", "model": "MiniMax-01", "provider": "HailuoAI", "prompt_tokens": 8, "completion_tokens": 32, "total_tokens": 40}
+{"user": "sheeee2222", "model": "qwen-2-72b", "provider": "HuggingSpace", "prompt_tokens": 15, "completion_tokens": 17, "total_tokens": 32}
+{"user": "sheeee2222", "model": "gpt-4o", "provider": "PollinationsAI", "completion_tokens": 10, "prompt_tokens": 174, "total_tokens": 184}
+{"user": "sheeee2222", "model": "gpt-4o", "provider": "PollinationsAI", "completion_tokens": 41, "prompt_tokens": 213, "total_tokens": 254}
+{"user": "sheeee2222", "model": "gpt-4o", "provider": "PollinationsAI", "completion_tokens": 566, "completion_tokens_details": {"accepted_prediction_tokens": 0, "audio_tokens": 0, "reasoning_tokens": 0, "rejected_prediction_tokens": 0}, "prompt_tokens": 3531, "prompt_tokens_details": {"audio_tokens": 0, "cached_tokens": 0}, "total_tokens": 4097}
+{"user": "roxky", "model": "auto", "provider": "OpenaiAccount", "prompt_tokens": 9, "completion_tokens": 11, "total_tokens": 20}
+{"user": "roxky", "model": "gpt-4o", "provider": "OpenaiAccount", "prompt_tokens": 9, "completion_tokens": 10, "total_tokens": 19}
+{"user": "roxky", "model": "gpt-4", "provider": "OpenaiAccount", "prompt_tokens": 9, "completion_tokens": 7, "total_tokens": 16}
+{"user": "roxky", "model": "gpt-4", "provider": "OpenaiAccount", "prompt_tokens": 9, "completion_tokens": 6, "total_tokens": 15}
+{"user": "Cocfff", "model": "llama-3.2-11b", "provider": "HuggingFaceAPI", "prompt_tokens": 20, "completion_tokens": 15, "total_tokens": 35}
+{"user": "Cocfff", "model": "llama-3.2-11b", "provider": "HuggingFaceAPI", "prompt_tokens": 46, "completion_tokens": 73, "total_tokens": 119}
+{"user": "Cocfff", "model": "llama-3.2-11b", "provider": "HuggingFaceAPI", "prompt_tokens": 126, "completion_tokens": 124, "total_tokens": 250}
+{"user": "Cocfff", "model": "llama-3.2-11b", "provider": "HuggingFaceAPI", "prompt_tokens": 246, "completion_tokens": 22, "total_tokens": 268}
+{"user": "Cocfff", "model": "llama-3.2-11b", "provider": "HuggingFaceAPI", "prompt_tokens": 274, "completion_tokens": 63, "total_tokens": 337}
+{"user": "Cocfff", "model": "llama-3.2-11b", "provider": "HuggingFaceAPI", "prompt_tokens": 342, "completion_tokens": 12, "total_tokens": 354}
+{"user": "Cocfff", "model": "llama-3.2-11b", "provider": "HuggingFaceAPI", "prompt_tokens": 359, "completion_tokens": 41, "total_tokens": 400}
+{"user": "Cocfff", "model": "llama-3.2-11b", "provider": "HuggingFaceAPI", "prompt_tokens": 420, "completion_tokens": 120, "total_tokens": 540}
+{"user": "Cocfff", "model": "llama-3.2-11b", "provider": "HuggingFaceAPI", "prompt_tokens": 420, "completion_tokens": 120, "total_tokens": 540}
+{"user": "cihairottoilcazzo", "model": "gpt-4", "provider": "OpenaiAccount", "prompt_tokens": 18, "completion_tokens": 261, "total_tokens": 279}
+{"user": "sanch1tx", "model": "deepseek-r1", "provider": "HuggingFace", "prompt_tokens": 8, "completion_tokens": 17, "total_tokens": 25}
+{"user": "nasrulam", "model": "deepseek-r1", "provider": "HuggingFaceAPI", "prompt_tokens": 19, "completion_tokens": 1225, "total_tokens": 1244}
+{"user": "nasrulam", "model": "auto", "provider": "OpenaiAccount", "prompt_tokens": 19, "completion_tokens": 69, "total_tokens": 88}
+{"user": "nasrulam", "model": "o1-mini", "provider": "OpenaiAccount", "prompt_tokens": 35, "completion_tokens": 1442, "total_tokens": 1477}
+{"user": "nasrulam", "model": "gpt-4o", "provider": "OpenaiAccount", "prompt_tokens": 35, "completion_tokens": 298, "total_tokens": 333}
+{"user": "nasrulam", "model": "flux-pro", "provider": "PollinationsAI", "prompt_tokens": 1574, "completion_tokens": 136, "total_tokens": 1710}
+{"user": "sfewewf232", "model": "auto", "provider": "OpenaiAccount", "prompt_tokens": 87, "completion_tokens": 160, "total_tokens": 247}
+{"user": "hashim1", "model": "gpt-4", "provider": "OpenaiAccount", "prompt_tokens": 13, "completion_tokens": 9, "total_tokens": 22}
+{"user": "hashim1", "model": "MiniMax-01", "provider": "HailuoAI", "prompt_tokens": 36, "completion_tokens": 20, "total_tokens": 56}
+{"user": "roxky", "model": "flux-dev", "provider": "HuggingSpace", "prompt_tokens": 30, "completion_tokens": 200, "total_tokens": 230}
+{"user": "roxky", "model": "flux-schnell", "provider": "HuggingFace", "prompt_tokens": 30, "completion_tokens": 80, "total_tokens": 110}
+{"user": "roxky", "model": "sonar-reasoning", "provider": "PerplexityLabs", "prompt_tokens": 9, "completion_tokens": 353, "total_tokens": 362}
+{"user": "roxky", "model": "gpt-4o", "provider": "OpenaiAccount", "prompt_tokens": 9, "completion_tokens": 10, "total_tokens": 19}
+{"user": "roxky", "model": "gpt-4o-mini", "provider": "OpenaiAccount", "prompt_tokens": 9, "completion_tokens": 7, "total_tokens": 16}
+{"user": "roxky", "model": "claude-3-haiku", "provider": "DDG", "prompt_tokens": 2447, "completion_tokens": 798, "total_tokens": 3245}

har_and_cookies/.usage/2025-01-31.jsonl

@@ -0,0 +1,126 @@
+{"user": "roxky", "model": "qvq-72b", "provider": "HuggingSpace", "prompt_tokens": 15, "completion_tokens": 10, "total_tokens": 25}
+{"user": "roxky", "model": "llama-3.2-11b", "provider": "HuggingFaceAPI", "prompt_tokens": 16, "completion_tokens": 1904, "total_tokens": 1920}
+{"user": "roxky", "model": "llama-3.2-11b", "provider": "HuggingFaceAPI", "prompt_tokens": 1490, "completion_tokens": 1736, "total_tokens": 3226}
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