#########################################################################################
# Title:  German AI-Interface with advanced RAG
# Author: Andreas Fischer
# Date:   January 31st, 2023
# Last update: February 21st, 2024
##########################################################################################

#https://github.com/abetlen/llama-cpp-python/issues/306
#sudo apt install libclblast-dev
#CMAKE_ARGS="-DLLAMA_CLBLAST=on" FORCE_CMAKE=1 pip install llama-cpp-python --force-reinstall --upgrade --no-cache-dir -v

# Prepare resources
#-------------------
import torch
import gc
torch.cuda.empty_cache()
gc.collect()

import os
from datetime import datetime
global filename
filename=f"./{datetime.now().strftime('%Y%m%d')}_history.json" # where to store the history as json-file
if(os.path.exists(filename)==True): os.remove(filename) 

# Chroma-DB
#-----------
import os
import chromadb
dbPath = "/home/af/Schreibtisch/Code/gradio/Chroma/db" 
onPrem = True if(os.path.exists(dbPath)) else False 
if(onPrem==False): dbPath="/home/user/app/db"

onPrem=False
print(dbPath)

#client = chromadb.Client()
path=dbPath
client = chromadb.PersistentClient(path=path)
print(client.heartbeat()) 
print(client.get_version())  
print(client.list_collections()) 
from chromadb.utils import embedding_functions
default_ef = embedding_functions.DefaultEmbeddingFunction()
#sentence_transformer_ef = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="T-Systems-onsite/cross-en-de-roberta-sentence-transformer")
#instructor_ef = embedding_functions.InstructorEmbeddingFunction(model_name="hkunlp/instructor-large", device="cuda")
embeddingModel = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="T-Systems-onsite/cross-en-de-roberta-sentence-transformer", device="cuda" if(onPrem) else "cpu")

print(str(client.list_collections()))

global collection
dbName="myDB"
if("name="+dbName in str(client.list_collections())): client.delete_collection(name=dbName)

if("name="+dbName in str(client.list_collections())):
  print(dbName+" found!")
  collection = client.get_collection(name=dbName, embedding_function=embeddingModel )
else:
  print(dbName+" created!")
  collection = client.create_collection(
    dbName,
    embedding_function=embeddingModel,
    metadata={"hnsw:space": "cosine"})
  # txts0: Intentions
  #------------------
  txts0=[
      "Ich suche ein KI-Programm mit bestimmten Fähigkeiten.",            # 1a
      #"Ich suche kein KI-Programm mit bestimmten Fähigkeiten.",          # !1a
      "Ich habe ein KI-Programm und habe Fragen zur Benutzung.",          # !1a (besser, um 1a und 1b abzugrenzen)
      "Ich habe ein KI-Programm und habe Fragen zur Benutzung.",          # 1b
      #"Ich habe kein KI-Programm und habe keine Fragen zur Benutzung.",  # !1b
      "Ich habe eine allgemeine Frage ohne KI-Bezug."                     # !1b (greift besser bei Alltagsfragen)
    ]
  # txts1a: RAG-Infos for first intention:
  #---------------------------------------
  txts1a=[
      "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.",
      "Image generating AI model stabilityai/sdxl-turbo: Suitable for image generation, e.g., illustrations, graphics, AI art, etc.",
      "Audio transcribing AI model openai/whisper-large-v3: Suitable for audio-transcription in different languages",
      "Speech synthesizing AI model coqui/XTTS-v2: Suitable for generating audio from text and for voice-cloning",
      "Code generating AI model deepseek-ai/deepseek-coder-6.7b-instruct: Suitable for programming in Python, JavaScript, PHP, Bash and many other programming languages.",
      "Translation AI model Helsinki-NLP/opus-mt: Suitable for translating text, e.g., from English to German or vice versa",
      "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"
    ]
  # txts1b: RAG-Infos for second intention
  #----------------------------------------
  txts1b=[
    "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)."
    ]
  #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)
  meta = []
  for _ in range(len(txts0)):
    meta.append({"type":"0", "type2":"0","source":"AF"})
  for _ in range(len(txts1a)):
    meta.append({"type":"1a","type2":"0","source":"AF"})
  for _ in range(len(txts1b)):
    meta.append({"type":"1b","type2":"0","source":"AF"})
  
  #Change type2 for txt0-entries
  #-----------------------------
  meta[0]["type2"]="1a" # RAG mit txts1a 
  meta[1]["type2"]="!1a" # else
  meta[2]["type2"]="1b" # RAG mit txts1b
  meta[3]["type2"]="!1b" # else 
  txts=txts0+txts1a+txts1b
  collection.add(
    documents=txts,     
    ids=[str(i) for i in list(range(len(txts)))],
    metadatas=meta
  )
  
  # Add entry to episodic memory
  x=collection.get(include=[])["ids"]
  if(True): #len(x)==0):
    message="Ich bin der User."
    response="Hallo User, wie kann ich dienen?"
    x=collection.get(include=[])["ids"]
    collection.add(
      documents=[message,response], 
      metadatas=[
        {"source": "ICH", "dialog": f"ICH: {message}\nDU: {response}", "type":"episode"},
        {"source": "DU",  "dialog": f"ICH: {message}\nDU: {response}", "type":"episode"}
      ], 
      ids=[str(len(x)+1),str(len(x)+2)] 
    )
    RAGResults=collection.query(
      query_texts=[message],
      n_results=1,
      #where={"source": "USER"}
    )
    RAGResults["metadatas"][0][0]["dialog"]

x=collection.get(include=[])["ids"]
x
collection.get() # Inspect db-entries

print("Database ready!")
print(collection.count()) 

rag0=collection.query(
  query_texts=[message],
  n_results=4,
  where={"type": "0"}
  )
x=rag0["metadatas"][0][0]["type2"]
x=[x["type2"] for x in rag0["metadatas"][0]]
x.index("1c") if "1c" in x else len(x)+1



# Model
#-------
#onPrem=False

if(onPrem==False): 
  modelPath="mistralai/Mixtral-8x7B-Instruct-v0.1"
  from huggingface_hub import InferenceClient
  import gradio as gr
  client = InferenceClient(
    modelPath
    #"mistralai/Mixtral-8x7B-Instruct-v0.1"
    #"mistralai/Mistral-7B-Instruct-v0.1"
  )
else:
  import os
  import requests
  import subprocess
  modelPath="/home/af/gguf/models/discolm_german_7b_v1.Q4_0.gguf"
  if(os.path.exists(modelPath)==False):
    #url="https://huggingface.co/TheBloke/WizardLM-13B-V1.2-GGUF/resolve/main/wizardlm-13b-v1.2.Q4_0.gguf"
    #url="https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF/resolve/main/mixtral-8x7b-instruct-v0.1.Q4_0.gguf?download=true"
    #url="https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/resolve/main/mistral-7b-instruct-v0.2.Q4_0.gguf?download=true"
    url="https://huggingface.co/TheBloke/DiscoLM_German_7b_v1-GGUF/resolve/main/discolm_german_7b_v1.Q4_0.gguf?download=true"
    response = requests.get(url)
    with open("./model.gguf", mode="wb") as file:
      file.write(response.content)
    print("Model downloaded")  
    modelPath="./model.gguf"
  print(modelPath)
  n="20" 
  if("mixtral-8x7b-instruct" in modelPath): n="0" # mixtral seems to cause problems here...
  command = ["python3", "-m", "llama_cpp.server", "--model", modelPath, "--host", "0.0.0.0", "--port", "2600", "--n_threads", "8", "--n_gpu_layers", n]
  subprocess.Popen(command)
  print("Server ready!")


# Gradio-GUI
#------------

def extend_prompt(message="", history=None, system=None, RAGAddon=None, system2=None, zeichenlimit=None,historylimit=4): #float("Inf")
  if zeichenlimit is None: zeichenlimit=1000000000 # :-)
  template0="[INST] {system} [/INST]</s>" #<s>
  template1="[INST] {message} [/INST] "
  template2="{response}</s>"
  if("mixtral-8x7b-instruct" in modelPath): # https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1
    template0="[INST] {system} [/INST]</s>" #<s>
    template1="[INST] {message} [/INST] "
    template2="{response}</s>"
  if("Mistral-7B-Instruct" in modelPath): #https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2
    template0="[INST] {system} [/INST]</s>" #<s>
    template1="[INST] {message} [/INST] "
    template2="{response}</s>"
  if("openchat-3.5" in modelPath): #https://huggingface.co/TheBloke/openchat-3.5-0106-GGUF
    template0="GPT4 Correct User: {system}<|end_of_turn|>GPT4 Correct Assistant: Okay.<|end_of_turn|>"
    template1="GPT4 Correct User: {message}<|end_of_turn|>GPT4 Correct Assistant: "
    template2="{response}<|end_of_turn|>"
  if("SauerkrautLM-7b-HerO" in modelPath):  #https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-HerO
    template0="<|im_start|>system\n{system}<|im_end|>\n"
    template1="<|im_start|>user\n{message}<|im_end|>\n<|im_start|>assistant\n"
    template2="{response}<|im_end|>\n"
  if("discolm_german_7b" in modelPath): #https://huggingface.co/DiscoResearch/DiscoLM_German_7b_v1
    template0="<|im_start|>system\n{system}<|im_end|>\n"
    template1="<|im_start|>user\n{message}<|im_end|>\n<|im_start|>assistant\n"
    template2="{response}<|im_end|>\n"
  if("WizardLM-13B-V1.2" in modelPath): #https://huggingface.co/WizardLM/WizardLM-13B-V1.2
    template0="{system} " #<s>
    template1="USER: {message} ASSISTANT: "
    template2="{response}</s>"
  if("phi-2" in modelPath): #https://huggingface.co/TheBloke/phi-2-GGUF
    template0="Instruct: {system}\nOutput: Okay.\n"
    template1="Instruct: {message}\nOutput:"
    template2="{response}\n"  
  prompt = ""
  if RAGAddon is not None:
    system += RAGAddon
  if system is not None:
    prompt += template0.format(system=system) #"<s>"
  if history is not None:
    for user_message, bot_response in history[-historylimit:]:
      if user_message is not None: prompt += template1.format(message=user_message[:zeichenlimit])  #"[INST] {user_prompt} [/INST] "
      if bot_response is not None: prompt += template2.format(response=bot_response[:zeichenlimit]) #"{bot_response}</s> "
  if message is not None: prompt += template1.format(message=message[:zeichenlimit])                #"[INST] {message} [/INST]"
  if system2 is not None:
    prompt += system2
  return prompt

import gradio as gr
import requests
import json
from datetime import datetime
import os
import re

def response(message, history):
  settings="Temporär"
  
  # Preprocessing to revent simple forms of prompt injection:
  #----------------------------------------------------------
  
  message=message.replace("[INST]","")
  message=message.replace("[/INST]","")
  message=re.sub("<[|](im_start|im_end|end_of_turn)[|]>", '', message)
  
  # Load Memory if settings=="Permanent"
  #-------------------------------------
  if (settings=="Permanent"):
    if((len(history)==0)&(os.path.isfile(filename))): history=json.load(open(filename,'r',encoding="utf-8")) # retrieve history (if available)
  
  system="Du bist ein deutschsprachiges wortkarges KI-basiertes Assistenzsystem. Fasse dich kurz und verzichte auf Codebeispiele."
  
  #RAG-layer 0: Intention-RAG
  #---------------------------
  typeResults=collection.query(
    query_texts=[message],
    n_results=4,
    where={"type": "0"}
  )
  myType=typeResults["metadatas"][0][0]["type2"] # einfachste Variante
  x=[x["type2"] for x in typeResults["metadatas"][0]] # liste die type2-Einträge auf
  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
  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
    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)
  
  print("Message:"+message+"\n\nIntention-Type: "+myType+"\n\n"+str(typeResults))
  
  #RAG-layer 1: Respond with CustomDB-RAG (1a, 1b) or Memory-RAG
  #--------------------------------------------------------------
  rag=None
  historylimit=4
  combination=None
  
  ## RAG 1a: Respond with CustomDB-RAG 
  #-----------------------------------
  if(myType=="1a"):  
    
    RAGResults=collection.query(
      query_texts=[message],
      n_results=2,
      where={"type": myType}
      #where_document={"$contains":"search_string"}
    )
    dists=["<br><small>(relevance: "+str(round((1-d)*100)/100)+";" for d in RAGResults['distances'][0]]
    sources=["source: "+s["source"]+")</small>" for s in RAGResults['metadatas'][0]]
    texts=RAGResults['documents'][0]
    combination = zip(texts,dists,sources)
    combination = [' '.join(triplets) for triplets in combination]
    #print(combination)
    rag="\n\n"
    rag += "Mit Blick auf die aktuelle Äußerung des Users erinnerst du dich insb. an folgende KI-Verfahren aus unserer Datenbank:\n"
    rag += str(texts)
    rag += "\n\nIm Folgenden siehst du den jüngsten Dialog-Verlauf:"
    
  else:
    
    ## RAG 1a: Respond with CustomDB-RAG 
    #-----------------------------------
    if(myType=="1b"):  
    
      RAGResults=collection.query(
        query_texts=[message],
        n_results=2,
        where={"type": myType}
        #where_document={"$contains":"search_string"}
      )
      dists=["<br><small>(relevance: "+str(round((1-d)*100)/100)+";" for d in RAGResults['distances'][0]]
      sources=["source: "+s["source"]+")</small>" for s in RAGResults['metadatas'][0]]
      texts=RAGResults['documents'][0]
      combination = zip(texts,dists,sources)
      combination = [' '.join(triplets) for triplets in combination]
      #print(combination)
      rag="\n\n"
      rag += "Beziehe dich in deiner Fortsetzung des Dialogs AUSSCHLIEßLICH auf die folgenden Informationen und gebe keine weiteren Informationen heraus:\n"
      rag += str(texts)
      rag += "\n\nIm Folgenden siehst du den jüngsten Dialog-Verlauf:"
      
    ## Else: Respond with Memory-RAG
    #--------------------------------    
    else: 
  
      x=collection.get(include=[])["ids"]
      if(len(x)>(historylimit*2)): # turn on RAG when the database contains entries that are not shown within historylimit 
        RAGResults=collection.query(
          query_texts=[message],
          n_results=1,
          where={"type": "episode"}
        )
        texts=RAGResults["metadatas"][0][0]["dialog"] #str()
        #print("Message: "+message+"\n\nBest Match: "+texts)
        rag="\n\n"
        rag += "Mit Blick auf die aktuelle Äußerung des Users erinnerst du dich insb. an folgende Episode aus eurem Dialog:\n"
        rag += str(texts) 
        rag += "\n\nIm Folgenden siehst du den jüngsten Dialog-Verlauf:"
  
  # Request Response from LLM:   
  system2=None # system2 can be used as fictive first words of the AI, which are not displayed or stored
  #print("RAG: "+rag)  
  #print("System: "+system+"\n\nMessage: "+message)
  prompt=extend_prompt(
    message,                  # current message of the user
    history,                  # complete history 
    system,                   # system prompt
    rag,                      # RAG-component added to the system prompt
    system2,                  # fictive first words of the AI (neither displayed nor stored)
    historylimit=historylimit # number of past messages to consider for response to current message
    )
  print("\n\n*** Prompt:\n"+prompt+"\n***\n\n")
  
  ## Request response from model
  #------------------------------
  
  print("AI running on prem!" if(onPrem) else "AI running HFHub!")
  if(onPrem==False):
    temperature=float(0.9) 
    max_new_tokens=500 
    top_p=0.95 
    repetition_penalty=1.0
    if temperature < 1e-2: temperature = 1e-2
    top_p = float(top_p)
    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        do_sample=True,
        seed=42,
    )
    stream = client.text_generation(prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
    response = ""
    #print("User: "+message+"\nAI: ")
    for text in stream:
        part=text.token.text
        #print(part, end="", flush=True)
        response += part
        yield response
    if((myType=="1a")): #add RAG-results to chat-output if appropriate 
      response2=response+"\n\n<br><details><summary><strong>Sources</strong></summary><br><ul>"+ "".join(["<li>" + s + "</li>" for s in combination])+"</ul></details>" 
      yield response2
    history.append((message, response)) # add current dialog to history
    # Store current state in DB if settings=="Permanent"
    if (settings=="Permanent"):
      x=collection.get(include=[])["ids"] # add current dialog to db
      collection.add(
        documents=[message,response], 
        metadatas=[
          { "source": "ICH", "dialog": f"ICH: {message.strip()}\n DU: {response.strip()}", "type":"episode"},
          { "source": "DU",  "dialog": f"ICH: {message.strip()}\n DU: {response.strip()}", "type":"episode"}
        ], 
        ids=[str(len(x)+1),str(len(x)+2)] 
      )
      json.dump(history,open(filename,'w',encoding="utf-8"),ensure_ascii=False)
 
  if(onPrem==True):
    # url="https://afischer1985-wizardlm-13b-v1-2-q4-0-gguf.hf.space/v1/completions"
    url="http://0.0.0.0:2600/v1/completions"  
    body={"prompt":prompt,"max_tokens":None, "echo":"False","stream":"True"}      # e.g. Mixtral-Instruct
    if("discolm_german_7b" in modelPath): body.update({"stop": ["<|im_end|>"]})   # fix stop-token of DiscoLM
    response="" #+"("+myType+")\n"
    buffer=""
    #print("URL: "+url)
    #print("User: "+message+"\nAI: ")
    for text in requests.post(url, json=body, stream=True):  #-H 'accept: application/json' -H 'Content-Type: application/json'
      if buffer is None: buffer=""
      buffer=str("".join(buffer))
      # print("*** Raw String: "+str(text)+"\n***\n")
      text=text.decode('utf-8')
      if((text.startswith(": ping -")==False) & (len(text.strip("\n\r"))>0)): buffer=buffer+str(text)
      # print("\n*** Buffer: "+str(buffer)+"\n***\n") 
      buffer=buffer.split('"finish_reason": null}]}')
      if(len(buffer)==1):
        buffer="".join(buffer)
        pass
      if(len(buffer)==2):
        part=buffer[0]+'"finish_reason": null}]}'  
        if(part.lstrip('\n\r').startswith("data: ")): part=part.lstrip('\n\r').replace("data: ", "")
        try: 
          part = str(json.loads(part)["choices"][0]["text"])
          #print(part, end="", flush=True)
          response=response+part
          buffer="" # reset buffer
        except Exception as e:
          print("Exception:"+str(e))
          pass
      yield response
    if((myType=="1a")): #add RAG-results to chat-output if appropriate 
      response2=response+"\n\n<br><details><summary><strong>Sources</strong></summary><br><ul>"+ "".join(["<li>" + s + "</li>" for s in combination])+"</ul></details>" 
      yield response2
    history.append((message, response)) # add current dialog to history
    # Store current state in DB if settings=="Permanent"
    if (settings=="Permanent"):
      x=collection.get(include=[])["ids"] # add current dialog to db
      collection.add(
        documents=[message,response], 
        metadatas=[
          { "source": "ICH", "dialog": f"ICH: {message.strip()}\n DU: {response.strip()}", "type":"episode"},
          { "source": "DU",  "dialog": f"ICH: {message.strip()}\n DU: {response.strip()}", "type":"episode"}
        ], 
        ids=[str(len(x)+1),str(len(x)+2)] 
      )
      json.dump(history,open(filename,'w',encoding="utf-8"),ensure_ascii=False)


gr.ChatInterface(
  response, 
  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.<ul><li>wenn du ein KI-Modell suchst, antworte ich auf Basis der Liste</li><li>wenn du Fragen zur Benutzung eines KI-Modells hast, verweise ich an andere Stellen</li><li>wenn du andre Fragen hast, antworte ich frei und berücksichtige dabei Relevantes aus dem gesamten bisherigen Dialog.</li></ul><br>Was ist dein Anliegen?"]],render_markdown=True),
  title="German AI-Interface with advanced RAG",
  #additional_inputs=[gr.Dropdown(["Permanent","Temporär"],value="Temporär",label="Dialog sichern?")]
  ).queue().launch(share=True) #False, server_name="0.0.0.0", server_port=7864)
print("Interface up and running!")