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AFischer1985
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Parent(s):
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Update run.py
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run.py
CHANGED
@@ -1,18 +1,34 @@
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# Title:
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# Author: Andreas Fischer
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# Date:
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# Last update:
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# Chroma-DB
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#-----------
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import os
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import chromadb
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dbPath="/home/af/Schreibtisch/gradio/Chroma/db"
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if(os.path.exists(dbPath)==False):
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print(dbPath)
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#client = chromadb.Client()
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path=dbPath
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print(client.list_collections())
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from chromadb.utils import embedding_functions
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default_ef = embedding_functions.DefaultEmbeddingFunction()
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sentence_transformer_ef = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="T-Systems-onsite/cross-en-de-roberta-sentence-transformer")
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#instructor_ef = embedding_functions.InstructorEmbeddingFunction(model_name="hkunlp/instructor-large", device="cuda")
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print(str(client.list_collections()))
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global collection
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else:
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print("
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collection = client.create_collection(
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embedding_function=
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metadata={"hnsw:space": "cosine"})
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"Text generating AI model mistralai/Mixtral-8x7B-Instruct-v0.1: Suitable for text generation, e.g., social media content, marketing copy, blog posts, short stories, etc.",
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"Image generating AI model stabilityai/sdxl-turbo: Suitable for image generation, e.g., illustrations, graphics, AI art, etc.",
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"Audio transcribing AI model openai/whisper-large-v3: Suitable for audio-transcription in different languages",
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@@ -46,80 +77,328 @@ else:
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"Code generating AI model deepseek-ai/deepseek-coder-6.7b-instruct: Suitable for programming in Python, JavaScript, PHP, Bash and many other programming languages.",
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"Translation AI model Helsinki-NLP/opus-mt: Suitable for translating text, e.g., from English to German or vice versa",
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"Search result-integrating AI model phind/phind-v9-model: Suitable for researching current topics and for obtaining precise and up-to-date answers to questions based on web search results"
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print("Database ready!")
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print(collection.count())
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import
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# Gradio-GUI
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#------------
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import gradio as gr
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import json
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def
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n_results=2,
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#where_document={"$contains":"search_string"}
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dists=["<br><small>(relevance: "+str(round((1-d)*100)/100)+";" for d in
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sources=["source: "+s["source"]+")</small>" for s in
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combination = zip(
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combination = [' '.join(triplets) for triplets in combination]
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print(combination)
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#########################################################################################
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# Title: German AI-Interface to the Hugging Face Hub with advanced RAG
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# Author: Andreas Fischer
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# Date: January 31st, 2023
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# Last update: February 21st, 2024
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##########################################################################################
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#https://github.com/abetlen/llama-cpp-python/issues/306
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#sudo apt install libclblast-dev
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#CMAKE_ARGS="-DLLAMA_CLBLAST=on" FORCE_CMAKE=1 pip install llama-cpp-python --force-reinstall --upgrade --no-cache-dir -v
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# Prepare resources
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#-------------------
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import torch
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import gc
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torch.cuda.empty_cache()
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gc.collect()
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import os
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from datetime import datetime
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global filename
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filename=f"./{datetime.now().strftime('%Y%m%d')}_history.json" # where to store the history as json-file
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if(os.path.exists(filename)==True): os.remove(filename)
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# Chroma-DB
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#-----------
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import os
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import chromadb
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dbPath="/home/af/Schreibtisch/gradio/Chroma/db"
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if(os.path.exists(dbPath)==False): dbPath="/home/user/app/db"
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print(dbPath)
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#client = chromadb.Client()
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path=dbPath
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print(client.list_collections())
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from chromadb.utils import embedding_functions
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default_ef = embedding_functions.DefaultEmbeddingFunction()
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#sentence_transformer_ef = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="T-Systems-onsite/cross-en-de-roberta-sentence-transformer")
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#instructor_ef = embedding_functions.InstructorEmbeddingFunction(model_name="hkunlp/instructor-large", device="cuda")
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embeddingModel = embedding_functions.InstructorEmbeddingFunction(model_name="T-Systems-onsite/cross-en-de-roberta-sentence-transformer", device="cuda")
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print(str(client.list_collections()))
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global collection
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dbName="myDB"
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if("name="+dbName in str(client.list_collections())): client.delete_collection(name=dbName)
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if("name="+dbName in str(client.list_collections())):
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print(dbName+" found!")
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collection = client.get_collection(name=dbName, embedding_function=embeddingModel )
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else:
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print(dbName+" created!")
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collection = client.create_collection(
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dbName,
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embedding_function=embeddingModel,
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metadata={"hnsw:space": "cosine"})
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# txts0: Intentions
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#------------------
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txts0=[
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"Ich suche ein KI-Programm mit bestimmten Fähigkeiten.", # 1a
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#"Ich suche kein KI-Programm mit bestimmten Fähigkeiten.", # !1a
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"Ich habe ein KI-Programm und habe Fragen zur Benutzung.", # !1a (besser, um 1a und 1b abzugrenzen)
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"Ich habe ein KI-Programm und habe Fragen zur Benutzung.", # 1b
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#"Ich habe kein KI-Programm und habe keine Fragen zur Benutzung.", # !1b
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"Ich habe eine allgemeine Frage ohne KI-Bezug." # !1b (greift besser bei Alltagsfragen)
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]
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# txts1a: RAG-Infos for first intention:
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#---------------------------------------
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txts1a=[
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"Text generating AI model mistralai/Mixtral-8x7B-Instruct-v0.1: Suitable for text generation, e.g., social media content, marketing copy, blog posts, short stories, etc.",
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"Image generating AI model stabilityai/sdxl-turbo: Suitable for image generation, e.g., illustrations, graphics, AI art, etc.",
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"Audio transcribing AI model openai/whisper-large-v3: Suitable for audio-transcription in different languages",
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"Code generating AI model deepseek-ai/deepseek-coder-6.7b-instruct: Suitable for programming in Python, JavaScript, PHP, Bash and many other programming languages.",
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"Translation AI model Helsinki-NLP/opus-mt: Suitable for translating text, e.g., from English to German or vice versa",
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"Search result-integrating AI model phind/phind-v9-model: Suitable for researching current topics and for obtaining precise and up-to-date answers to questions based on web search results"
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]
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# txts1b: RAG-Infos for second intention
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#----------------------------------------
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txts1b=[
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"Für Fragen zur Umsetzung von KI-Verfahren ist das KI-basierte Assistenzsystem nicht geeignet. Möglicherweise empfiehlt sich ein KI-Modell mit Internetzugriff, wie beispielsweise phind.com, oder das Kontaktieren eines Experten wie Dr. Andreas Fischer (andreasfischer1985@web.de)."
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]
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#meta=[{"type":"0", "type2":"0","source":"AF"}]*len(txts0)+[{"type":"1a","type2":"0","source":"AF"}]*len(txts1a)+[{"type":"1b","type2":"0","source":"AF"}]*len(txts1b)
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meta = []
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for _ in range(len(txts0)):
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meta.append({"type":"0", "type2":"0","source":"AF"})
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for _ in range(len(txts1a)):
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meta.append({"type":"1a","type2":"0","source":"AF"})
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for _ in range(len(txts1b)):
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meta.append({"type":"1b","type2":"0","source":"AF"})
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#Change type2 for txt0-entries
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#-----------------------------
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meta[0]["type2"]="1a" # RAG mit txts1a
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meta[1]["type2"]="!1a" # else
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meta[2]["type2"]="1b" # RAG mit txts1b
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meta[3]["type2"]="!1b" # else
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txts=txts0+txts1a+txts1b
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collection.add(
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documents=txts,
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ids=[str(i) for i in list(range(len(txts)))],
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metadatas=meta
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)
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# Add entry to episodic memory
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x=collection.get(include=[])["ids"]
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if(True): #len(x)==0):
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message="Ich bin der User."
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response="Hallo User, wie kann ich dienen?"
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x=collection.get(include=[])["ids"]
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collection.add(
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documents=[message,response],
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metadatas=[
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{"source": "ICH", "dialog": f"ICH: {message}\nDU: {response}", "type":"episode"},
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{"source": "DU", "dialog": f"ICH: {message}\nDU: {response}", "type":"episode"}
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],
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ids=[str(len(x)+1),str(len(x)+2)]
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)
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RAGResults=collection.query(
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query_texts=[message],
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n_results=1,
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#where={"source": "USER"}
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)
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RAGResults["metadatas"][0][0]["dialog"]
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x=collection.get(include=[])["ids"]
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x
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collection.get() # Inspect db-entries
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print("Database ready!")
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print(collection.count())
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rag0=collection.query(
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query_texts=[message],
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n_results=4,
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where={"type": "0"}
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)
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x=rag0["metadatas"][0][0]["type2"]
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x=[x["type2"] for x in rag0["metadatas"][0]]
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x.index("1c") if "1c" in x else len(x)+1
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# Get model
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#-----------
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import os
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import requests
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modelPath="/home/af/gguf/models/discolm_german_7b_v1.Q4_0.gguf"
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if(os.path.exists(modelPath)==False):
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#url="https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GGUF/resolve/main/wizardlm-13b-v1.2.Q4_0.gguf"
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#url="https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF/resolve/main/mixtral-8x7b-instruct-v0.1.Q4_0.gguf?download=true"
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#url="https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/resolve/main/mistral-7b-instruct-v0.2.Q4_0.gguf?download=true"
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url="https://huggingface.co/TheBloke/DiscoLM_German_7b_v1-GGUF/resolve/main/discolm_german_7b_v1.Q4_0.gguf?download=true"
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response = requests.get(url)
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with open("./model.gguf", mode="wb") as file:
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file.write(response.content)
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print("Model downloaded")
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modelPath="./model.gguf"
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print(modelPath)
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# Llama-cpp-Server
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#------------------
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import subprocess
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n="20"
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if("mixtral-8x7b-instruct" in modelPath): n="0" # mixtral seems to cause problems here...
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command = ["python3", "-m", "llama_cpp.server", "--model", modelPath, "--host", "0.0.0.0", "--port", "2600", "--n_threads", "8", "--n_gpu_layers", n]
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subprocess.Popen(command)
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print("Server ready!")
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# Gradio-GUI
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#------------
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def extend_prompt(message="", history=None, system=None, RAGAddon=None, system2=None, zeichenlimit=None,historylimit=4): #float("Inf")
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if zeichenlimit is None: zeichenlimit=1000000000 # :-)
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template0="[INST] {system} [/INST]</s>" #<s>
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template1="[INST] {message} [/INST] "
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template2="{response}</s>"
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if("mixtral-8x7b-instruct" in modelPath): # https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1
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template0="[INST] {system} [/INST]</s>" #<s>
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template1="[INST] {message} [/INST] "
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template2="{response}</s>"
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if("Mistral-7B-Instruct" in modelPath): #https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2
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template0="[INST] {system} [/INST]</s>" #<s>
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193 |
+
template1="[INST] {message} [/INST] "
|
194 |
+
template2="{response}</s>"
|
195 |
+
if("openchat-3.5" in modelPath): #https://huggingface.co/TheBloke/openchat-3.5-0106-GGUF
|
196 |
+
template0="GPT4 Correct User: {system}<|end_of_turn|>GPT4 Correct Assistant: Okay.<|end_of_turn|>"
|
197 |
+
template1="GPT4 Correct User: {message}<|end_of_turn|>GPT4 Correct Assistant: "
|
198 |
+
template2="{response}<|end_of_turn|>"
|
199 |
+
if("SauerkrautLM-7b-HerO" in modelPath): #https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-HerO
|
200 |
+
template0="<|im_start|>system\n{system}<|im_end|>\n"
|
201 |
+
template1="<|im_start|>user\n{message}<|im_end|>\n<|im_start|>assistant\n"
|
202 |
+
template2="{response}<|im_end|>\n"
|
203 |
+
if("discolm_german_7b" in modelPath): #https://huggingface.co/DiscoResearch/DiscoLM_German_7b_v1
|
204 |
+
template0="<|im_start|>system\n{system}<|im_end|>\n"
|
205 |
+
template1="<|im_start|>user\n{message}<|im_end|>\n<|im_start|>assistant\n"
|
206 |
+
template2="{response}<|im_end|>\n"
|
207 |
+
if("WizardLM-13B-V1.2" in modelPath): #https://huggingface.co/WizardLM/WizardLM-13B-V1.2
|
208 |
+
template0="{system} " #<s>
|
209 |
+
template1="USER: {message} ASSISTANT: "
|
210 |
+
template2="{response}</s>"
|
211 |
+
if("phi-2" in modelPath): #https://huggingface.co/TheBloke/phi-2-GGUF
|
212 |
+
template0="Instruct: {system}\nOutput: Okay.\n"
|
213 |
+
template1="Instruct: {message}\nOutput:"
|
214 |
+
template2="{response}\n"
|
215 |
+
prompt = ""
|
216 |
+
if RAGAddon is not None:
|
217 |
+
system += RAGAddon
|
218 |
+
if system is not None:
|
219 |
+
prompt += template0.format(system=system) #"<s>"
|
220 |
+
if history is not None:
|
221 |
+
for user_message, bot_response in history[-historylimit:]:
|
222 |
+
if user_message is not None: prompt += template1.format(message=user_message[:zeichenlimit]) #"[INST] {user_prompt} [/INST] "
|
223 |
+
if bot_response is not None: prompt += template2.format(response=bot_response[:zeichenlimit]) #"{bot_response}</s> "
|
224 |
+
if message is not None: prompt += template1.format(message=message[:zeichenlimit]) #"[INST] {message} [/INST]"
|
225 |
+
if system2 is not None:
|
226 |
+
prompt += system2
|
227 |
+
return prompt
|
228 |
+
|
229 |
import gradio as gr
|
230 |
+
import requests
|
231 |
import json
|
232 |
+
from datetime import datetime
|
233 |
+
import os
|
234 |
+
import re
|
235 |
|
236 |
+
def response(message, history):
|
237 |
+
settings="Temporär"
|
238 |
+
|
239 |
+
# Preprocessing to revent simple forms of prompt injection:
|
240 |
+
#----------------------------------------------------------
|
241 |
+
|
242 |
+
message=message.replace("[INST]","")
|
243 |
+
message=message.replace("[/INST]","")
|
244 |
+
message=re.sub("<[|](im_start|im_end|end_of_turn)[|]>", '', message)
|
245 |
+
|
246 |
+
# Load Memory if settings=="Permanent"
|
247 |
+
#-------------------------------------
|
248 |
+
if (settings=="Permanent"):
|
249 |
+
if((len(history)==0)&(os.path.isfile(filename))): history=json.load(open(filename,'r',encoding="utf-8")) # retrieve history (if available)
|
250 |
+
|
251 |
+
system="Du bist ein deutschsprachiges KI-basiertes Assistenzsystem."
|
252 |
+
|
253 |
+
#RAG-layer 0: Intention-RAG
|
254 |
+
#---------------------------
|
255 |
+
typeResults=collection.query(
|
256 |
+
query_texts=[message],
|
257 |
+
n_results=4,
|
258 |
+
where={"type": "0"}
|
259 |
+
)
|
260 |
+
myType=typeResults["metadatas"][0][0]["type2"] # einfachste Variante
|
261 |
+
x=[x["type2"] for x in typeResults["metadatas"][0]] # liste die type2-Einträge auf
|
262 |
+
myType="1a" if ((x.index("1a") if "1a" in x else len(x)+1) < (x.index("!1a") if "!1a" in x else len(x)+1)) else "else" # setze 1a wenn es besser passt als !1a
|
263 |
+
if ((x.index("1b") if "1b" in x else len(x)+1) < (x.index("1a") if "1a" in x else len(x)+1)): # prüfe 1b wenn 1b besser passt als 1a
|
264 |
+
if ((x.index("1b") if "1b" in x else len(x)+1) < (x.index("!1b") if "!1b" in x else len(x)+1)): myType="1b" # setze 1b wenn besser als !1b (sonst lass 1a/else)
|
265 |
+
|
266 |
+
print("Message:"+message+"\n\nIntention-Type: "+myType+"\n\n"+str(typeResults))
|
267 |
+
|
268 |
+
#RAG-layer 1: Respond with CustomDB-RAG (1a, 1b) or Memory-RAG
|
269 |
+
#--------------------------------------------------------------
|
270 |
+
rag=None
|
271 |
+
historylimit=4
|
272 |
+
combination=None
|
273 |
+
|
274 |
+
## RAG 1a: Respond with CustomDB-RAG
|
275 |
+
#-----------------------------------
|
276 |
+
if(myType=="1a"):
|
277 |
+
|
278 |
+
RAGResults=collection.query(
|
279 |
+
query_texts=[message],
|
280 |
n_results=2,
|
281 |
+
where={"type": myType}
|
282 |
#where_document={"$contains":"search_string"}
|
283 |
)
|
284 |
+
dists=["<br><small>(relevance: "+str(round((1-d)*100)/100)+";" for d in RAGResults['distances'][0]]
|
285 |
+
sources=["source: "+s["source"]+")</small>" for s in RAGResults['metadatas'][0]]
|
286 |
+
texts=RAGResults['documents'][0]
|
287 |
+
combination = zip(texts,dists,sources)
|
288 |
combination = [' '.join(triplets) for triplets in combination]
|
289 |
+
#print(combination)
|
290 |
+
rag="\n\n"
|
291 |
+
rag += "Mit Blick auf die aktuelle Äußerung des Users erinnerst du dich insb. an folgende KI-Verfahren aus unserer Datenbank:\n"
|
292 |
+
rag += str(texts)
|
293 |
+
rag += "\n\nIm Folgenden siehst du den jüngsten Dialog-Verlauf:"
|
294 |
+
|
295 |
+
else:
|
296 |
+
|
297 |
+
## RAG 1a: Respond with CustomDB-RAG
|
298 |
+
#-----------------------------------
|
299 |
+
if(myType=="1b"):
|
300 |
+
|
301 |
+
RAGResults=collection.query(
|
302 |
+
query_texts=[message],
|
303 |
+
n_results=2,
|
304 |
+
where={"type": myType}
|
305 |
+
#where_document={"$contains":"search_string"}
|
306 |
+
)
|
307 |
+
dists=["<br><small>(relevance: "+str(round((1-d)*100)/100)+";" for d in RAGResults['distances'][0]]
|
308 |
+
sources=["source: "+s["source"]+")</small>" for s in RAGResults['metadatas'][0]]
|
309 |
+
texts=RAGResults['documents'][0]
|
310 |
+
combination = zip(texts,dists,sources)
|
311 |
+
combination = [' '.join(triplets) for triplets in combination]
|
312 |
+
#print(combination)
|
313 |
+
rag="\n\n"
|
314 |
+
rag += "Beziehe dich in deiner Antwort AUSSCHLIEßLICH auf die folgenden Informationen:\n"
|
315 |
+
rag += str(texts)
|
316 |
+
rag += "\n\nIm Folgenden siehst du den jüngsten Dialog-Verlauf:"
|
317 |
+
|
318 |
+
## Else: Respond with Memory-RAG
|
319 |
+
#--------------------------------
|
320 |
+
else:
|
321 |
+
|
322 |
+
x=collection.get(include=[])["ids"]
|
323 |
+
if(len(x)>(historylimit*2)): # turn on RAG when the database contains entries that are not shown within historylimit
|
324 |
+
RAGResults=collection.query(
|
325 |
+
query_texts=[message],
|
326 |
+
n_results=1,
|
327 |
+
where={"type": "episode"}
|
328 |
+
)
|
329 |
+
texts=RAGResults["metadatas"][0][0]["dialog"] #str()
|
330 |
+
#print("Message: "+message+"\n\nBest Match: "+texts)
|
331 |
+
rag="\n\n"
|
332 |
+
rag += "Mit Blick auf die aktuelle Äußerung des Users erinnerst du dich insb. an folgende Episode aus eurem Dialog:\n"
|
333 |
+
rag += str(texts)
|
334 |
+
rag += "\n\nIm Folgenden siehst du den jüngsten Dialog-Verlauf:"
|
335 |
+
|
336 |
+
# Request Response from LLM:
|
337 |
+
system2=None # system2 can be used as fictive first words of the AI, which are not displayed or stored
|
338 |
+
print("RAG: "+rag)
|
339 |
+
print("System: "+system+"\n\nMessage: "+message)
|
340 |
+
prompt=extend_prompt(
|
341 |
+
message, # current message of the user
|
342 |
+
history, # complete history
|
343 |
+
system, # system prompt
|
344 |
+
rag, # RAG-component added to the system prompt
|
345 |
+
system2, # fictive first words of the AI (neither displayed nor stored)
|
346 |
+
historylimit=historylimit # number of past messages to consider for response to current message
|
347 |
+
)
|
348 |
+
print(prompt)
|
349 |
+
# url="https://afischer1985-wizardlm-13b-v1-2-q4-0-gguf.hf.space/v1/completions"
|
350 |
+
url="http://0.0.0.0:2600/v1/completions"
|
351 |
+
body={"prompt":prompt,"max_tokens":None, "echo":"False","stream":"True"} # e.g. Mixtral-Instruct
|
352 |
+
if("discolm_german_7b" in modelPath): body.update({"stop": ["<|im_end|>"]}) # fix stop-token of DiscoLM
|
353 |
+
response="" #+"("+myType+")\n"
|
354 |
+
buffer=""
|
355 |
+
print("URL: "+url)
|
356 |
+
print("User: "+message+"\nAI: ")
|
357 |
+
for text in requests.post(url, json=body, stream=True): #-H 'accept: application/json' -H 'Content-Type: application/json'
|
358 |
+
if buffer is None: buffer=""
|
359 |
+
buffer=str("".join(buffer))
|
360 |
+
# print("*** Raw String: "+str(text)+"\n***\n")
|
361 |
+
text=text.decode('utf-8')
|
362 |
+
if((text.startswith(": ping -")==False) & (len(text.strip("\n\r"))>0)): buffer=buffer+str(text)
|
363 |
+
# print("\n*** Buffer: "+str(buffer)+"\n***\n")
|
364 |
+
buffer=buffer.split('"finish_reason": null}]}')
|
365 |
+
if(len(buffer)==1):
|
366 |
+
buffer="".join(buffer)
|
367 |
+
pass
|
368 |
+
if(len(buffer)==2):
|
369 |
+
part=buffer[0]+'"finish_reason": null}]}'
|
370 |
+
if(part.lstrip('\n\r').startswith("data: ")): part=part.lstrip('\n\r').replace("data: ", "")
|
371 |
+
try:
|
372 |
+
part = str(json.loads(part)["choices"][0]["text"])
|
373 |
+
print(part, end="", flush=True)
|
374 |
+
response=response+part
|
375 |
+
buffer="" # reset buffer
|
376 |
+
except Exception as e:
|
377 |
+
print("Exception:"+str(e))
|
378 |
+
pass
|
379 |
+
yield response
|
380 |
+
if((myType=="1a")|(myType=="1b")): #add RAG-results to chat-output if appropriate
|
381 |
+
response=response+"\n\n<br><details><summary><strong>Sources</strong></summary><br><ul>"+ "".join(["<li>" + s + "</li>" for s in combination])+"</ul></details>"
|
382 |
+
yield response
|
383 |
+
history.append((message, response)) # add current dialog to history
|
384 |
+
# Store current state in DB if settings=="Permanent"
|
385 |
+
if (settings=="Permanent"):
|
386 |
+
x=collection.get(include=[])["ids"] # add current dialog to db
|
387 |
+
collection.add(
|
388 |
+
documents=[message,response],
|
389 |
+
metadatas=[
|
390 |
+
{ "source": "ICH", "dialog": f"ICH: {message.strip()}\n DU: {response.strip()}", "type":"episode"},
|
391 |
+
{ "source": "DU", "dialog": f"ICH: {message.strip()}\n DU: {response.strip()}", "type":"episode"}
|
392 |
+
],
|
393 |
+
ids=[str(len(x)+1),str(len(x)+2)]
|
394 |
+
)
|
395 |
+
json.dump(history,open(filename,'w',encoding="utf-8"),ensure_ascii=False)
|
396 |
+
|
397 |
+
gr.ChatInterface(
|
398 |
+
response,
|
399 |
+
chatbot=gr.Chatbot(value=[[None,"Herzlich willkommen! Ich bin ein KI-basiertes Assistenzsystem, das für jede Anfrage die am besten geeigneten KI-Tools empfiehlt.<br>Aktuell bin ich wenig mehr als eine Tech-Demo und kenne nur 7 KI-Modelle - also sei bitte nicht zu streng mit mir.<br>Was ist dein Anliegen?"]],render_markdown=True)
|
400 |
+
title="German AI-Interface to the Hugging Face Hub with advanced RAG",
|
401 |
+
#additional_inputs=[gr.Dropdown(["Permanent","Temporär"],value="Temporär",label="Dialog sichern?")]
|
402 |
+
).queue().launch(share=True) #False, server_name="0.0.0.0", server_port=7864)
|
403 |
+
print("Interface up and running!")
|
404 |
+
|