Update model list in OpenaiChat (o3-mini, o3-mini-high)

Add Reasoning to OpenaiChat provider
Check for pipeline_tag in HuggingChat providers
Add image preview in PollinationsAI
Add input of custom Model in GUI

g4f/Provider/Blackbox.py

@@ -308,14 +308,7 @@ class Blackbox(AsyncGeneratorProvider, ProviderModelMixin):
                         image_url = image_url_match.group(1)
                         yield ImageResponse(image_url, format_image_prompt(messages, prompt))
                 else:
-                    if "<think>" in text_to_yield and "</think>" in text_to_yield:
-                        parts = text_to_yield.split('<think>', 1)
-                        yield parts[0]
-                        reasoning_parts = parts[1].split('</think>', 1)
-                        yield Reasoning(f"<think>{reasoning_parts[0]}</think>")
-                        yield reasoning_parts[1]
-                        full_response = text_to_yield
-                    elif "Generated by BLACKBOX.AI" in text_to_yield:
+                    if "Generated by BLACKBOX.AI" in text_to_yield:
                         conversation.validated_value = await cls.fetch_validated(force_refresh=True)
                         if conversation.validated_value:
                             data["validated"] = conversation.validated_value

g4f/Provider/PollinationsAI.py

@@ -13,7 +13,7 @@ from ..typing import AsyncResult, Messages, ImagesType
 from ..image import to_data_uri
 from ..requests.raise_for_status import raise_for_status
 from ..requests.aiohttp import get_connector
-from ..providers.response import ImageResponse, FinishReason, Usage
+from ..providers.response import ImageResponse, ImagePreview, FinishReason, Usage
 
 DEFAULT_HEADERS = {
     'Accept': '*/*',
@@ -125,7 +125,7 @@ class PollinationsAI(AsyncGeneratorProvider, ProviderModelMixin):
         # Check if models
         # Image generation
         if model in cls.image_models:
-           yield await cls._generate_image(
+           async for chunk in cls._generate_image(
                 model=model,
                 prompt=format_image_prompt(messages, prompt),
                 proxy=proxy,
@@ -136,7 +136,8 @@ class PollinationsAI(AsyncGeneratorProvider, ProviderModelMixin):
                 private=private,
                 enhance=enhance,
                 safe=safe
-            )
+            ):
+                yield chunk
         else:
             # Text generation
             async for result in cls._generate_text(
@@ -167,7 +168,7 @@ class PollinationsAI(AsyncGeneratorProvider, ProviderModelMixin):
         private: bool,
         enhance: bool,
         safe: bool
-    ) -> ImageResponse:
+    ) -> AsyncResult:
         params = {
             "seed": seed,
             "width": width,
@@ -178,11 +179,16 @@ class PollinationsAI(AsyncGeneratorProvider, ProviderModelMixin):
             "enhance": enhance,
             "safe": safe
         }
-        params = {k: json.dumps(v) if isinstance(v, bool) else v for k, v in params.items() if v is not None}
+        params = {k: json.dumps(v) if isinstance(v, bool) else str(v) for k, v in params.items() if v is not None}
+        params = "&".join( "%s=%s" % (key, quote_plus(params[key])) 
+            for key in params.keys())
+        url = f"{cls.image_api_endpoint}prompt/{quote_plus(prompt)}?{params}"
+        yield ImagePreview(url, prompt)
         async with ClientSession(headers=DEFAULT_HEADERS, connector=get_connector(proxy=proxy)) as session:
-            async with session.head(f"{cls.image_api_endpoint}prompt/{quote_plus(prompt)}", params=params) as response:
-                await raise_for_status(response)
-                return ImageResponse(str(response.url), prompt)
+            async with session.head(url) as response:
+                if response.status != 500: # Server is busy
+                    await raise_for_status(response)
+                yield ImageResponse(str(response.url), prompt)
 
     @classmethod
     async def _generate_text(

g4f/Provider/hf/HuggingFaceAPI.py

@@ -1,8 +1,11 @@
 from __future__ import annotations
 
+from ...providers.types import Messages
+from ...typing import ImagesType
+from ...requests import StreamSession, raise_for_status
+from ...errors import ModelNotSupportedError
 from ..template.OpenaiTemplate import OpenaiTemplate
 from .models import model_aliases
-from ...providers.types import Messages
 from .HuggingChat import HuggingChat
 from ... import debug
 
@@ -37,6 +40,10 @@ class HuggingFaceAPI(OpenaiTemplate):
         api_base: str = None,
         max_tokens: int = 2048,
         max_inputs_lenght: int = 10000,
+        impersonate: str = None,
+        proxy: str = None,
+        timeout: int = 300,
+        images: ImagesType = None,
         **kwargs
     ):
         if api_base is None:
@@ -44,6 +51,20 @@ class HuggingFaceAPI(OpenaiTemplate):
             if model in cls.model_aliases:
                 model_name = cls.model_aliases[model]
             api_base = f"https://api-inference.huggingface.co/models/{model_name}/v1"
+        if images is not None:
+            async with StreamSession(
+                proxy=proxy,
+                timeout=timeout,
+                impersonate=impersonate,
+            ) as session:
+                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)
+                    model_data = await response.json()
+                    pipeline_tag = model_data.get("pipeline_tag")
+                    if pipeline_tag != "image-text-to-text":
+                        raise ModelNotSupportedError(f"Model is not supported: {model} in: {cls.__name__} pipeline_tag={pipeline_tag}")
         start = calculate_lenght(messages)
         if start > max_inputs_lenght:
             if len(messages) > 6:
@@ -54,7 +75,7 @@ class HuggingFaceAPI(OpenaiTemplate):
                 if len(messages) > 1 and calculate_lenght(messages) > max_inputs_lenght:
                     messages = [messages[-1]]
             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, max_tokens=max_tokens, **kwargs):
+        async for chunk in super().create_async_generator(model, messages, api_base=api_base, max_tokens=max_tokens, images=images, **kwargs):
             yield chunk
 
 def calculate_lenght(messages: Messages) -> int:

g4f/Provider/hf/HuggingFaceInference.py

@@ -78,18 +78,13 @@ class HuggingFaceInference(AsyncGeneratorProvider, ProviderModelMixin):
         if api_key is not None:
             headers["Authorization"] = f"Bearer {api_key}"
         payload = None
-        if cls.get_models() and model in cls.image_models:
-            stream = False
-            prompt = format_image_prompt(messages, prompt)
-            payload = {"inputs": prompt, "parameters": {"seed": random.randint(0, 2**32), **extra_data}}
-        else:
-            params = {
-                "return_full_text": False,
-                "max_new_tokens": max_tokens,
-                "temperature": temperature,
-                **extra_data
-            }
-            do_continue = action == "continue"
+        params = {
+            "return_full_text": False,
+            "max_new_tokens": max_tokens,
+            "temperature": temperature,
+            **extra_data
+        }
+        do_continue = action == "continue"
         async with StreamSession(
             headers=headers,
             proxy=proxy,
@@ -101,22 +96,30 @@ class HuggingFaceInference(AsyncGeneratorProvider, ProviderModelMixin):
                         raise ModelNotSupportedError(f"Model is not supported: {model} in: {cls.__name__}")
                     await raise_for_status(response)
                     model_data = await response.json()
-                    model_type = None
-                    if "config" in model_data and "model_type" in model_data["config"]:
-                        model_type = model_data["config"]["model_type"]
-                    debug.log(f"Model type: {model_type}")
-                    inputs = get_inputs(messages, model_data, model_type, do_continue)
-                    debug.log(f"Inputs len: {len(inputs)}")
-                    if len(inputs) > 4096:
-                        if len(messages) > 6:
-                            messages = messages[:3] + messages[-3:]
-                        else:
-                            messages = [m for m in messages if m["role"] == "system"] + [messages[-1]]
+                    pipeline_tag = model_data.get("pipeline_tag")
+                    if pipeline_tag == "text-to-image":
+                        stream = False
+                        inputs = format_image_prompt(messages, prompt)
+                        payload = {"inputs": inputs, "parameters": {"seed": random.randint(0, 2**32), **extra_data}}
+                    elif pipeline_tag in ("text-generation", "image-text-to-text"):
+                        model_type = None
+                        if "config" in model_data and "model_type" in model_data["config"]:
+                            model_type = model_data["config"]["model_type"]
+                        debug.log(f"Model type: {model_type}")
                         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:
-                        params["max_new_tokens"] = 512
-                payload = {"inputs": inputs, "parameters": params, "stream": stream}
+                        debug.log(f"Inputs len: {len(inputs)}")
+                        if len(inputs) > 4096:
+                            if len(messages) > 6:
+                                messages = messages[:3] + messages[-3:]
+                            else:
+                                messages = [m for m in messages if m["role"] == "system"] + [messages[-1]]
+                            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:
+                            params["max_new_tokens"] = 512
+                        payload = {"inputs": inputs, "parameters": params, "stream": stream}
+                    else:
+                        raise ModelNotSupportedError(f"Model is not supported: {model} in: {cls.__name__} pipeline_tag: {pipeline_tag}")
 
             async with session.post(f"{api_base.rstrip('/')}/models/{model}", json=payload) as response:
                 if response.status == 404:

g4f/Provider/needs_auth/OpenaiChat.py

@@ -25,8 +25,9 @@ from ...requests import get_nodriver
 from ...image import ImageResponse, ImageRequest, to_image, to_bytes, is_accepted_format
 from ...errors import MissingAuthError, NoValidHarFileError
 from ...providers.response import JsonConversation, FinishReason, SynthesizeData, AuthResult
-from ...providers.response import Sources, TitleGeneration, RequestLogin, Parameters
+from ...providers.response import Sources, TitleGeneration, RequestLogin, Parameters, Reasoning
 from ..helper import format_cookies
+from ..openai.models import default_model, default_image_model, models, image_models, text_models
 from ..openai.har_file import get_request_config
 from ..openai.har_file import RequestConfig, arkReq, arkose_url, start_url, conversation_url, backend_url, backend_anon_url
 from ..openai.proofofwork import generate_proof_token
@@ -95,12 +96,11 @@ class OpenaiChat(AsyncAuthedProvider, ProviderModelMixin):
     supports_gpt_4 = True
     supports_message_history = True
     supports_system_message = True
-    default_model = "auto"
-    default_image_model = "dall-e-3"
-    image_models = [default_image_model]
-    text_models = [default_model, "gpt-4", "gpt-4o", "gpt-4o-mini", "o1", "o1-preview", "o1-mini"]
+    default_model = default_model
+    default_image_model = default_image_model
+    image_models = image_models
     vision_models = text_models
-    models = text_models + image_models
+    models = models
     synthesize_content_type = "audio/mpeg"
 
     _api_key: str = None
@@ -368,9 +368,11 @@ class OpenaiChat(AsyncAuthedProvider, ProviderModelMixin):
                     )
                 [debug.log(text) for text in (
                     #f"Arkose: {'False' if not need_arkose else auth_result.arkose_token[:12]+'...'}",
-                    f"Proofofwork: {'False' if proofofwork is None else proofofwork[:12]+'...'}",
-                    f"AccessToken: {'False' if cls._api_key is None else cls._api_key[:12]+'...'}",
+                    #f"Proofofwork: {'False' if proofofwork is None else proofofwork[:12]+'...'}",
+                    #f"AccessToken: {'False' if cls._api_key is None else cls._api_key[:12]+'...'}",
                 )]
+                if action == "continue" and conversation.message_id is None:
+                    action = "next"
                 data = {
                     "action": action,
                     "parent_message_id": conversation.message_id,
@@ -497,7 +499,7 @@ class OpenaiChat(AsyncAuthedProvider, ProviderModelMixin):
             v = line.get("v")
             if isinstance(v, str) and fields.is_recipient:
                 if "p" not in line or line.get("p") == "/message/content/parts/0":
-                    yield v
+                    yield Reasoning(token=v) if fields.is_thinking else v
             elif isinstance(v, list):
                 for m in v:
                     if m.get("p") == "/message/content/parts/0" and fields.is_recipient:
@@ -508,6 +510,9 @@ class OpenaiChat(AsyncAuthedProvider, ProviderModelMixin):
                                 sources.add_source(link)
                     elif re.match(r"^/message/metadata/content_references/\d+$", m.get("p")):
                         sources.add_source(m.get("v"))
+                    elif m.get("p") == "/message/metadata/finished_text":
+                        fields.is_thinking = False
+                        yield Reasoning(status=m.get("v"))
                     elif m.get("p") == "/message/metadata":
                         fields.finish_reason = m.get("v", {}).get("finish_details", {}).get("type")
                         break
@@ -519,6 +524,9 @@ class OpenaiChat(AsyncAuthedProvider, ProviderModelMixin):
                 fields.is_recipient = m.get("recipient", "all") == "all"
                 if fields.is_recipient:
                     c = m.get("content", {})
+                    if c.get("content_type") == "text" and m.get("author", {}).get("role") == "tool" and "initial_text" in m.get("metadata", {}):
+                        fields.is_thinking = True
+                        yield Reasoning(status=c.get("metadata", {}).get("initial_text"))
                     if c.get("content_type") == "multimodal_text":
                         generated_images = []
                         for element in c.get("parts"):
@@ -697,13 +705,14 @@ class Conversation(JsonConversation):
     """
     Class to encapsulate response fields.
     """
-    def __init__(self, conversation_id: str = None, message_id: str = None, user_id: str = None, finish_reason: str = None, parent_message_id: str = None):
+    def __init__(self, conversation_id: str = None, message_id: str = None, user_id: str = None, finish_reason: str = None, parent_message_id: str = None, is_thinking: bool = False):
         self.conversation_id = conversation_id
         self.message_id = message_id
         self.finish_reason = finish_reason
         self.is_recipient = False
         self.parent_message_id = message_id if parent_message_id is None else parent_message_id
         self.user_id = user_id
+        self.is_thinking = is_thinking
 
 def get_cookies(
     urls: Optional[Iterator[str]] = None

g4f/Provider/openai/models.py

@@ -0,0 +1,6 @@
+default_model = "auto"
+default_image_model = "dall-e-3"
+image_models = [default_image_model]
+text_models = [default_model, "gpt-4", "gpt-4o", "gpt-4o-mini", "o1", "o1-preview", "o1-mini", "o3-mini", "o3-mini-high"]
+vision_models = text_models
+models = text_models + image_models

g4f/api/__init__.py

@@ -10,6 +10,7 @@ from email.utils import formatdate
 import os.path
 import hashlib
 import asyncio
+from urllib.parse import quote_plus
 from fastapi import FastAPI, Response, Request, UploadFile, Depends
 from fastapi.middleware.wsgi import WSGIMiddleware
 from fastapi.responses import StreamingResponse, RedirectResponse, HTMLResponse, JSONResponse
@@ -176,11 +177,11 @@ class Api:
                         return ErrorResponse.from_message("G4F API key required", HTTP_401_UNAUTHORIZED)
                     if not secrets.compare_digest(AppConfig.g4f_api_key, user_g4f_api_key):
                         return ErrorResponse.from_message("Invalid G4F API key", HTTP_403_FORBIDDEN)
-                elif not AppConfig.demo:
-                    if user_g4f_api_key is not None and path.startswith("/images/"):
+                elif not AppConfig.demo and not path.startswith("/images/"):
+                    if user_g4f_api_key is not None:
                         if not secrets.compare_digest(AppConfig.g4f_api_key, user_g4f_api_key):
                             return ErrorResponse.from_message("Invalid G4F API key", HTTP_403_FORBIDDEN)
-                    elif path.startswith("/backend-api/") or path.startswith("/images/") or path.startswith("/chat/") and path != "/chat/":
+                    elif path.startswith("/backend-api/") or path.startswith("/chat/") and path != "/chat/":
                         try:
                             username = await self.get_username(request)
                         except HTTPException as e:
@@ -551,8 +552,8 @@ class Api:
             HTTP_404_NOT_FOUND: {}
         })
         async def get_image(filename, request: Request):
-            target = os.path.join(images_dir, filename)
-            ext = os.path.splitext(filename)[1]
+            target = os.path.join(images_dir, quote_plus(filename))
+            ext = os.path.splitext(filename)[1][1:]
             stat_result = SimpleNamespace()
             stat_result.st_size = 0
             if os.path.isfile(target):
@@ -560,10 +561,12 @@ class Api:
             stat_result.st_mtime = int(f"{filename.split('_')[0]}") if filename.startswith("1") else 0
             headers = {
                 "cache-control": "public, max-age=31536000",
-                "content-type": f"image/{ext.replace('jpg', 'jpeg')[1:] or 'jpeg'}",
-                "content-length": str(stat_result.st_size),
+                "content-type": f"image/{ext.replace('jpg', 'jpeg') or 'jpeg'}",
                 "last-modified": formatdate(stat_result.st_mtime, usegmt=True),
                 "etag": f'"{hashlib.md5(filename.encode()).hexdigest()}"',
+                **({
+                    "content-length": str(stat_result.st_size),
+                } if stat_result.st_size else {})
             }
             response = FileResponse(
                 target,
@@ -583,10 +586,14 @@ class Api:
                     source_url = source_url[1]
                     source_url = source_url.replace("%2F", "/").replace("%3A", ":").replace("%3F", "?").replace("%3D", "=")
                     if source_url.startswith("https://"):
-                        await copy_images(
-                            [source_url],
-                            target=target)
-                        debug.log(f"Image copied from {source_url}")
+                        try:
+                            await copy_images(
+                                [source_url],
+                                target=target)
+                            debug.log(f"Image copied from {source_url}")
+                        except Exception as e:
+                            debug.log(f"{type(e).__name__}: Download failed:  {source_url}\n{e}")
+                            return RedirectResponse(url=source_url)
             if not os.path.isfile(target):
                 return ErrorResponse.from_message("File not found", HTTP_404_NOT_FOUND)
             async def stream():

g4f/gui/client/index.html

@@ -47,12 +47,13 @@
             gallery: '#messages',
             children: 'a:has(img)',
             secondaryZoomLevel: 2,
+            allowPanToNext: true,
             pswpModule: () => import('https://cdn.jsdelivr.net/npm/photoswipe'),
         });
         lightbox.addFilter('itemData', (itemData, index) => {
             const img = itemData.element.querySelector('img');
-            itemData.width = img.naturalWidth;
-            itemData.height = img.naturalHeight;
+            itemData.width = img.naturalWidth || 1024;
+            itemData.height = img.naturalHeight || 1024;
             return itemData;
         });
         lightbox.on('uiRegister', function() {
@@ -66,7 +67,13 @@
                     lightbox.pswp.on('change', () => {
                         const currSlideElement = lightbox.pswp.currSlide.data.element;
                         if (currSlideElement) {
-                            el.innerText = currSlideElement.querySelector('img').getAttribute('alt');
+                            const img = currSlideElement.querySelector('img');
+                            el.innerText = img.getAttribute('alt');
+                            const download = document.createElement("a");
+                            download.setAttribute("href", img.getAttribute('src'));
+                            download.setAttribute("download", `${img.getAttribute('alt')}${lightbox.pswp.currSlide.index}.jpg`);
+                            download.innerHTML = '<i class="fa-solid fa-download"></i>';
+                            el.appendChild(download);
                         }
                     });
                 }
@@ -157,8 +164,8 @@
                 <label for="report_error" class="toogle" title=""></label>
             </div>
             <div class="field box">
-                <label for="systemPrompt" class="label" title="">System prompt</label>
-                <textarea id="systemPrompt" placeholder="You are a helpful assistant."></textarea>
+                <label for="systemPrompt" class="label">System prompt</label>
+                <textarea id="systemPrompt" placeholder="You are a helpful assistant." data-value="If you need to generate images, you can use the following format: ![keywords](/generate/filename.jpg). This will enable the use of an image generation tool."></textarea>
             </div>
             <div class="field box">
                 <label for="message-input-height" class="label" title="">Input max. height</label>
@@ -269,6 +276,7 @@
                     <div id="send-button">
                         <i class="fa-solid fa-square-plus"></i>
                         <i class="fa-regular fa-paper-plane"></i>
+                        <a href="" id="download" class="hidden"></a>
                     </div>
                 </div>
             </div>
@@ -293,7 +301,8 @@
                      <option value="dall-e-3">dall-e-3 (Image Generation)</option>
                      <option disabled="disabled">----</option>
                   </select>
-                  <select name="model2" id="model2" class="hidden"></select>
+                  <select name="model2" id="model2" class="hidden model"></select>
+                  <input type="text" id="model3" value="" class="hidden model" placeholder="Model:"/>
                </div>
                <div class="field">
                   <select name="provider" id="provider">
@@ -303,6 +312,7 @@
                      <option value="Gemini">Google Gemini</option>
                      <option value="DDG">DuckDuckGo AI Chat</option>
                      <option value="Blackbox">Blackbox AI</option>
+                     <option value="Custom Model">Custom Model</option>
                      <option disabled="disabled">----</option>
                     </select>
                 </div>

g4f/gui/client/static/css/style.css

@@ -799,7 +799,7 @@ form input:checked+label:after {
     color: var(--colour-3);
 }
 
-select {
+select, input.model {
     border-radius: 8px;
     backdrop-filter: blur(20px);
     cursor: pointer;
@@ -871,6 +871,7 @@ button.regenerate_button, button.continue_button, button.options_button {
 }
 
 select:hover,
+input.model:hover
 .buttons button:hover,
 .stop_generating button:hover,
 .toolbar .regenerate button:hover,
@@ -948,7 +949,7 @@ select:hover,
 }
 
 @media only screen and (min-width: 40em) {
-    select {
+    select, input.model {
         width: 200px;
     }
     .field {
@@ -1446,7 +1447,7 @@ form .field.saved .fa-xmark {
     max-height: 200px;
 }
 
-.hidden {
+.hidden, input.hidden {
     display: none;
 }
 

g4f/gui/client/static/img/site.webmanifest

@@ -17,7 +17,7 @@
     "background_color": "#ffffff",
     "display": "standalone",
     "share_target": {
-        "action": "/chat/",
+        "action": "/chat/share",
         "method": "GET",
         "enctype": "application/x-www-form-urlencoded",
         "params": {

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

@@ -15,6 +15,7 @@ const inputCount        = document.getElementById("input-count").querySelector("
 const providerSelect    = document.getElementById("provider");
 const modelSelect       = document.getElementById("model");
 const modelProvider     = document.getElementById("model2");
+const custom_model      = document.getElementById("model3");
 const chatPrompt        = document.getElementById("chatPrompt");
 const settings          = document.querySelector(".settings");
 const chat              = document.querySelector(".conversation");
@@ -78,18 +79,28 @@ function render_reasoning(reasoning, final = false) {
     </div>` : "";
     return `<div class="reasoning_body">
         <div class="reasoning_title">
-           <strong>Reasoning <i class="fa-solid fa-brain"></i>:</strong> ${escapeHtml(reasoning.status)}
+           <strong>Reasoning <i class="brain">🧠</i>:</strong> ${escapeHtml(reasoning.status)}
         </div>
         ${inner_text}
     </div>`;
 }
 
+function render_reasoning_text(reasoning) {
+    return `Reasoning 🧠: ${reasoning.status}\n\n${reasoning.text}\n\n`;
+}
+
 function filter_message(text) {
     return text.replaceAll(
         /<!-- generated images start -->[\s\S]+<!-- generated images end -->/gm, ""
     ).replace(/ \[aborted\]$/g, "").replace(/ \[error\]$/g, "");
 }
 
+function filter_message_content(text) {
+    return text.replaceAll(
+        /\/\]\(\/generate\//gm, "/](/images/"
+    ).replace(/ \[aborted\]$/g, "").replace(/ \[error\]$/g, "")
+}
+
 function fallback_clipboard (text) {
     var textBox = document.createElement("textarea");
     textBox.value = text;
@@ -182,6 +193,53 @@ const get_message_el = (el) => {
     return message_el;
 }
 
+function register_message_images() {
+    message_box.querySelectorAll(`.loading-indicator`).forEach((el) => el.remove());
+    message_box.querySelectorAll(`.message img:not([alt="your avatar"])`).forEach(async (el) => {
+        if (!el.complete) {
+            const indicator = document.createElement("span");
+            indicator.classList.add("loading-indicator");
+            indicator.innerHTML = `<i class="fas fa-spinner fa-spin"></i>`;
+            el.parentElement.appendChild(indicator);
+            el.onerror = () => {
+                let indexCommand;
+                if ((indexCommand = el.src.indexOf("/generate/")) >= 0) {
+                    indexCommand = indexCommand + "/generate/".length + 1;
+                    let newPath = el.src.substring(indexCommand)
+                    let filename = newPath.replace(/(?:\?.+?|$)/, "");
+                    let seed = Math.floor(Date.now() / 1000);
+                    newPath = `https://image.pollinations.ai/prompt/${newPath}?seed=${seed}&nologo=true`;
+                    let downloadUrl = newPath;
+                    if (document.getElementById("download_images")?.checked) {
+                        downloadUrl = `/images/${filename}?url=${escapeHtml(newPath)}`;
+                    }
+                    const link = document.createElement("a");
+                    link.setAttribute("href", newPath);
+                    const newImg = document.createElement("img");
+                    newImg.src = downloadUrl;
+                    newImg.alt = el.alt;
+                    newImg.onload = () => {
+                        lazy_scroll_to_bottom();
+                        indicator.remove();
+                    }
+                    link.appendChild(newImg);
+                    el.parentElement.appendChild(link);
+                } else {
+                    const span = document.createElement("span");
+                    span.innerHTML = `<i class="fa-solid fa-plug"></i>${escapeHtml(el.alt)}`;
+                    el.parentElement.appendChild(span);
+                }
+                el.remove();
+                indicator.remove();
+            }
+            el.onload = () => {
+                indicator.remove();
+                lazy_scroll_to_bottom();
+            }
+        }
+    });
+}
+
 const register_message_buttons = async () => {
     message_box.querySelectorAll(".message .content .provider").forEach(async (el) => {
         if (!("click" in el.dataset)) {
@@ -243,24 +301,22 @@ const register_message_buttons = async () => {
     message_box.querySelectorAll(".message .fa-file-export").forEach(async (el) => {
         if (!("click" in el.dataset)) {
             el.dataset.click = "true";
+            //
             el.addEventListener("click", async () => {
                 const elem = window.document.createElement('a');
                 let filename = `chat ${new Date().toLocaleString()}.md`.replaceAll(":", "-");
                 const conversation = await get_conversation(window.conversation_id);
                 let buffer = "";
                 conversation.items.forEach(message => {
+                    buffer += render_reasoning_text(message.reasoning);
                     buffer += `${message.role == 'user' ? 'User' : 'Assistant'}: ${message.content.trim()}\n\n\n`;
                 });
-                const file = new File([buffer.trim()], 'message.md', {type: 'text/plain'});
-                const objectUrl = URL.createObjectURL(file);
-                elem.href = objectUrl;
-                elem.download = filename;        
-                document.body.appendChild(elem);
-                elem.click();        
-                document.body.removeChild(elem);
+                var download = document.getElementById("download");
+                download.setAttribute("href", "data:text/markdown;charset=utf-8," + encodeURIComponent(buffer.trim()));
+                download.setAttribute("download", filename);
+                download.click();
                 el.classList.add("clicked");
                 setTimeout(() => el.classList.remove("clicked"), 1000);
-                URL.revokeObjectURL(objectUrl);
             })
         }
     });
@@ -376,7 +432,7 @@ const handle_ask = async (do_ask_gpt = true) => {
     messageInput.focus();
     await scroll_to_bottom();
 
-    let message = messageInput.value;
+    let message = messageInput.value.trim();
     if (message.length <= 0) {
         return;
     }
@@ -755,6 +811,7 @@ async function add_message_chunk(message, message_id, provider, scroll, finish_m
             if (!img.complete)
                 return;
         content_map.inner.innerHTML = markdown_render(message.preview);
+        await register_message_images();
     } else if (message.type == "content") {
         message_storage[message_id] += message.content;
         update_message(content_map, message_id, null, scroll);
@@ -779,7 +836,7 @@ async function add_message_chunk(message, message_id, provider, scroll, finish_m
     } else if (message.type == "reasoning") {
         if (!reasoning_storage[message_id]) {
             reasoning_storage[message_id] = message;
-            reasoning_storage[message_id].text = message.token || "";
+            reasoning_storage[message_id].text = "";
         } else if (message.status) {
             reasoning_storage[message_id].status = message.status;
         } else if (message.token) {
@@ -952,6 +1009,7 @@ const ask_gpt = async (message_id, message_index = -1, regenerate = false, provi
         }
         await safe_remove_cancel_button();
         await register_message_buttons();
+        await register_message_images();
         await load_conversations();
         regenerate_button.classList.remove("regenerate-hidden");
     }
@@ -1201,8 +1259,8 @@ const load_conversation = async (conversation_id, scroll=true) => {
         } else {
             buffer = "";
         }
-        buffer = buffer.replace(/ \[aborted\]$/g, "").replace(/ \[error\]$/g, "");
-        new_content = item.content.replace(/ \[aborted\]$/g, "").replace(/ \[error\]$/g, "");
+        buffer = filter_message_content(buffer);
+        new_content = filter_message_content(item.content);
         buffer = merge_messages(buffer, new_content);
         last_model = item.provider?.model;
         providers.push(item.provider?.name);
@@ -1658,12 +1716,9 @@ const register_settings_storage = async () => {
 const load_settings_storage = async () => {
     const optionElements = document.querySelectorAll(optionElementsSelector);
     optionElements.forEach((element) => {
-        if (element.name && element.name != element.id && (value = appStorage.getItem(element.name))) {
-            appStorage.setItem(element.id, value);
-            appStorage.removeItem(element.name);
-        }
-        if (!(value = appStorage.getItem(element.id))) {
-            return;
+        value = appStorage.getItem(element.id);
+        if (value == null && element.dataset.value) {
+            value = element.dataset.value;
         }
         if (value) {
             switch (element.type) {
@@ -1677,10 +1732,10 @@ const load_settings_storage = async () => {
                 case "number":
                 case "textarea":
                     if (element.id.endsWith("-api_key")) {
-                        element.placeholder = value && value.length >= 22 ? (value.substring(0, 12)+"*".repeat(12)+value.substring(value.length-12)) : "*".repeat(value.length);
+                        element.placeholder = value && value.length >= 22 ? (value.substring(0, 12)+"*".repeat(12)+value.substring(value.length-12)) : "*".repeat(value ? value.length : 0);
                         element.dataset.value = value;
                     } else {
-                        element.value = value;
+                        element.value = value == null ? element.dataset.value : value;
                     }
                     break;
                 default:
@@ -1834,7 +1889,7 @@ async function on_load() {
         let chat_url = new URL(window.location.href)
         let chat_params = new URLSearchParams(chat_url.search);
         if (chat_params.get("prompt")) {
-            messageInput.value = `${chat_params.title}\n${chat_params.prompt}\n${chat_params.url}`.trim();
+            messageInput.value = `${window.location.href}\n`;
             messageInput.style.height = messageInput.scrollHeight  + "px";
             messageInput.focus();
             //await handle_ask();
@@ -2255,7 +2310,9 @@ chatPrompt?.addEventListener("input", async () => {
 });
 
 function get_selected_model() {
-    if (modelProvider.selectedIndex >= 0) {
+    if (custom_model.value) {
+        return custom_model;
+    } else if (modelProvider.selectedIndex >= 0) {
         return modelProvider.options[modelProvider.selectedIndex];
     } else if (modelSelect.selectedIndex >= 0) {
         model = modelSelect.options[modelSelect.selectedIndex];
@@ -2401,17 +2458,31 @@ async function load_provider_models(provider=null) {
     if (!provider) {
         provider = providerSelect.value;
     }
+    if (!custom_model.value) {
+        custom_model.classList.add("hidden");
+    }
+    if (provider == "Custom Model" || custom_model.value) {
+        modelProvider.classList.add("hidden");
+        modelSelect.classList.add("hidden");
+        document.getElementById("model3").classList.remove("hidden");
+        return;
+    }
     modelProvider.innerHTML = '';
     modelProvider.name = `model[${provider}]`;
     if (!provider) {
         modelProvider.classList.add("hidden");
         modelSelect.classList.remove("hidden");
+        document.getElementById("model3").value = "";
+        document.getElementById("model3").classList.remove("hidden");
         return;
     }
     const models = await api('models', provider);
     if (models && models.length > 0) {
         modelSelect.classList.add("hidden");
-        modelProvider.classList.remove("hidden");
+        if (!custom_model.value) {
+            custom_model.classList.add("hidden");
+            modelProvider.classList.remove("hidden");
+        }
         let defaultIndex = 0;
         models.forEach((model, i) => {
             let option = document.createElement('option');
@@ -2423,11 +2494,13 @@ async function load_provider_models(provider=null) {
                 defaultIndex = i;
             }
         });
-        modelProvider.selectedIndex = defaultIndex;
         let value = appStorage.getItem(modelProvider.name);
         if (value) {
             modelProvider.value = value;
         }
+        modelProvider.selectedIndex = defaultIndex;
+    } else if (custom_model.value) {
+        modelSelect.classList.add("hidden");
     } else {
         modelProvider.classList.add("hidden");
         modelSelect.classList.remove("hidden");
@@ -2439,6 +2512,12 @@ providerSelect.addEventListener("change", () => {
 });
 modelSelect.addEventListener("change", () => messageInput.focus());
 modelProvider.addEventListener("change", () =>  messageInput.focus());
+custom_model.addEventListener("change", () => {
+    if (!custom_model.value) {
+        load_provider_models();
+    }
+    messageInput.focus();
+});
 
 document.getElementById("pin").addEventListener("click", async () => {
     const pin_container = document.getElementById("pin_container");

g4f/gui/server/backend_api.py

@@ -276,7 +276,7 @@ class Backend_Api(Api):
                     response = iter_run_tools(ChatCompletion.create, **parameters)
 
                 if do_filter_markdown:
-                    return Response(filter_markdown(response, do_filter_markdown), mimetype='text/plain')
+                    return Response(filter_markdown("".join([str(chunk) for chunk in response]), do_filter_markdown), mimetype='text/plain')
                 def cast_str():
                     for chunk in response:
                         if not isinstance(chunk, Exception):

g4f/tools/run_tools.py

@@ -155,16 +155,28 @@ def iter_run_tools(
             yield chunk
             continue
         if "<think>" in chunk:
-            chunk = chunk.split("<think>", 1)
-            yield chunk[0]
-            yield Reasoning(None, "Is thinking...", is_thinking="<think>")
-            yield Reasoning(chunk[1])
+            if chunk != "<think>":
+                chunk = chunk.split("<think>", 1)
+                if len(chunk) > 0 and chunk[0]:
+                    yield chunk[0]
+            yield Reasoning(None, "🤔 Is thinking...", is_thinking="<think>")
+            if chunk != "<think>":
+                if len(chunk) > 1 and chunk[1]:
+                    yield Reasoning(chunk[1])
             is_thinking = time.time()
         if "</think>" in chunk:
-            chunk = chunk.split("</think>", 1)
-            yield Reasoning(chunk[0])
-            yield Reasoning(None, f"Finished in {round(time.time()-is_thinking, 2)} seconds", is_thinking="</think>")
-            yield chunk[1]
+            if chunk != "<think>":
+                chunk = chunk.split("</think>", 1)
+                if len(chunk) > 0 and chunk[0]:
+                    yield Reasoning(chunk[0])
+            is_thinking = time.time() - is_thinking
+            if is_thinking > 1:
+                yield Reasoning(None, f"Thought for {is_thinking:.2f}s", is_thinking="</think>")
+            else:
+                yield Reasoning(None, f"Finished", is_thinking="</think>")
+            if chunk != "<think>":
+                if len(chunk) > 1 and chunk[1]:
+                    yield chunk[1]
             is_thinking = 0
         elif is_thinking:
             yield Reasoning(chunk)