app.py

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import gradio as gr
#from transformers import pipeline 
import torch
from utils import *
from presets import *

#antwort=""
######################################################################
#Modelle und Tokenizer

#Hugging Chat nutzen
# Create a chatbot connection
#chatbot = hugchat.ChatBot(cookie_path="cookies.json")

#base_model = "project-baize/baize-v2-7b"
#base_model = "EleutherAI/gpt-neo-1.3B"
#base_model = "EleutherAI/gpt-neo-2.7B"
base_model = "dbmdz/electra-base-italian-xxl-cased-discriminator"

tokenizer,model,device = load_tokenizer_and_model(base_model)


########################################################################
#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,"Testo vuoto."
        return 
    try:
        model
    except:
        yield [[text,"Nessun modello trovato"]],[],"Nessun modello trovato"
        return

    inputs = generate_prompt_with_history(text,history,tokenizer,max_length=max_context_length_tokens)
    if inputs is None:
        yield chatbotGr,history,"Input troppo lungo."
        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, "Sto elaborando ..."
            if shared_state.interrupted:
                shared_state.recover()
                try:
                    yield a, b, "Stop: OK"
                    return
                except:
                    pass
    del input_ids
    gc.collect()
    torch.cuda.empty_cache()
    
    try:
        yield a,b,"Generazione: OK"
    except:
        pass


def reset_chat():
    #id_new = chatbot.new_conversation()
    #chatbot.change_conversation(id_new)
    reset_textbox()
    
    
##########################################################
def translate():
    return "In costruzione"
    
def coding():
    return "In costruzione"

#######################################################################
#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("Scegli cosa vuoi provare:")
    with gr.Tabs():
        with gr.TabItem("Chat"):
            with gr.Row():
                gr.HTML(title)
                status_display = gr.Markdown("OK", 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="Chat").style(height="100%")
                    with gr.Row(scale=1):
                        with gr.Column(scale=12):
                            user_input = gr.Textbox(
                                show_label=False, placeholder="Inserisci il tuo testo / domanda"
                            ).style(container=False)
                        with gr.Column(min_width=100, scale=1):
                            submitBtn = gr.Button("Invia")
                        with gr.Column(min_width=100, scale=1):
                            cancelBtn = gr.Button("Cancella")
                    with gr.Row(scale=1):
                        emptyBtn = gr.Button(
                            "🧹 Nuova Chat",
                        )
                with gr.Column():
                    with gr.Column(min_width=50, scale=1):
                        with gr.Tab(label="Parametri del modello"):
                            gr.Markdown("# Parametri")
                            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="Temperatura",
                            )
                            max_length_tokens = gr.Slider(
                                minimum=0,
                                maximum=512,
                                value=512,
                                step=8,
                                interactive=True,
                                label="Numeno massimo di parole",
                            )
                            max_context_length_tokens = gr.Slider(
                                minimum=0,
                                maximum=4096,
                                value=2048,
                                step=128,
                                interactive=True,
                                label="Numero massimo di parole memorizzate",
                            )
            gr.Markdown(description)

        with gr.TabItem("Traduzioni"):
            with gr.Row():
                    gr.Textbox(
                                show_label=False, placeholder="In costruzione ..."
                            ).style(container=False)
        with gr.TabItem("Generazione di codice"):
            with gr.Row():
                    gr.Textbox(
                                show_label=False, placeholder="In costruzione ..."
                            ).style(container=False)
    
    predict_args = dict(
        fn=predict,
        inputs=[
            user_question,
            chatbotGr,
            history,
            top_p,
            temperature,
            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)

demo.title = "Chat"
#demo.queue(concurrency_count=1).launch(share=True) 
demo.queue(concurrency_count=1).launch(debug=True)