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import requests |
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import os, sys, json |
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import gradio as gr |
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import openai |
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from openai import OpenAI |
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import time |
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import re |
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import io |
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from PIL import Image, ImageDraw, ImageOps, ImageFont |
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import base64 |
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from langchain.chains import LLMChain, RetrievalQA |
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from langchain.chat_models import ChatOpenAI |
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from langchain.document_loaders import PyPDFLoader, WebBaseLoader, UnstructuredWordDocumentLoader, DirectoryLoader |
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from langchain.document_loaders.blob_loaders.youtube_audio import YoutubeAudioLoader |
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from langchain.document_loaders.generic import GenericLoader |
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from langchain.document_loaders.parsers import OpenAIWhisperParser |
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from langchain.schema import AIMessage, HumanMessage |
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from langchain.llms import HuggingFaceHub |
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from langchain.llms import HuggingFaceTextGenInference |
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from langchain.embeddings import HuggingFaceInstructEmbeddings, HuggingFaceEmbeddings, HuggingFaceBgeEmbeddings, HuggingFaceInferenceAPIEmbeddings |
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from langchain.embeddings.openai import OpenAIEmbeddings |
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from langchain.prompts import PromptTemplate |
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from langchain.text_splitter import RecursiveCharacterTextSplitter |
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from langchain.vectorstores import Chroma |
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from chromadb.errors import InvalidDimensionException |
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from utils import * |
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from beschreibungen import * |
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from dotenv import load_dotenv, find_dotenv |
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_ = load_dotenv(find_dotenv()) |
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splittet = False |
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template = """Antworte in deutsch, wenn es nicht explizit anders gefordert wird. Wenn du die Antwort nicht kennst, antworte einfach, dass du es nicht weißt. Versuche nicht, die Antwort zu erfinden oder aufzumocken. Halte die Antwort so kurz aber exakt.""" |
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llm_template = "Beantworte die Frage am Ende. " + template + "Frage: {question} Hilfreiche Antwort: " |
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rag_template = "Nutze die folgenden Kontext Teile, um die Frage zu beantworten am Ende. " + template + "{context} Frage: {question} Hilfreiche Antwort: " |
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LLM_CHAIN_PROMPT = PromptTemplate(input_variables = ["question"], |
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template = llm_template) |
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RAG_CHAIN_PROMPT = PromptTemplate(input_variables = ["context", "question"], |
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template = rag_template) |
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HUGGINGFACEHUB_API_TOKEN = os.getenv("HF_ACCESS_READ") |
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OAI_API_KEY=os.getenv("OPENAI_API_KEY") |
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HEADERS = {"Authorization": f"Bearer {HUGGINGFACEHUB_API_TOKEN}"} |
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PATH_WORK = "." |
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CHROMA_DIR = "/chroma" |
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YOUTUBE_DIR = "/youtube" |
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HISTORY_PFAD = "/data/history" |
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PDF_URL = "https://arxiv.org/pdf/2303.08774.pdf" |
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WEB_URL = "https://openai.com/research/gpt-4" |
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YOUTUBE_URL_1 = "https://www.youtube.com/watch?v=--khbXchTeE" |
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YOUTUBE_URL_2 = "https://www.youtube.com/watch?v=hdhZwyf24mE" |
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MODEL_NAME = "gpt-3.5-turbo-16k" |
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MODEL_NAME_IMAGE = "gpt-4-vision-preview" |
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repo_id = "HuggingFaceH4/zephyr-7b-alpha" |
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MODEL_NAME_HF = "mistralai/Mixtral-8x7B-Instruct-v0.1" |
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MODEL_NAME_OAI_ZEICHNEN = "dall-e-3" |
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API_URL = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-2-1" |
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os.environ["HUGGINGFACEHUB_API_TOKEN"] = HUGGINGFACEHUB_API_TOKEN |
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def clear_all(): |
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return None, gr.Image(visible=False) |
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def add_text(chatbot, history, prompt, file): |
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if (file == None): |
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chatbot = chatbot +[(prompt, None)] |
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else: |
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if (prompt == ""): |
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chatbot=chatbot + [((file.name,), "Prompt fehlt!")] |
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else: |
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chatbot = chatbot +[((file.name,), None), (prompt, None)] |
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print("chatbot nach add_text............") |
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print(chatbot) |
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return chatbot, history, prompt, "" |
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def add_text2(chatbot, prompt): |
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if (prompt == ""): |
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chatbot = chatbot + [("", "Prompt fehlt!")] |
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else: |
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chatbot = chatbot + [(prompt, None)] |
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print("chatbot nach add_text............") |
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print(chatbot) |
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return chatbot, prompt, "" |
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def file_anzeigen(file): |
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return gr.Image(label = None, width=37, visible=True, interactive = False, height=37, min_width=37, show_download_button=False, show_share_button=False, show_label=False, scale = 0.5), file, file |
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def cancel_outputing(): |
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reset_textbox() |
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return "Stop Done" |
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def reset_textbox(): |
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return gr.update(value=""),"" |
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def umwandeln_fuer_anzeige(image): |
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buffer = io.BytesIO() |
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image.save(buffer, format='PNG') |
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return buffer.getvalue() |
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def create_picture(history, prompt): |
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client = OpenAI() |
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response = client.images.generate(model="dall-e-3", prompt=prompt,size="1024x1024",quality="standard",n=1,) |
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image_url = response.data[0].url |
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response2 = requests.get(image_url) |
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image = Image.open(response2.raw) |
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return image |
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def process_image(image_path, prompt): |
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with open(image_path, "rb") as image_file: |
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encoded_string = base64.b64encode(image_file.read()).decode('utf-8') |
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headers = { |
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"Content-Type": "application/json", |
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"Authorization": f"Bearer {OAI_API_KEY}" |
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} |
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payload = { |
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"model": MODEL_NAME_IMAGE, |
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"messages": [ |
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{ |
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"role": "user", |
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"content": [ |
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{ |
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"type": "text", |
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"text": prompt |
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}, |
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{ |
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"type": "image_url", |
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"image_url": { |
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"url": f"data:image/jpeg;base64,{encoded_string}" |
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} |
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} |
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] |
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} |
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], |
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"max_tokens": 300 |
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} |
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return headers, payload |
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def transfer_input(inputs): |
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textbox = reset_textbox() |
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return ( |
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inputs, |
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gr.update(value=""), |
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gr.Button.update(visible=True), |
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) |
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def create_directory_loader(file_type, directory_path): |
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loaders = { |
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'.pdf': PyPDFLoader, |
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'.word': UnstructuredWordDocumentLoader, |
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} |
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return DirectoryLoader( |
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path=directory_path, |
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glob=f"**/*{file_type}", |
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loader_cls=loaders[file_type], |
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) |
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def document_loading_splitting(): |
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global splittet |
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docs = [] |
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pdf_loader = create_directory_loader('.pdf', './chroma/pdf') |
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word_loader = create_directory_loader('.word', './chroma/word') |
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pdf_documents = pdf_loader.load() |
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word_documents = word_loader.load() |
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docs.extend(pdf_documents) |
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docs.extend(word_documents) |
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loader = PyPDFLoader(PDF_URL) |
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docs.extend(loader.load()) |
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loader = WebBaseLoader(WEB_URL) |
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docs.extend(loader.load()) |
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loader = GenericLoader(YoutubeAudioLoader([YOUTUBE_URL_1,YOUTUBE_URL_2], PATH_WORK + YOUTUBE_DIR), OpenAIWhisperParser()) |
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docs.extend(loader.load()) |
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text_splitter = RecursiveCharacterTextSplitter(chunk_overlap = 150, chunk_size = 1500) |
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splits = text_splitter.split_documents(docs) |
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splittet = True |
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return splits |
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def document_storage_chroma(splits): |
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Chroma.from_documents(documents = splits, embedding = OpenAIEmbeddings(disallowed_special = ()), persist_directory = PATH_WORK + CHROMA_DIR) |
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def document_storage_mongodb(splits): |
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MongoDBAtlasVectorSearch.from_documents(documents = splits, |
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embedding = OpenAIEmbeddings(disallowed_special = ()), |
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collection = MONGODB_COLLECTION, |
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index_name = MONGODB_INDEX_NAME) |
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def document_retrieval_chroma(llm, prompt): |
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embeddings = OpenAIEmbeddings() |
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db = Chroma(embedding_function = embeddings, persist_directory = PATH_WORK + CHROMA_DIR) |
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return db |
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def document_retrieval_mongodb(llm, prompt): |
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db = MongoDBAtlasVectorSearch.from_connection_string(MONGODB_URI, |
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MONGODB_DB_NAME + "." + MONGODB_COLLECTION_NAME, |
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OpenAIEmbeddings(disallowed_special = ()), |
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index_name = MONGODB_INDEX_NAME) |
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return db |
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def llm_chain(llm, prompt): |
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llm_chain = LLMChain(llm = llm, prompt = LLM_CHAIN_PROMPT) |
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result = llm_chain.run({"question": prompt}) |
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return result |
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def rag_chain(llm, prompt, db): |
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rag_chain = RetrievalQA.from_chain_type(llm, |
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chain_type_kwargs = {"prompt": RAG_CHAIN_PROMPT}, |
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retriever = db.as_retriever(search_kwargs = {"k": 3}), |
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return_source_documents = True) |
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result = rag_chain({"query": prompt}) |
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return result["result"] |
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def generate_prompt_with_history(text, history, max_length=4048): |
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prompt="" |
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history = ["\n{}\n{}".format(x[0],x[1]) for x in history] |
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history.append("\n{}\n".format(text)) |
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history_text = "" |
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flag = False |
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for x in history[::-1]: |
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history_text = x + history_text |
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flag = True |
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print ("Prompt: ..........................") |
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print(prompt+history_text) |
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if flag: |
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return prompt+history_text |
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else: |
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return None |
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def generate_prompt_with_history_openai(prompt, history): |
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history_openai_format = [] |
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for human, assistant in history: |
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history_openai_format.append({"role": "user", "content": human }) |
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history_openai_format.append({"role": "assistant", "content":assistant}) |
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history_openai_format.append({"role": "user", "content": prompt}) |
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print("openai history und prompt................") |
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print(history_openai_format) |
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return history_openai_format |
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def generate_prompt_with_history_hf(prompt, history): |
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history_transformer_format = history + [[prompt, ""]] |
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messages = "".join(["".join(["\n<human>:"+item[0], "\n<bot>:"+item[1]]) |
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for item in history_transformer_format]) |
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def generate_prompt_with_history_langchain(prompt, history): |
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history_langchain_format = [] |
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for human, ai in history: |
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history_langchain_format.append(HumanMessage(content=human)) |
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history_langchain_format.append(AIMessage(content=ai)) |
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history_langchain_format.append(HumanMessage(content=prompt)) |
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return history_langchain_format |
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def generate_auswahl(prompt, file, chatbot, history, rag_option, model_option, openai_api_key, k=3, top_p=0.6, temperature=0.5, max_new_tokens=4048, max_context_length_tokens=2048, repetition_penalty=1.3,): |
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if (file == None): |
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result = generate_text(prompt, chatbot, history, rag_option, model_option, openai_api_key, k=3, top_p=0.6, temperature=0.5, max_new_tokens=4048, max_context_length_tokens=2048, repetition_penalty=1.3,) |
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history = history + [(prompt, result)] |
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else: |
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result= generate_text_zu_bild(file, prompt) |
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history = history + [((file,), None),(prompt, result)] |
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print("result..................") |
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print(result) |
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print("history.......................") |
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print(chatbot) |
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chatbot[-1][1] = "" |
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for character in result: |
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chatbot[-1][1] += character |
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time.sleep(0.03) |
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yield chatbot, history, "Generating" |
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if shared_state.interrupted: |
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shared_state.recover() |
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try: |
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yield chatbot, history, "Stop: Success" |
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except: |
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pass |
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def generate_bild(prompt, chatbot, temperature=0.5, max_new_tokens=4048,top_p=0.6, repetition_penalty=1.3): |
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data = {"inputs": prompt} |
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response = requests.post(API_URL, headers=HEADERS, json=data) |
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print("fertig Bild") |
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result = response.content |
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image = Image.open(io.BytesIO(result)) |
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image_64 = umwandeln_fuer_anzeige(image) |
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chatbot[-1][1]= "<img src='data:image/png;base64,{0}'/>".format(base64.b64encode(image_64).decode('utf-8')) |
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return chatbot, "Success" |
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def generate_text_zu_bild(file, prompt): |
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headers, payload = process_image(file, prompt) |
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response = "Deas ist ein Test mit att Bild" |
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return response |
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def generate_text (prompt, chatbot, history, rag_option, model_option, openai_api_key, k=3, top_p=0.6, temperature=0.5, max_new_tokens=4048, max_context_length_tokens=2048, repetition_penalty=1.3,): |
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global splittet |
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print(splittet) |
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if (openai_api_key == "" or openai_api_key == "sk-"): |
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openai_api_key= OAI_API_KEY |
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if (rag_option is None): |
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raise gr.Error("Retrieval Augmented Generation ist erforderlich.") |
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if (prompt == ""): |
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raise gr.Error("Prompt ist erforderlich.") |
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history_text_und_prompt = generate_prompt_with_history_openai(prompt, history) |
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try: |
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if (model_option == "OpenAI"): |
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print("OpenAI normal.......................") |
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llm = ChatOpenAI(model_name = MODEL_NAME, openai_api_key = openai_api_key, temperature=temperature) |
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else: |
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llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={"temperature": 0.5, "max_length": 128}) |
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print("HF") |
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if (rag_option == "An"): |
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if not splittet: |
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splits = document_loading_splitting() |
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document_storage_chroma(splits) |
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db = document_retrieval_chroma(llm, history_text_und_prompt) |
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print("LLM aufrufen mit RAG: ...........") |
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result = rag_chain(llm, history_text_und_prompt, db) |
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elif (rag_option == "MongoDB"): |
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db = document_retrieval_mongodb(llm, history_text_und_prompt) |
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result = rag_chain(llm, history_text_und_prompt, db) |
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else: |
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print("LLM aufrufen ohne RAG: ...........") |
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result = " Ein Text " |
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except Exception as e: |
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raise gr.Error(e) |
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return result |
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description = """<strong>Information:</strong> Hier wird ein <strong>Large Language Model (LLM)</strong> mit |
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<strong>Retrieval Augmented Generation (RAG)</strong> auf <strong>externen Daten</strong> verwendet.\n\n |
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""" |
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description2 = "<strong>Information:</strong> Hier wird ein <strong>Large Language Model (LLM)</strong> zum Zeichnen verwendet. Zur Zeit wird hier Stable Diffusion verwendet.\n\n" |
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def vote(data: gr.LikeData): |
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if data.liked: print("You upvoted this response: " + data.value) |
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else: print("You downvoted this response: " + data.value) |
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print ("Start GUIneu") |
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with open("custom.css", "r", encoding="utf-8") as f: |
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customCSS = f.read() |
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additional_inputs = [ |
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gr.Slider(label="Temperature", value=0.65, minimum=0.0, maximum=1.0, step=0.05, interactive=True, info="Höhere Werte erzeugen diversere Antworten", visible=True), |
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gr.Slider(label="Max new tokens", value=1024, minimum=0, maximum=4096, step=64, interactive=True, info="Maximale Anzahl neuer Tokens", visible=True), |
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gr.Slider(label="Top-p (nucleus sampling)", value=0.6, minimum=0.0, maximum=1, step=0.05, interactive=True, info="Höhere Werte verwenden auch Tokens mit niedrigerer Wahrscheinlichkeit.", visible=True), |
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gr.Slider(label="Repetition penalty", value=1.2, minimum=1.0, maximum=2.0, step=0.05, interactive=True, info="Strafe für wiederholte Tokens", visible=True) |
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] |
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with gr.Blocks(css=customCSS, theme=small_and_beautiful_theme) as demo: |
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history = gr.State([]) |
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user_question = gr.State("") |
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user_question2 = gr.State("") |
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attached_file = gr.State(None) |
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with gr.Tab("Chatbot"): |
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with gr.Row(): |
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gr.HTML("LI Chatot") |
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status_display = gr.Markdown("Success", elem_id="status_display") |
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gr.Markdown(description_top) |
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with gr.Row(): |
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with gr.Column(scale=5): |
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with gr.Row(): |
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chatbot = gr.Chatbot(elem_id="li-chat",) |
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with gr.Row(): |
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with gr.Column(scale=12): |
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user_input = gr.Textbox( |
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show_label=False, placeholder="Gib hier deinen Prompt ein...", |
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container=False |
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) |
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with gr.Column(min_width=70, scale=1): |
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submitBtn = gr.Button("Senden") |
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with gr.Column(min_width=70, scale=1): |
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cancelBtn = gr.Button("Stop") |
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with gr.Row(): |
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image_display = gr.Image( visible=False) |
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upload = gr.UploadButton("📁", file_types=["image", "video", "audio"], scale = 10) |
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emptyBtn = gr.ClearButton([user_input, chatbot, history, attached_file, image_display], value="🧹 Neue Session", scale=10) |
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with gr.Column(): |
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with gr.Column(min_width=50, scale=1): |
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with gr.Tab(label="Parameter Einstellung"): |
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rag_option = gr.Radio(["Aus", "An"], label="RAG - LI Erweiterungen", value = "Aus") |
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model_option = gr.Radio(["OpenAI", "HuggingFace"], label="Modellauswahl", value = "OpenAI") |
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top_p = gr.Slider( |
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minimum=-0, |
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maximum=1.0, |
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value=0.95, |
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step=0.05, |
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interactive=True, |
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label="Top-p", |
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) |
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temperature = gr.Slider( |
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minimum=0.1, |
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maximum=2.0, |
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value=0.5, |
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step=0.1, |
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interactive=True, |
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label="Temperature", |
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) |
|
max_length_tokens = gr.Slider( |
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minimum=0, |
|
maximum=512, |
|
value=512, |
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step=8, |
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interactive=True, |
|
label="Max Generation Tokens", |
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) |
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max_context_length_tokens = gr.Slider( |
|
minimum=0, |
|
maximum=4096, |
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value=2048, |
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step=128, |
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interactive=True, |
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label="Max History Tokens", |
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) |
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repetition_penalty=gr.Slider(label="Repetition penalty", value=1.2, minimum=1.0, maximum=2.0, step=0.05, interactive=True, info="Strafe für wiederholte Tokens", visible=True) |
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anzahl_docs = gr.Slider(label="Anzahl Dokumente", value=3, minimum=1, maximum=10, step=1, interactive=True, info="wie viele Dokumententeile aus dem Vektorstore an den prompt gehängt werden", visible=True) |
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openai_key = gr.Textbox(label = "OpenAI API Key", value = "sk-", lines = 1) |
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|
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with gr.Tab("KI zum Zeichnen"): |
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with gr.Row(): |
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gr.HTML("LI Zeichnen mit KI") |
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status_display2 = gr.Markdown("Success", elem_id="status_display") |
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gr.Markdown(description2) |
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with gr.Row(): |
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with gr.Column(scale=5): |
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with gr.Row(): |
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chatbot_bild = gr.Chatbot(elem_id="li-zeichnen") |
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with gr.Row(): |
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with gr.Column(scale=12): |
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user_input2 = gr.Textbox( |
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show_label=False, placeholder="Gib hier deinen Prompt ein...", |
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container=False |
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) |
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with gr.Column(min_width=70, scale=1): |
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submitBtn2 = gr.Button("Senden") |
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|
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with gr.Row(): |
|
emptyBtn2 = gr.ClearButton([user_input, chatbot_bild], value="🧹 Neue Session", scale=10) |
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|
|
predict_args = dict( |
|
fn=generate_auswahl, |
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inputs=[ |
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user_question, |
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attached_file, |
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chatbot, |
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history, |
|
rag_option, |
|
model_option, |
|
openai_key, |
|
anzahl_docs, |
|
top_p, |
|
temperature, |
|
max_length_tokens, |
|
max_context_length_tokens, |
|
repetition_penalty |
|
], |
|
outputs=[chatbot, history, status_display], |
|
show_progress=True, |
|
) |
|
|
|
|
|
reset_args = dict( |
|
fn=reset_textbox, inputs=[], outputs=[user_input, status_display] |
|
) |
|
|
|
|
|
transfer_input_args = dict( |
|
fn=add_text, inputs=[chatbot, history, user_input, attached_file], outputs=[chatbot, history, user_question, user_input], show_progress=True |
|
) |
|
|
|
predict_event1 = user_input.submit(**transfer_input_args, queue=False,).then(**predict_args) |
|
predict_event2 = submitBtn.click(**transfer_input_args, queue=False,).then(**predict_args) |
|
predict_event3 = upload.upload(file_anzeigen, [upload], [image_display, image_display, attached_file] ) |
|
emptyBtn.click(clear_all, [], [attached_file, image_display]) |
|
|
|
|
|
cancelBtn.click(cancel_outputing, [], [status_display], cancels=[predict_event1,predict_event2, predict_event3]) |
|
|
|
|
|
|
|
predict_args2 = dict( |
|
fn=generate_bild, |
|
inputs=[ |
|
user_question2, |
|
chatbot_bild, |
|
|
|
], |
|
outputs=[chatbot_bild, status_display2], |
|
show_progress=True, |
|
) |
|
transfer_input_args2 = dict( |
|
fn=add_text2, inputs=[chatbot_bild, user_input2], outputs=[chatbot_bild, user_question2, user_input2], show_progress=True |
|
) |
|
predict_event2_1 = user_input2.submit(**transfer_input_args2, queue=False,).then(**predict_args2) |
|
predict_event2_2 = submitBtn2.click(**transfer_input_args2, queue=False,).then(**predict_args2) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
demo.title = "LI-ChatBot" |
|
demo.queue().launch(debug=True) |
|
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