File size: 8,208 Bytes
f6d5624 982cb18 f71c47a f6d5624 f893897 f6d5624 046be03 f6d5624 046be03 f6d5624 046be03 f6d5624 f71c47a f6d5624 f71c47a f6d5624 f71c47a f6d5624 f71c47a f6d5624 f71c47a f6d5624 f71c47a f6d5624 f71c47a f6d5624 f71c47a f6d5624 046be03 f6d5624 046be03 f6d5624 f71c47a f6d5624 f71c47a f6d5624 046be03 f6d5624 046be03 f6d5624 046be03 f6d5624 046be03 f6d5624 f71c47a f6d5624 f71c47a f6d5624 f71c47a f6d5624 f71c47a f6d5624 f71c47a f6d5624 f71c47a 046be03 f6d5624 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 |
#!/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")
#Alternativ mit beliebigen Modellen:
#base_model = "project-baize/baize-v2-7b"
base_model = "EleutherAI/gpt-neo-1.3B"
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, "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()
##########################################################
#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("Scegli cosa vuoi provare")
with gr.Tabs():
with gr.TabItem("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="Parameter zum Model"):
gr.Markdown("# Parameters")
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("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) |