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| #!/usr/bin/env python | |
| # -*- coding: utf-8 -*- | |
| import gradio as gr | |
| #from transformers import pipeline | |
| import torch | |
| from utils import * | |
| from presets import * | |
| import time | |
| from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline | |
| ###################################################################### | |
| #Modelle und Tokenizer | |
| #Alternativ mit beliebigen Modellen: | |
| #base_model = "project-baize/baize-v2-7b" | |
| base_model = "bigcode/starcoder" | |
| tokenizer,model,device = load_tokenizer_and_model(base_model) | |
| #pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0) | |
| #print( pipe("def hello():") ) | |
| ######################################################################## | |
| #Chat KI nutzen, um Text zu generieren... | |
| def predict(text, | |
| chatbotGr, | |
| history, | |
| top_p, | |
| temperature, | |
| max_length_tokens, | |
| max_context_length_tokens,): | |
| global model, tokenizer, device | |
| #wenn eingabe leer - nix tun | |
| if text=="": | |
| yield history,"Empty context." | |
| return | |
| #wenn Model nicht gefunden -> Fehler | |
| try: | |
| model | |
| except: | |
| yield [],"No Model Found" | |
| return | |
| #Prompt generieren -> mit Kontext bezogen auch auf vorhergehende Eingaben in dem chat | |
| inputs = generate_prompt_with_history(text,history,tokenizer,max_length=max_context_length_tokens) | |
| if inputs is None: | |
| yield history,"Input too long." | |
| return | |
| else: | |
| prompt,inputs=inputs | |
| begin_length = len(prompt) | |
| ##################################################################################################### | |
| #ist glaube ich unnötig, da ich mit Pipeline arbeiten -> mal schauen, ich behalte es noch... | |
| """ | |
| 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 | |
| """ | |
| ########################################################################## | |
| #Prompt ist erzeugt, nun mit pipeline eine Antwort von der KI bekommen! | |
| pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0, do_sample=True, top_p=0.95) | |
| bot_message = pipe(prompt) | |
| #chatbot - history erweitern und an chatbotGr zurückschicken | |
| history = history.append((text, bot_message)) | |
| return history, "Erfolg!" | |
| #neuen Chat beginnen | |
| def reset_chat(): | |
| #id_new = chatbot.new_conversation() | |
| #chatbot.change_conversation(id_new) | |
| reset_textbox() | |
| ####################################################################### | |
| #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 - hier um Programmcode generieren zu lassen!") | |
| with gr.Tabs(): | |
| with gr.TabItem("LI-Coding-KI"): | |
| 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="Parameter zum Model"): | |
| gr.Markdown("# Parameter für Testzwecke") | |
| 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) | |
| predict_args = dict( | |
| fn=predict, | |
| inputs=[ | |
| user_input, | |
| history, | |
| top_p, | |
| temperature, | |
| max_length_tokens, | |
| max_context_length_tokens, | |
| ], | |
| outputs=[chatbotGr, 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) | |
| demo.title = "LI Coding KI" | |
| #demo.queue(concurrency_count=1).launch(share=True) | |
| demo.queue(concurrency_count=1).launch(debug=True) | |