#########################################################################################
# Title:  Gradio AI-Interface with Memory-RAG
# Author: Andreas Fischer
# Date:   October 15th, 2023
# Last update: May 27th, 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=True  # uncomment to override automatic detection
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="historicalChromaDB1"

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

if("name="+dbName in str(client.list_collections())):
  print(dbName+" found!")
  collection = client.get_collection(name=dbName, embedding_function=embeddingModel) #sentence_transformer_ef)
else:
  #client.delete_collection(name=dbName)
  print(dbName+" created!")
  collection = client.create_collection(
    dbName,
    embedding_function=embeddingModel,
    metadata={"hnsw:space": "cosine"})

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

x=collection.get(include=[])["ids"]
if(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}"},
      {"source": "DU",  "dialog": f"ICH: {message}\nDU: {response}"}
    ], 
    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"]


# 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"
  modelPath="/home/af/gguf/models/Mixtral-8x7b-instruct-v0.1.Q4_0.gguf"
  if(os.path.exists(modelPath)==False):
    #url="https://huggingface.co/TheBloke/DiscoLM_German_7b_v1-GGUF/resolve/main/discolm_german_7b_v1.Q4_0.gguf?download=true"
    url="https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF/resolve/main/mixtral-8x7b-instruct-v0.1.Q4_0.gguf?download=true"
    response = requests.get(url)
    with open("./Mixtral-8x7b-instruct.gguf", mode="wb") as file:
      file.write(response.content)
    print("Model downloaded")  
    modelPath="./Mixtral-8x7b-instruct.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!")


#import llama_cpp
#llama_cpp.llama_backend_init(numa=False)
#params=llama_cpp.llama_context_default_params()
#params.n_ctx

# Gradio-GUI
#------------
import re
def extend_prompt(message="", history=None, system=None, RAGAddon=None, system2=None, zeichenlimit=None,historylimit=4, removeHTML=True): 
  startOfString=""
  if zeichenlimit is None: zeichenlimit=1000000000 # :-)
  template0=" [INST]{system}\n  [/INST] </s>" 
  template1=" [INST] {message} [/INST]"
  template2=" {response}</s>"
  if("command-r" in modelPath): #https://huggingface.co/CohereForAI/c4ai-command-r-v01
    ## <BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hello, how are you?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
    template0="<BOS_TOKEN><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|> {system}<|END_OF_TURN_TOKEN|>" 
    template1="<|START_OF_TURN_TOKEN|><|USER_TOKEN|>{message}<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>"
    template2="{response}<|END_OF_TURN_TOKEN|>"
  if("Gemma-" in modelPath): # https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1
    template0="<start_of_turn>user{system}</end_of_turn>" 
    template1="<start_of_turn>user{message}</end_of_turn><start_of_turn>model"
    template2="{response}</end_of_turn>"      
  if("Mixtral-8x22B-Instruct" in modelPath): # AutoTokenizer: <s>[INST] U1[/INST] A1</s>[INST] U2[/INST] A2</s>
    startOfString="<s>"
    template0="[INST]{system}\n  [/INST] </s>"  
    template1="[INST] {message}[/INST]"
    template2=" {response}</s>"
  if("Mixtral-8x7b-instruct" in modelPath): # https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1
    startOfString="<s>"                     # AutoTokenzizer: <s> [INST] U1 [/INST]A1</s> [INST] U2 [/INST]A2</s>
    template0=" [INST]{system}\n  [/INST] </s>"  
    template1=" [INST] {message} [/INST]"
    template2=" {response}</s>"
  if("Mistral-7B-Instruct" in modelPath): #https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2
    startOfString="<s>"
    template0="[INST]{system}\n [/INST]</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(("Discolm_german_7b" in modelPath) or ("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("Llama-3-SauerkrautLM-8b-Instruct" in modelPath):  #https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-HerO
    template0="<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n{system}<|eot_id|>"
    template1="<|start_header_id|>user<|end_header_id|>\n\n{message}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
    template2="{response}<|eot_id|>\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 None: user_message = "" 
      if bot_response is None: bot_response = ""
      bot_response = re.sub("\n\n<details(| open)>.*?</details>","", bot_response, flags=re.DOTALL) # remove RAG-compontents
      if removeHTML==True: bot_response = re.sub("<(.*?)>","\n", bot_response) # remove HTML-components in general (may cause bugs with markdown-rendering)
      if user_message is not None: prompt += template1.format(message=user_message[:zeichenlimit])  
      if bot_response is not None: prompt += template2.format(response=bot_response[:zeichenlimit]) 
  if message is not None: prompt += template1.format(message=message[:zeichenlimit])                
  if system2 is not None:
    prompt += system2
  return startOfString+prompt

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

def response(message, history,customSysPrompt,settings):
  #settings="Memory Off"
  removeHTML=True
  system=customSysPrompt
  message=message.replace("[INST]","")
  message=message.replace("[/INST]","")
  message=message.replace("</s>","")
  message=re.sub("<[|](im_start|im_end|end_of_turn)[|]>", '', message)
  if (settings=="Memory On"):
    if((len(history)==0)&(os.path.isfile(filename))): history=json.load(open(filename,'r',encoding="utf-8")) # retrieve history (if available)
  x=collection.get(include=[])["ids"]
  rag=None # RAG is turned off until history gets too long
  historylimit=0 #4
  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={"source": "USER"}
    )
    bestMatch=str(RAGResults["metadatas"][0][0]["dialog"])
    #print("Message: "+message+"\n\nBest Match: "+bestMatch)
    rag="\n\n"
    rag += "Mit Blick auf den aktuellen Stand des Dialogs erinnerst du dich insb. an folgende Episode:\n"
    rag += bestMatch 
    rag += "\n\nIm Folgenden siehst du den aktuellen Stand des Dialogs."
  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
    removeHTML=removeHTML     # remove HTML-components from History (to prevent bugs with Markdown)
    )
  #print("\n\nMESSAGE:"+str(message))
  #print("\n\nHISTORY:"+str(history))
  #print("\n\nSYSTEM:"+str(system))
  #print("\n\nRAG:"+str(rag))
  #print("\n\nSYSTEM2:"+str(system2))
  #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
        if removeHTML==True: response = re.sub("<(.*?)>","\n", response) # remove HTML-components in general (may cause bugs with markdown-rendering)
        yield response
    # Store current state in DB if memory is turned on
    if (settings=="Memory On"):
      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
    if("Gemma-" in modelPath): body.update({"stop": ["<|im_end|>","</end_of_turn>"]})   # fix stop-token of Gemma
    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
      if removeHTML==True: response = re.sub("<(.*?)>","\n", response) # remove HTML-components in general (may cause bugs with markdown-rendering)
      yield response
    history.append((message, response)) # add current dialog to history
    # Store current state in DB if memory is turned on
    if (settings=="Memory On"):
      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, mit dem du zu unterschiedlichen Themen sprechen kannst.<br>Wenn du mich lokal bzw. \"on premises\" betreibst, kann ich unseren Dialog auf Wunsch auch speichern und bei meinen Antworten auch lange zurück liegende Episoden berücksichtigen.<br>Was ist dein Anliegen?"]], render_markdown=True),
  title="AI-Interface (on prem)" if onPrem else "AI-Interface (HFHub)",
  additional_inputs=[
    gr.Textbox(value=None,label="System Prompt"),
    gr.Dropdown(["Memory On","Memory Off"],value="Memory Off",label="Memory")
  ]
  ).queue().launch(share=True) #False, server_name="0.0.0.0", server_port=7864)
print("Interface up and running!")