#!/usr/bin/env python # -*- coding: utf-8 -*- import gradio as gr #from transformers import pipeline import torch from utils import * from presets import * from huggingface_hub import login from transformers import LlamaForCausalLM, LlamaTokenizer #antwort="" ###################################################################### #Modelle und Tokenizer #Hugging Chat nutzen # Create a chatbot connection #chatbot = hugchat.ChatBot(cookie_path="cookies.json") #Alternativ mit beliebigen Modellen: base_model = "project-baize/baize-v2-7b" #load_8bit = False (in load_tokenizer_and_model) #base_model = "MAGAer13/mPLUG-Owl" #load_8bit = False (in load_tokenizer_and_model) #base_model = "alexkueck/li-tis-tuned-2" #load_8bit = False (in load_tokenizer_and_model) #base_model = "TheBloke/airoboros-13B-HF" #load_8bit = False (in load_tokenizer_and_model) #base_model = "EleutherAI/gpt-neo-1.3B" #load_8bit = False (in load_tokenizer_and_model) #base_model = "TheBloke/airoboros-13B-HF" #load_8bit = True #base_model = "TheBloke/vicuna-13B-1.1-HF" #load_8bit = ? #following runs only on GPU upgrade #base_model = "TheBloke/airoboros-65B-gpt4-1.3-GPTQ" #model_basename = "airoboros-65b-gpt4-1.3-GPTQ-4bit--1g.act.order" #base_model = "lmsys/vicuna-13b-v1.3" #base_model = "gpt2-xl" # options: ['gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'] #################################### #Model und Tokenzier laden tokenizer,model,device = load_tokenizer_and_model(base_model,False) ################################ #Alternativ: Model und Tokenizer für GPT2 #tokenizer,model,device = load_tokenizer_and_model_gpt2(base_model,False) #Alternativ bloke gpt3 und4 - only with GPU upgarde #tokenizer,model,device = load_tokenizer_and_model_bloke_gpt(base_model, "airoboros-65b-gpt4-1.3-GPTQ-4bit--1g.act.order") #Alternativ Model und Tokenzier laden für Baize #tokenizer,model,device = load_tokenizer_and_model_Baize(base_model,False) ######################################################################## #Chat KI nutzen, um Text zu generieren... def predict(text, chatbotGr, history, top_p, temperature, max_length_tokens, max_context_length_tokens,): if text=="": yield chatbotGr,history,"Empty context." return try: model except: yield [[text,"No Model Found"]],[],"No Model Found" return inputs = generate_prompt_with_history(text,history,tokenizer,max_length=max_context_length_tokens) if inputs is None: yield chatbotGr,history,"Input too long." return else: prompt,inputs=inputs begin_length = len(prompt) input_ids = inputs["input_ids"][:,-max_context_length_tokens:].to(device) torch.cuda.empty_cache() #torch.no_grad() bedeutet, dass für die betreffenden tensoren keine Ableitungen berechnet werden bei der backpropagation #hier soll das NN ja auch nicht geändert werden 8backprop ist nicht nötig), da es um interference-prompts geht! with torch.no_grad(): #die vergangenen prompts werden alle als Tupel in history abgelegt sortiert nach 'Human' und 'AI'- dass sind daher auch die stop-words, die den jeweils nächsten Eintrag kennzeichnen for x in greedy_search(input_ids,model,tokenizer,stop_words=["[|Human|]", "[|AI|]"],max_length=max_length_tokens,temperature=temperature,top_p=top_p): if is_stop_word_or_prefix(x,["[|Human|]", "[|AI|]"]) is False: if "[|Human|]" in x: x = x[:x.index("[|Human|]")].strip() if "[|AI|]" in x: x = x[:x.index("[|AI|]")].strip() x = x.strip() a, b= [[y[0],convert_to_markdown(y[1])] for y in history]+[[text, convert_to_markdown(x)]],history + [[text,x]] yield a, b, "Generating..." if shared_state.interrupted: shared_state.recover() try: yield a, b, "Stop: Success" return except: pass del input_ids gc.collect() torch.cuda.empty_cache() try: yield a,b,"Generate: Success" except: pass def reset_chat(): #id_new = chatbot.new_conversation() #chatbot.change_conversation(id_new) reset_textbox() #wenn 'Stop' Button geklickt, dann Message dazu und das Eingabe-Fenster leeren def cancel_outputing(): reset_textbox() return "Stop Done" ########################################################## #Übersetzungs Ki nutzen def translate(): return "Kommt noch!" #Programmcode KI def coding(): return "Kommt noch!" ####################################################################### #Darstellung mit Gradio with open("custom.css", "r", encoding="utf-8") as f: customCSS = f.read() with gr.Blocks(theme=small_and_beautiful_theme) as demo: history = gr.State([]) user_question = gr.State("") gr.Markdown("KIs am LI - wähle aus, was du bzgl. KI-Bots ausprobieren möchtest!") with gr.Tabs(): with gr.TabItem("LI-Chat"): with gr.Row(): gr.HTML(title) status_display = gr.Markdown("Erfolg", elem_id="status_display") gr.Markdown(description_top) with gr.Row(scale=1).style(equal_height=True): with gr.Column(scale=5): with gr.Row(scale=1): chatbotGr = gr.Chatbot(elem_id="LI_chatbot").style(height="100%") with gr.Row(scale=1): with gr.Column(scale=12): user_input = gr.Textbox( show_label=False, placeholder="Gib deinen Text / Frage ein." ).style(container=False) with gr.Column(min_width=100, scale=1): submitBtn = gr.Button("Absenden") with gr.Column(min_width=100, scale=1): cancelBtn = gr.Button("Stoppen") with gr.Row(scale=1): emptyBtn = gr.Button( "🧹 Neuer Chat", ) with gr.Column(): with gr.Column(min_width=50, scale=1): with gr.Tab(label="Nur zum Testen:"): gr.Markdown("# Parameter") top_p = gr.Slider( minimum=-0, maximum=1.0, value=0.95, step=0.05, interactive=True, label="Top-p", ) temperature = gr.Slider( minimum=0.1, maximum=2.0, value=1, step=0.1, interactive=True, label="Temperature", ) max_length_tokens = gr.Slider( minimum=0, maximum=512, value=512, step=8, interactive=True, label="Max Generation Tokens", ) max_context_length_tokens = gr.Slider( minimum=0, maximum=4096, value=2048, step=128, interactive=True, label="Max History Tokens", ) gr.Markdown(description) with gr.TabItem("Übersetzungen"): with gr.Row(): gr.Textbox( show_label=False, placeholder="Ist noch in Arbeit..." ).style(container=False) with gr.TabItem("Code-Generierungen"): with gr.Row(): gr.Textbox( show_label=False, placeholder="Ist noch in Arbeit..." ).style(container=False) predict_args = dict( fn=predict, inputs=[ user_question, chatbotGr, history, top_p, temperature, #Variation der Antworten - stand. 1.0 max_length_tokens, max_context_length_tokens, ], outputs=[chatbotGr, history, status_display], show_progress=True, ) #neuer Chat reset_args = dict( #fn=reset_chat, inputs=[], outputs=[user_input, status_display] fn=reset_textbox, inputs=[], outputs=[user_input, status_display] ) # Chatbot transfer_input_args = dict( fn=transfer_input, inputs=[user_input], outputs=[user_question, user_input, submitBtn], show_progress=True ) #Listener auf Start-Click auf Button oder Return predict_event1 = user_input.submit(**transfer_input_args).then(**predict_args) predict_event2 = submitBtn.click(**transfer_input_args).then(**predict_args) #Listener, Wenn reset... emptyBtn.click( reset_state, outputs=[chatbotGr, history, status_display], show_progress=True, ) emptyBtn.click(**reset_args) #Berechnung oder Ausgabe anhalten (kann danach fortgesetzt werden) cancelBtn.click(cancel_outputing, [], [status_display], cancels=[predict_event1,predict_event2]) #cancelBtn.click(lambda: None, None, chatbotGr, queue=False) demo.title = "LI Chat" #demo.queue(concurrency_count=1).launch(share=True) demo.queue(concurrency_count=1).launch(debug=True)