mirror of
https://github.com/ParisNeo/lollms.git
synced 2024-12-18 20:27:58 +00:00
added stuff
This commit is contained in:
Saifeddine ALOUI
parent
3a2bc02c1c
commit
8b833fb615
@ -1,5 +1,5 @@
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# =================== Lord Of Large Language Multimodal Systems Configuration file ===========================
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version: 106
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version: 108
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binding_name: null
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model_name: null
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model_variant: null
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@ -31,7 +31,17 @@ app_custom_logo: ""
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discussion_prompt_separator: "!@>"
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start_header_id_template: "!@>"
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end_header_id_template: ": "
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separator_template: "\n"
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start_user_header_id_template: "!@>"
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end_user_header_id_template: ": "
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end_user_message_id_template: ""
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start_ai_header_id_template: "!@>"
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end_ai_header_id_template: ": "
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end_ai_message_id_template: ""
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system_message_template: "system"
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seed: -1
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@ -156,6 +166,10 @@ openai_tts_voice: "alloy"
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enable_sd_service: false
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sd_base_url: http://localhost:7860
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# Image generation service
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enable_fooocus_service: false
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fooocus_base_url: http://localhost:7860
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# diffuser
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diffusers_offloading_mode: sequential_cpu_offload # sequential_cpu_offload
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diffusers_model: PixArt-alpha/PixArt-Sigma-XL-2-1024-MS
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@ -419,29 +419,29 @@ class LollmsApplication(LoLLMsCom):
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trace_exception(ex)
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self.warning(f"Couldn't load Motion control")
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if self.config.active_tti_service == "diffusers":
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if self.config.active_tti_service == "diffusers" and type(self.tti)!=LollmsDiffusers:
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from lollms.services.diffusers.lollms_diffusers import LollmsDiffusers
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self.tti = LollmsDiffusers(self)
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elif self.config.active_tti_service == "autosd":
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elif self.config.active_tti_service == "autosd" and type(self.tti)!=LollmsSD:
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from lollms.services.sd.lollms_sd import LollmsSD
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self.tti = LollmsSD(self)
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elif self.config.active_tti_service == "dall-e":
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elif self.config.active_tti_service == "dall-e" and type(self.tti)!=LollmsDalle:
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from lollms.services.dalle.lollms_dalle import LollmsDalle
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self.tti = LollmsDalle(self, self.config.dall_e_key)
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elif self.config.active_tti_service == "midjourney":
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elif self.config.active_tti_service == "midjourney" and type(self.tti)!=LollmsMidjourney:
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from lollms.services.midjourney.lollms_midjourney import LollmsMidjourney
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self.tti = LollmsMidjourney(self, self.config.midjourney_key)
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if self.config.active_tts_service == "openai_tts":
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if self.config.active_tts_service == "openai_tts" and type(self.tts)!=LollmsOpenAITTS:
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from lollms.services.open_ai_tts.lollms_openai_tts import LollmsOpenAITTS
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self.tts = LollmsOpenAITTS(self, self.config.openai_tts_model, self.config.openai_tts_voice, self.config.openai_tts_key)
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elif self.config.active_tts_service == "xtts" and self.xtts:
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self.tts = self.xtts
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if self.config.active_stt_service == "openai_whisper":
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if self.config.active_stt_service == "openai_whisper" and type(self.stt)!=LollmsOpenAIWhisper:
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from lollms.services.openai_whisper.lollms_openai_whisper import LollmsOpenAIWhisper
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self.stt = LollmsOpenAIWhisper(self, self.config.openai_whisper_model, self.config.openai_whisper_key)
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elif self.config.active_stt_service == "whisper":
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elif self.config.active_stt_service == "whisper" and type(self.stt)!=LollmsWhisper :
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from lollms.services.whisper.lollms_whisper import LollmsWhisper
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self.stt = LollmsWhisper(self, self.config.whisper_model)
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@ -771,11 +771,19 @@ class LollmsApplication(LoLLMsCom):
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"""
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start_header_id_template = self.config.start_header_id_template
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end_header_id_template = self.config.end_header_id_template
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separator_template = self.config.separator_template
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start_user_header_id_template = self.config.start_user_header_id_template
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end_user_header_id_template = self.config.end_user_header_id_template
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end_user_message_id_template = self.config.end_user_message_id_template
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start_ai_header_id_template = self.config.start_ai_header_id_template
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end_ai_header_id_template = self.config.end_ai_header_id_template
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end_ai_message_id_template = self.config.end_ai_message_id_template
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system_message_template = self.config.system_message_template
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if self.personality.callback is None:
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self.personality.callback = partial(self.process_chunk, client_id=client_id)
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# Get the list of messages
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@ -1069,13 +1077,13 @@ class LollmsApplication(LoLLMsCom):
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# Tokenize the message content
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if self.config.use_model_name_in_discussions:
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if message.model:
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msg = f"{separator_template}{start_header_id_template}{message.sender}({message.model}){end_header_id_template}" + message.content.strip()
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msg = f"{separator_template}{start_ai_header_id_template if message.sender_type == SENDER_TYPES.SENDER_TYPES_AI else start_user_header_id_template}{message.sender}({message.model}){end_ai_header_id_template if message.sender_type == SENDER_TYPES.SENDER_TYPES_AI else end_user_header_id_template}" + message.content.strip()
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else:
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msg = f"{separator_template}{start_header_id_template}{message.sender}{end_header_id_template}" + message.content.strip()
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msg = f"{separator_template}{start_ai_header_id_template if message.sender_type == SENDER_TYPES.SENDER_TYPES_AI else start_user_header_id_template}{message.sender}{end_ai_header_id_template if message.sender_type == SENDER_TYPES.SENDER_TYPES_AI else end_user_header_id_template}" + message.content.strip()
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message_tokenized = self.model.tokenize(msg)
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else:
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message_tokenized = self.model.tokenize(
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f"{separator_template}{start_header_id_template}{message.sender}{end_header_id_template}" + message.content.strip()
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f"{separator_template}{start_ai_header_id_template if message.sender_type == SENDER_TYPES.SENDER_TYPES_AI else start_user_header_id_template}{message.sender}{end_ai_header_id_template if message.sender_type == SENDER_TYPES.SENDER_TYPES_AI else end_user_header_id_template}" + message.content.strip()
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)
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# Check if adding the message will exceed the available space
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if tokens_accumulated + len(message_tokenized) > available_space-n_tokens:
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@ -1099,13 +1107,13 @@ class LollmsApplication(LoLLMsCom):
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if self.config.use_model_name_in_discussions:
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if message.model:
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msg = f"{separator_template}{start_header_id_template}{message.sender}({message.model}){end_header_id_template}" + message.content.strip()
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msg = f"{separator_template}{start_ai_header_id_template if message.sender_type == SENDER_TYPES.SENDER_TYPES_AI else start_user_header_id_template}{message.sender}({message.model}){end_ai_header_id_template if message.sender_type == SENDER_TYPES.SENDER_TYPES_AI else end_user_header_id_template}" + message.content.strip()
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else:
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msg = f"{separator_template}{start_header_id_template}{message.sender}{end_header_id_template}" + message.content.strip()
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msg = f"{separator_template}{start_ai_header_id_template if message.sender_type == SENDER_TYPES.SENDER_TYPES_AI else start_user_header_id_template}{message.sender}{end_ai_header_id_template if message.sender_type == SENDER_TYPES.SENDER_TYPES_AI else end_user_header_id_template}" + message.content.strip()
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message_tokenized = self.model.tokenize(msg)
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else:
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message_tokenized = self.model.tokenize(
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f"{separator_template}{start_header_id_template}{message.sender}{end_header_id_template}" + message.content.strip()
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f"{separator_template}{start_ai_header_id_template if message.sender_type == SENDER_TYPES.SENDER_TYPES_AI else start_user_header_id_template}{message.sender}{end_ai_header_id_template if message.sender_type == SENDER_TYPES.SENDER_TYPES_AI else end_user_header_id_template}" + message.content.strip()
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)
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# Add the tokenized message to the full_message_list
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@ -1,5 +1,5 @@
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# =================== Lord Of Large Language Multimodal Systems Configuration file ===========================
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version: 106
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version: 108
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binding_name: null
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model_name: null
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model_variant: null
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@ -31,7 +31,17 @@ app_custom_logo: ""
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discussion_prompt_separator: "!@>"
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start_header_id_template: "!@>"
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end_header_id_template: ": "
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separator_template: "\n"
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start_user_header_id_template: "!@>"
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end_user_header_id_template: ": "
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end_user_message_id_template: ""
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start_ai_header_id_template: "!@>"
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end_ai_header_id_template: ": "
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end_ai_message_id_template: ""
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system_message_template: "system"
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seed: -1
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@ -156,6 +166,10 @@ openai_tts_voice: "alloy"
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enable_sd_service: false
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sd_base_url: http://localhost:7860
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# Image generation service
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enable_fooocus_service: false
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fooocus_base_url: http://localhost:7860
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# diffuser
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diffusers_offloading_mode: sequential_cpu_offload # sequential_cpu_offload
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diffusers_model: PixArt-alpha/PixArt-Sigma-XL-2-1024-MS
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@ -206,3 +206,153 @@ if __name__ == "__main__":
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# Print the HTML output
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print(html_output)
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# Core function to search for PDFs on arXiv and download them to a specified directory
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def search_and_rank(llm, search_subject:str, score_threshold:float, query: str, max_results: Optional[int] = 5, sort_by: Optional[str] = 'relevance', start_date: Optional[str] = None, end_date: Optional[str] = None, author: Optional[str] = None, client: Optional[Any] = None) -> (str, Dict[str, Any]):
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try:
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if client is None:
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download_to = Path("./pdf_search")
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else:
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download_to = client.discussion.discussion_folder / "pdf_search"
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# Construct the search URL with additional parameters
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url = f'http://export.arxiv.org/api/query?search_query={query}&start=0&max_results={max_results}&sortBy={sort_by}'
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if start_date:
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url += f'&startDate={start_date}'
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if end_date:
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url += f'&endDate={end_date}'
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if author:
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url += f'&author={author}'
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response = requests.get(url)
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response.raise_for_status()
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# Parse the response
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soup = BeautifulSoup(response.content, 'xml')
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entries = soup.find_all('entry')
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# Create the directory if it doesn't exist
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download_to.mkdir(parents=True, exist_ok=True)
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# Extract PDF URLs and additional information
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html_output = "<html><body>"
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report_content = ""
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pdf_info = {}
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for entry in entries:
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pdf_url = entry.id.text.replace('abs', 'pdf') + '.pdf'
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pdf_name = pdf_url.split('/')[-1]
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pdf_path = download_to / pdf_name
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if client is None:
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local_url = f'/discussions/pdf_search/{pdf_name}'
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else:
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local_url = discussion_path_to_url(pdf_path)
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# Extract additional information
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title = entry.title.text
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authors = ', '.join(author.find('name').text for author in entry.find_all('author'))
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affiliations = ', '.join(affiliation.text for affiliation in entry.find_all('affiliation'))
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abstract = entry.summary.text
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published_date = entry.published.text
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journal_ref = entry.find('journal_ref').text if entry.find('journal_ref') else 'N/A'
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# Use the LLM to rate the relevance of the text example compared to the search prompt
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relevance_score = llm.fast_gen(f"Rate the relevance of the following text compared to the prompt on a scale of 0 to 1. Only respond with a number between 0 and 1:\nPrompt: {search_subject}\nText: {abstract}\nRelevance score:")
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# Extract the relevance score from the generated text
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try:
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score = float(relevance_score.strip())
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except ValueError:
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score = 0.0 # Default score in case of parsing issues
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if score<score_threshold: # drop irrelevant scores
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continue
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# Create the output tuple
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result = (f"Relevance Score: {score}", {"score": score})
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# Write abstract and additional information to text file
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abstract_path = download_to / f"{pdf_name}_abstract.txt"
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with abstract_path.open('w', encoding='utf-8') as abstract_file:
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abstract_file.write(f"Title: {title}\n")
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abstract_file.write(f"Authors: {authors}\n")
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abstract_file.write(f"Affiliations: {affiliations}\n")
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abstract_file.write(f"Abstract: {abstract}\n")
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abstract_file.write(f"Published Date: {published_date}\n")
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abstract_file.write(f"Journal/Conference: {journal_ref}\n")
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abstract_file.write(f"Relevance score: {score}\n")
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# Download PDF
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pdf_response = requests.get(pdf_url)
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with pdf_path.open('wb') as pdf_file:
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pdf_file.write(pdf_response.content)
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# Append to HTML output
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html_output += f"""
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<div>
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<h2>{title}</h2>
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<p><strong>Authors:</strong> {authors}</p>
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<p><strong>Affiliations:</strong> {affiliations}</p>
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<p><strong>Abstract:</strong> {abstract}</p>
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<p><strong>Published Date:</strong> {published_date}</p>
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<p><strong>Journal/Conference:</strong> {journal_ref}</p>
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<p><strong>Relevance score:</strong> {score}</p>
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<p><a href="{pdf_url}" target="_blank">PDF Link</a></p>
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<p><a href="{local_url}" target="_blank">Local PDF</a></p>
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</div>
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"""
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# Append to report content
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report_content += f"""
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Title: {title}
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Authors: {authors}
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Affiliations: {affiliations}
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Abstract: {abstract}
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Published Date: {published_date}
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Journal/Conference: {journal_ref}
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Relevance score: {score}
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PDF Link: {pdf_url}
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Local PDF: {local_url}
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------------------------
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"""
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# Append to pdf_info dict
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pdf_info[pdf_name] = {
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"title": title,
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"authors": authors,
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"affiliations": affiliations,
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"abstract": abstract,
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"published_date": published_date,
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"journal_ref": journal_ref,
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"pdf_url": pdf_url,
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"local_url": local_url
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}
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# Save the report to a text file
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report_path = download_to / "pdf_search_report.txt"
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with report_path.open('w', encoding='utf-8') as report_file:
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report_file.write(report_content)
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html_output += "</body></html>"
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return html_output, pdf_info
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except Exception as e:
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return trace_exception(e), {}
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# Metadata function
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def search_and_rank_function(llm, score_threshold:float, client: Optional[Any] = None):
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return {
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"function_name": "arxiv_pdf_search", # The function name in string
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"function": partial(arxiv_pdf_search, llm=llm, client=client, score_threshold= score_threshold), # The function to be called with partial to preset client
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"function_description": "Searches for PDFs on arXiv based on a query, downloads them to a specified directory, rates them, sort them by rating and returns a HTML string containing article details and links, along with a dictionary containing detailed information about each PDF.", # Description of the function
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"function_parameters": [ # The set of parameters
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{"name": "search_subject", "type": "str", "description": "The search subject."},
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{"name": "query", "type": "str", "description": "The search query for arXiv."},
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{"name": "max_results", "type": "int", "description": "The maximum number of results to return. (Optional)", "optional": True, "default": 5},
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{"name": "sort_by", "type": "str", "description": "The sorting criteria for the search results (e.g., relevance, lastUpdatedDate). (Optional)", "optional": True, "default": "relevance"},
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{"name": "start_date", "type": "str", "description": "The start date for the search results in the format YYYY-MM-DD. (Optional)", "optional": True},
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{"name": "end_date", "type": "str", "description": "The end date for the search results in the format YYYY-MM-DD. (Optional)", "optional": True},
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{"name": "author", "type": "str", "description": "The author name for the search results. (Optional)", "optional": True},
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]
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}
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@ -3401,8 +3401,9 @@ The AI should respond in this format using data from actions_list:
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start_header_id_template = self.config.start_header_id_template
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end_header_id_template = self.config.end_header_id_template
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system_message_template = self.config.system_message_template
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separator_template = self.config.separator_template
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function_descriptions = [f"{start_header_id_template}{system_message_template}{end_header_id_template}If you need to call a function to fulfull the user request, use a function markdown tag with the function call as the following json format:",
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function_descriptions = [f"{start_header_id_template}{system_message_template}{end_header_id_template}If you need to call a function to fulfill the user request, use a function markdown tag with the function call as the following json format:",
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"```function",
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"{",
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'"function_name":the name of the function to be called,',
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@ -3412,8 +3413,9 @@ The AI should respond in this format using data from actions_list:
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"Only use available functions.",
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"You can call multiple functions in one generation.",
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"Each function call needs to be in a separate function markdown tag.",
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"Do not add status of the execution as it will be added automatically by the system.",
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f"Do not add status of the execution as it will be added automatically by the system.{separator_template}"
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f"{start_header_id_template}Available functions{end_header_id_template}\n"]
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for function in functions:
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description = f"{function['function_name']}: {function['function_description']}\nparameters:{function['function_parameters']}"
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function_descriptions.append(description)
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179
lollms/services/fooocus/lollms_fooocus.py
Normal file
179
lollms/services/fooocus/lollms_fooocus.py
Normal file
@ -0,0 +1,179 @@
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# Title LollmsFooocus
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# Licence: MIT
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# Author : Paris Neo
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# All rights are reserved
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from pathlib import Path
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import sys
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from lollms.app import LollmsApplication
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from lollms.paths import LollmsPaths
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from lollms.config import TypedConfig, ConfigTemplate, BaseConfig
|
||||
from lollms.utilities import PackageManager, check_and_install_torch, find_next_available_filename
|
||||
import time
|
||||
import io
|
||||
import sys
|
||||
import requests
|
||||
import os
|
||||
import base64
|
||||
import subprocess
|
||||
import time
|
||||
import json
|
||||
import platform
|
||||
from dataclasses import dataclass
|
||||
from PIL import Image, PngImagePlugin
|
||||
from enum import Enum
|
||||
from typing import List, Dict, Any
|
||||
|
||||
from ascii_colors import ASCIIColors, trace_exception
|
||||
from lollms.paths import LollmsPaths
|
||||
from lollms.tti import LollmsTTI
|
||||
from lollms.utilities import git_pull, show_yes_no_dialog, run_script_in_env, create_conda_env
|
||||
import subprocess
|
||||
import shutil
|
||||
from tqdm import tqdm
|
||||
import threading
|
||||
|
||||
|
||||
|
||||
def download_file(url, folder_path, local_filename):
|
||||
# Make sure 'folder_path' exists
|
||||
folder_path.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
with requests.get(url, stream=True) as r:
|
||||
r.raise_for_status()
|
||||
total_size = int(r.headers.get('content-length', 0))
|
||||
progress_bar = tqdm(total=total_size, unit='B', unit_scale=True)
|
||||
with open(folder_path / local_filename, 'wb') as f:
|
||||
for chunk in r.iter_content(chunk_size=8192):
|
||||
f.write(chunk)
|
||||
progress_bar.update(len(chunk))
|
||||
progress_bar.close()
|
||||
|
||||
return local_filename
|
||||
|
||||
def install_model(lollms_app:LollmsApplication, model_url):
|
||||
root_dir = lollms_app.lollms_paths.personal_path
|
||||
shared_folder = root_dir/"shared"
|
||||
diffusers_folder = shared_folder / "diffusers"
|
||||
if not PackageManager.check_package_installed("diffusers"):
|
||||
PackageManager.install_or_update("diffusers")
|
||||
if not PackageManager.check_package_installed("torch"):
|
||||
check_and_install_torch(True)
|
||||
|
||||
import torch
|
||||
from diffusers import PixArtSigmaPipeline
|
||||
|
||||
# You can replace the checkpoint id with "PixArt-alpha/PixArt-Sigma-XL-2-512-MS" too.
|
||||
pipe = PixArtSigmaPipeline.from_pretrained(
|
||||
"PixArt-alpha/PixArt-Sigma-XL-2-1024-MS", torch_dtype=torch.float16
|
||||
)
|
||||
|
||||
|
||||
def install_diffusers(lollms_app:LollmsApplication):
|
||||
root_dir = lollms_app.lollms_paths.personal_path
|
||||
shared_folder = root_dir/"shared"
|
||||
diffusers_folder = shared_folder / "diffusers"
|
||||
diffusers_folder.mkdir(exist_ok=True, parents=True)
|
||||
if not PackageManager.check_package_installed("diffusers"):
|
||||
PackageManager.install_or_update("diffusers")
|
||||
PackageManager.install_or_update("xformers")
|
||||
|
||||
|
||||
|
||||
|
||||
def upgrade_diffusers(lollms_app:LollmsApplication):
|
||||
PackageManager.install_or_update("diffusers")
|
||||
PackageManager.install_or_update("xformers")
|
||||
|
||||
|
||||
class LollmsFooocus(LollmsTTI):
|
||||
has_controlnet = False
|
||||
def __init__(
|
||||
self,
|
||||
app:LollmsApplication,
|
||||
wm = "Artbot",
|
||||
):
|
||||
super().__init__(app)
|
||||
self.ready = False
|
||||
# Get the current directory
|
||||
lollms_paths = app.lollms_paths
|
||||
root_dir = lollms_paths.personal_path
|
||||
|
||||
self.wm = wm
|
||||
|
||||
shared_folder = root_dir/"shared"
|
||||
self.diffusers_folder = shared_folder / "diffusers"
|
||||
self.output_dir = root_dir / "outputs/diffusers"
|
||||
self.models_dir = self.diffusers_folder / "models"
|
||||
self.output_dir.mkdir(parents=True, exist_ok=True)
|
||||
self.models_dir.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
ASCIIColors.red(" _ _ _ ______ ")
|
||||
ASCIIColors.red("| | | | | | ___| ")
|
||||
ASCIIColors.red("| | ___ | | |_ __ ___ ___ | |_ ___ ___ ___ ___ _ _ ___ ")
|
||||
ASCIIColors.red("| | / _ \| | | '_ ` _ \/ __| | _/ _ \ / _ \ / _ \ / __| | | / __|")
|
||||
ASCIIColors.red("| |___| (_) | | | | | | | \__ \ | || (_) | (_) | (_) | (__| |_| \__ \ ")
|
||||
ASCIIColors.red("\_____/\___/|_|_|_| |_| |_|___/ \_| \___/ \___/ \___/ \___|\__,_|___/")
|
||||
ASCIIColors.red(" ______ ")
|
||||
ASCIIColors.red(" |______| ")
|
||||
import torch
|
||||
from diffusers import PixArtSigmaPipeline
|
||||
self.model = PixArtSigmaPipeline.from_pretrained(
|
||||
app.config.diffusers_model, torch_dtype=torch.float16, cache_dir=self.models_dir,
|
||||
use_safetensors=True,
|
||||
)
|
||||
# Enable memory optimizations.
|
||||
if app.config.diffusers_offloading_mode=="sequential_cpu_offload":
|
||||
self.model.enable_sequential_cpu_offload()
|
||||
elif app.coinfig.diffusers_offloading_mode=="model_cpu_offload":
|
||||
self.model.enable_model_cpu_offload()
|
||||
|
||||
@staticmethod
|
||||
def verify(app:LollmsApplication):
|
||||
# Clone repository
|
||||
root_dir = app.lollms_paths.personal_path
|
||||
shared_folder = root_dir/"shared"
|
||||
diffusers_folder = shared_folder / "diffusers"
|
||||
return diffusers_folder.exists()
|
||||
|
||||
def get(app:LollmsApplication):
|
||||
root_dir = app.lollms_paths.personal_path
|
||||
shared_folder = root_dir/"shared"
|
||||
diffusers_folder = shared_folder / "diffusers"
|
||||
diffusers_script_path = diffusers_folder / "lollms_diffusers.py"
|
||||
git_pull(diffusers_folder)
|
||||
|
||||
if diffusers_script_path.exists():
|
||||
ASCIIColors.success("lollms_diffusers found.")
|
||||
ASCIIColors.success("Loading source file...",end="")
|
||||
# use importlib to load the module from the file path
|
||||
from lollms.services.diffusers.lollms_diffusers import LollmsFooocus
|
||||
ASCIIColors.success("ok")
|
||||
return LollmsFooocus
|
||||
|
||||
|
||||
def paint(
|
||||
self,
|
||||
positive_prompt,
|
||||
negative_prompt,
|
||||
files=[],
|
||||
sampler_name="Euler",
|
||||
seed=-1,
|
||||
scale=7.5,
|
||||
steps=20,
|
||||
img2img_denoising_strength=0.9,
|
||||
width=512,
|
||||
height=512,
|
||||
restore_faces=True,
|
||||
output_path=None
|
||||
):
|
||||
if output_path is None:
|
||||
output_path = self.output_dir
|
||||
from diffusers.utils.pil_utils import pt_to_pil
|
||||
image = self.model(positive_prompt, negative_prompt=negative_prompt, guidance_scale=scale, num_inference_steps=steps,).images[0]
|
||||
output_path = Path(output_path)
|
||||
fn = find_next_available_filename(output_path,"diff_img_")
|
||||
# Save the image
|
||||
image.save(fn)
|
||||
return fn, {"prompt":positive_prompt, "negative_prompt":negative_prompt}
|
||||
|
||||
@ -767,7 +767,7 @@ class TasksLibrary:
|
||||
end_header_id_template = self.config.end_header_id_template
|
||||
system_message_template = self.config.system_message_template
|
||||
separator_template = self.config.separator_template
|
||||
function_descriptions = [f"{start_header_id_template}information{end_header_id_template}If you need to call a function to fulfull the user request, use a function markdown tag with the function call as the following json format:",
|
||||
function_descriptions = [f"{start_header_id_template}information{end_header_id_template}If you need to call a function to fulfill the user request, use a function markdown tag with the function call as the following json format:",
|
||||
"```function",
|
||||
"{",
|
||||
'"function_name":the name of the function to be called,',
|
||||
|
||||
Loading…
Reference in New Issue
Block a user