Show only free providers by default

g4f/Provider/PollinationsAI.py

@@ -123,9 +123,6 @@ class PollinationsAI(AsyncGeneratorProvider, ProviderModelMixin):
             if model not in cls.image_models:
                 raise
 
-        if not cache and seed is None:
-            seed = random.randint(1000, 999999)
-
         if model in cls.image_models:
             async for chunk in cls._generate_image(
                 model=model,
@@ -134,6 +131,7 @@ class PollinationsAI(AsyncGeneratorProvider, ProviderModelMixin):
                 width=width,
                 height=height,
                 seed=seed,
+                cache=cache,
                 nologo=nologo,
                 private=private,
                 enhance=enhance,
@@ -165,11 +163,14 @@ class PollinationsAI(AsyncGeneratorProvider, ProviderModelMixin):
         width: int,
         height: int,
         seed: Optional[int],
+        cache: bool,
         nologo: bool,
         private: bool,
         enhance: bool,
         safe: bool
     ) -> AsyncResult:
+        if not cache and seed is None:
+            seed = random.randint(9999, 99999999)
         params = {
             "seed": str(seed) if seed is not None else None,
             "width": str(width),
@@ -207,6 +208,8 @@ class PollinationsAI(AsyncGeneratorProvider, ProviderModelMixin):
         seed: Optional[int],
         cache: bool
     ) -> AsyncResult:
+        if not cache and seed is None:
+            seed = random.randint(9999, 99999999)
         json_mode = False
         if response_format and response_format.get("type") == "json_object":
             json_mode = True

g4f/Provider/PollinationsImage.py

@@ -28,6 +28,7 @@ class PollinationsImage(PollinationsAI):
         width: int = 1024,
         height: int = 1024,
         seed: Optional[int] = None,
+        cache: bool = False,
         nologo: bool = True,
         private: bool = False,
         enhance: bool = False,
@@ -41,6 +42,7 @@ class PollinationsImage(PollinationsAI):
             width=width,
             height=height,
             seed=seed,
+            cache=cache,
             nologo=nologo,
             private=private,
             enhance=enhance,

g4f/Provider/hf/HuggingChat.py

@@ -8,7 +8,8 @@ import base64
 from typing import AsyncIterator
 
 try:
-    from curl_cffi.requests import Session, CurlMime
+    from curl_cffi.requests import Session
+    from curl_cffi import CurlMime
     has_curl_cffi = True
 except ImportError:
     has_curl_cffi = False

g4f/Provider/hf/HuggingFaceAPI.py

@@ -4,6 +4,7 @@ from ...providers.types import Messages
 from ...typing import ImagesType
 from ...requests import StreamSession, raise_for_status
 from ...errors import ModelNotSupportedError
+from ...providers.helper import get_last_user_message
 from ..template.OpenaiTemplate import OpenaiTemplate
 from .models import model_aliases, vision_models, default_vision_model
 from .HuggingChat import HuggingChat
@@ -22,7 +23,7 @@ class HuggingFaceAPI(OpenaiTemplate):
     vision_models = vision_models
     model_aliases = model_aliases
 
-    pipeline_tag: dict[str, str] = {}
+    pipeline_tags: dict[str, str] = {}
 
     @classmethod
     def get_models(cls, **kwargs):
@@ -36,8 +37,8 @@ class HuggingFaceAPI(OpenaiTemplate):
 
     @classmethod
     async def get_pipline_tag(cls, model: str, api_key: str = None):
-        if model in cls.pipeline_tag:
+        if model in cls.pipeline_tags:
-            return cls.pipeline_tag[model]
+            return cls.pipeline_tags[model]
         async with StreamSession(
             timeout=30,
             headers=cls.get_headers(False, api_key),
@@ -45,8 +46,8 @@ class HuggingFaceAPI(OpenaiTemplate):
             async with session.get(f"https://huggingface.co/api/models/{model}") as response:
                 await raise_for_status(response)
                 model_data = await response.json()
-                cls.pipeline_tag[model] = model_data.get("pipeline_tag")
+                cls.pipeline_tags[model] = model_data.get("pipeline_tag")
-        return cls.pipeline_tag[model]
+        return cls.pipeline_tags[model]
 
     @classmethod
     async def create_async_generator(
@@ -73,10 +74,11 @@ class HuggingFaceAPI(OpenaiTemplate):
             if len(messages) > 6:
                 messages = messages[:3] + messages[-3:]
             if calculate_lenght(messages) > max_inputs_lenght:
+                last_user_message = [{"role": "user", "content": get_last_user_message(messages)}]
                 if len(messages) > 2:
-                    messages = [m for m in messages if m["role"] == "system"] + messages[-1:]
+                    messages = [m for m in messages if m["role"] == "system"] + last_user_message
                 if len(messages) > 1 and calculate_lenght(messages) > max_inputs_lenght:
-                    messages = [messages[-1]]
+                    messages = last_user_message
             debug.log(f"Messages trimmed from: {start} to: {calculate_lenght(messages)}")
         async for chunk in super().create_async_generator(model, messages, api_base=api_base, api_key=api_key, max_tokens=max_tokens, images=images, **kwargs):
             yield chunk

g4f/Provider/hf/HuggingFaceInference.py

@@ -7,7 +7,7 @@ import requests
 
 from ...typing import AsyncResult, Messages
 from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin, format_prompt
-from ...errors import ModelNotFoundError, ModelNotSupportedError, ResponseError
+from ...errors import ModelNotSupportedError, ResponseError
 from ...requests import StreamSession, raise_for_status
 from ...providers.response import FinishReason, ImageResponse
 from ..helper import format_image_prompt, get_last_user_message
@@ -24,6 +24,8 @@ class HuggingFaceInference(AsyncGeneratorProvider, ProviderModelMixin):
     model_aliases = model_aliases
     image_models = image_models
 
+    model_data: dict[str, dict] = {}
+
     @classmethod
     def get_models(cls) -> list[str]:
         if not cls.models:
@@ -43,6 +45,17 @@ class HuggingFaceInference(AsyncGeneratorProvider, ProviderModelMixin):
             cls.models = models
         return cls.models
 
+    @classmethod
+    async def get_model_data(cls, session: StreamSession, model: str) -> str:
+        if model in cls.model_data:
+            return cls.model_data[model]
+        async with session.get(f"https://huggingface.co/api/models/{model}") as response:
+            if response.status == 404:
+                raise ModelNotSupportedError(f"Model is not supported: {model} in: {cls.__name__}")
+            await raise_for_status(response)
+            cls.model_data[model] = await response.json()
+        return cls.model_data[model]
+
     @classmethod
     async def create_async_generator(
         cls,
@@ -96,11 +109,7 @@ class HuggingFaceInference(AsyncGeneratorProvider, ProviderModelMixin):
             timeout=600
         ) as session:
             if payload is None:
-                async with session.get(f"https://huggingface.co/api/models/{model}") as response:
+                model_data = await cls.get_model_data(session, model)
-                    if response.status == 404:
-                        raise ModelNotSupportedError(f"Model is not supported: {model} in: {cls.__name__}")
-                    await raise_for_status(response)
-                    model_data = await response.json()
                 pipeline_tag = model_data.get("pipeline_tag")
                 if pipeline_tag == "text-to-image":
                     stream = False
@@ -117,7 +126,7 @@ class HuggingFaceInference(AsyncGeneratorProvider, ProviderModelMixin):
                         if len(messages) > 6:
                             messages = messages[:3] + messages[-3:]
                         else:
-                                messages = [m for m in messages if m["role"] == "system"] + [get_last_user_message(messages)]
+                            messages = [m for m in messages if m["role"] == "system"] + [{"role": "user", "content": get_last_user_message(messages)}]
                         inputs = get_inputs(messages, model_data, model_type, do_continue)
                         debug.log(f"New len: {len(inputs)}")
                     if model_type == "gpt2" and max_tokens >= 1024:
@@ -130,7 +139,7 @@ class HuggingFaceInference(AsyncGeneratorProvider, ProviderModelMixin):
 
             async with session.post(f"{api_base.rstrip('/')}/models/{model}", json=payload) as response:
                 if response.status == 404:
-                    raise ModelNotFoundError(f"Model is not supported: {model}")
+                    raise ModelNotSupportedError(f"Model is not supported: {model}")
                 await raise_for_status(response)
                 if stream:
                     first = True

g4f/Provider/hf/__init__.py

@@ -36,7 +36,7 @@ class HuggingFace(AsyncGeneratorProvider, ProviderModelMixin):
         messages: Messages,
         **kwargs
     ) -> AsyncResult:
-        if "api_key" not in kwargs and "images" not in kwargs and random.random() >= 0.5:
+        if "images" not in kwargs and "deepseek" in model or random.random() >= 0.5:
             try:
                 is_started = False
                 async for chunk in HuggingFaceInference.create_async_generator(model, messages, **kwargs):

g4f/Provider/needs_auth/DeepSeekAPI.py

@@ -13,7 +13,6 @@ from ...errors import MissingAuthError
 from ...requests import get_args_from_nodriver, get_nodriver
 from ...providers.response import AuthResult, RequestLogin, Reasoning, JsonConversation, FinishReason
 from ...typing import AsyncResult, Messages
-from ... import debug
 try:
     from curl_cffi import requests
     from dsk.api import DeepSeekAPI, AuthenticationError, DeepSeekPOW

g4f/Provider/needs_auth/OpenaiAccount.py

@@ -5,3 +5,4 @@ from .OpenaiChat import OpenaiChat
 class OpenaiAccount(OpenaiChat):
     needs_auth = True
     parent = "OpenaiChat"
+    use_nodriver = False # Show (Auth) in the model name

g4f/Provider/template/OpenaiTemplate.py

@@ -65,7 +65,7 @@ class OpenaiTemplate(AsyncGeneratorProvider, ProviderModelMixin, RaiseErrorMixin
         prompt: str = None,
         headers: dict = None,
         impersonate: str = None,
-        extra_parameters: list[str] = ["tools", "parallel_tool_calls", "", "reasoning_effort", "logit_bias"],
+        extra_parameters: list[str] = ["tools", "parallel_tool_calls", "tool_choice", "reasoning_effort", "logit_bias"],
         extra_data: dict = {},
         **kwargs
     ) -> AsyncResult:

g4f/client/__init__.py

@@ -365,7 +365,7 @@ class Images:
                         break
                 except Exception as e:
                     error = e
-                    debug.error(e, name=f"{provider.__name__} {type(e).__name__}")
+                    debug.error(f"{provider.__name__} {type(e).__name__}: {e}")
         else:
             response = await self._generate_image_response(provider_handler, provider_name, model, prompt, **kwargs)
 
@@ -460,7 +460,7 @@ class Images:
                         break
                 except Exception as e:
                     error = e
-                    debug.error(e, name=f"{provider.__name__} {type(e).__name__}")
+                    debug.error(f"{provider.__name__} {type(e).__name__}: {e}")
         else:
             response = await self._generate_image_response(provider_handler, provider_name, model, prompt, **kwargs)
 

g4f/gui/client/static/js/chat.v1.js

@@ -1932,7 +1932,7 @@ const load_provider_option = (input, provider_name) => {
         providerSelect.querySelectorAll(`option[data-parent="${provider_name}"]`).forEach(
             (el) => el.removeAttribute("disabled")
         );
-        settings.querySelector(`.field:has(#${provider_name}-api_key)`)?.classList.remove("hidden");
+        //settings.querySelector(`.field:has(#${provider_name}-api_key)`)?.classList.remove("hidden");
     } else {
         modelSelect.querySelectorAll(`option[data-providers*="${provider_name}"]`).forEach(
             (el) => {
@@ -1947,7 +1947,7 @@ const load_provider_option = (input, provider_name) => {
         providerSelect.querySelectorAll(`option[data-parent="${provider_name}"]`).forEach(
             (el) => el.setAttribute("disabled", "disabled")
         );
-        settings.querySelector(`.field:has(#${provider_name}-api_key)`)?.classList.add("hidden");
+        //settings.querySelector(`.field:has(#${provider_name}-api_key)`)?.classList.add("hidden");
     }
 };
 
@@ -2039,13 +2039,13 @@ async function on_api() {
 
             if (provider.parent) {
                 if (!login_urls[provider.parent]) {
-                    login_urls[provider.parent] = [provider.label, provider.login_url, [provider.name]];
+                    login_urls[provider.parent] = [provider.label, provider.login_url, [provider.name], provider.auth];
                 } else {
                     login_urls[provider.parent][2].push(provider.name);
                 }
             } else if (provider.login_url) {
                 if (!login_urls[provider.name]) {
-                    login_urls[provider.name] = [provider.label, provider.login_url, []];
+                    login_urls[provider.name] = [provider.label, provider.login_url, [], provider.auth];
                 } else {
                     login_urls[provider.name][0] = provider.label;
                     login_urls[provider.name][1] = provider.login_url;
@@ -2068,9 +2068,10 @@ async function on_api() {
             if (!provider.parent) {
                 let option = document.createElement("div");
                 option.classList.add("provider-item");
+                let api_key = appStorage.getItem(`${provider.name}-api_key`);
                 option.innerHTML = `
                     <span class="label">Enable ${provider.label}</span>
-                    <input id="Provider${provider.name}" type="checkbox" name="Provider${provider.name}" value="${provider.name}" class="provider" checked="">
+                    <input id="Provider${provider.name}" type="checkbox" name="Provider${provider.name}" value="${provider.name}" class="provider" ${'checked="checked"' ? !provider.auth || api_key : ''}/>
                     <label for="Provider${provider.name}" class="toogle" title="Remove provider from dropdown"></label>
                 `;
                 option.querySelector("input").addEventListener("change", (event) => load_provider_option(event.target, provider.name));
@@ -2102,7 +2103,7 @@ async function on_api() {
     `;
     settings.querySelector(".paper").appendChild(providersListContainer);
 
-    for (let [name, [label, login_url, childs]] of Object.entries(login_urls)) {
+    for (let [name, [label, login_url, childs, auth]] of Object.entries(login_urls)) {
         if (!login_url && !is_demo) {
             continue;
         }
@@ -2113,6 +2114,13 @@ async function on_api() {
             <label for="${name}-api_key" class="label" title="">${label}:</label>
             <input type="text" id="${name}-api_key" name="${name}[api_key]" class="${childs}" placeholder="api_key" autocomplete="off"/>
         ` + (login_url ? `<a href="${login_url}" target="_blank" title="Login to ${label}">Get API key</a>` : "");
+        if (auth) {
+            providerBox.querySelector("input").addEventListener("input", (event) => {
+                const input = document.getElementById(`Provider${name}`);
+                input.checked = !!event.target.value;
+                load_provider_option(input, name);
+            });
+        }
         providersListContainer.querySelector(".collapsible-content").appendChild(providerBox);
     }
 

g4f/gui/server/api.py

@@ -143,7 +143,7 @@ class Api:
         def decorated_log(text: str, file = None):
             debug.logs.append(text)
             if debug.logging:
-                debug.log_handler(text, file)
+                debug.log_handler(text, file=file)
         debug.log = decorated_log
         proxy = os.environ.get("G4F_PROXY")
         provider = kwargs.get("provider")
@@ -187,7 +187,7 @@ class Api:
                             yield self._format_json("conversation_id", conversation_id)
                 elif isinstance(chunk, Exception):
                     logger.exception(chunk)
-                    debug.error(e)
+                    debug.error(chunk)
                     yield self._format_json('message', get_error_message(chunk), error=type(chunk).__name__)
                 elif isinstance(chunk, PreviewResponse):
                     yield self._format_json("preview", chunk.to_string())

g4f/image/copy_images.py

@@ -123,7 +123,7 @@ async def copy_images(
                 return f"/images/{url_filename}{'?url=' + quote(image) if add_url and not image.startswith('data:') else ''}"
 
             except (ClientError, IOError, OSError) as e:
-                debug.error(f"Image processing failed: {type(e).__name__}: {e}")
+                debug.error(f"Image copying failed: {type(e).__name__}: {e}")
                 if target_path and os.path.exists(target_path):
                     os.unlink(target_path)
                 return get_source_url(image, image)

g4f/providers/retry_provider.py

@@ -105,7 +105,7 @@ class IterListProvider(BaseRetryProvider):
                     return
             except Exception as e:
                 exceptions[provider.__name__] = e
-                debug.error(name=f"{provider.__name__} {type(e).__name__}: {e}")
+                debug.error(f"{provider.__name__} {type(e).__name__}: {e}")
                 if started:
                     raise e
                 yield e