import gradio as gr
import transformers 
from transformers import AutoTokenizer, AutoModelForCausalLM
import accelerate 
import einops 
import langchain
import xformers
import os
import bitsandbytes
import sentence_transformers
import huggingface_hub
import torch
from torch import cuda, bfloat16
from transformers import StoppingCriteria, StoppingCriteriaList
from langchain.llms import HuggingFacePipeline
from langchain.document_loaders import TextLoader, DirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains import ConversationalRetrievalChain
from huggingface_hub import InferenceClient

# Login to Hugging Face using a token
# huggingface_hub.login(HF_TOKEN)

"""
Loading of the LLama3 model
"""
HF_TOKEN = os.environ.get("HF_TOKEN", None)
model_id = 'meta-llama/Meta-Llama-3-8B'
device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'


"""set quantization configuration to load large model with less GPU memory
this requires the `bitsandbytes` library"""
bnb_config = transformers.BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type='nf4',
    bnb_4bit_use_double_quant=True,
    bnb_4bit_compute_dtype=bfloat16
)

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct",token=HF_TOKEN)
model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct", device_map="auto",token=HF_TOKEN,quantization_config=bnb_config)  # to("cuda:0") 
terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

"""CPU"""

# model_config = transformers.AutoConfig.from_pretrained(
#             model_id,
#             token=HF_TOKEN,
#             # use_auth_token=hf_auth
#         )
# model = transformers.AutoModelForCausalLM.from_pretrained(
#             model_id,
#             trust_remote_code=True,
#             config=model_config,
#             # quantization_config=bnb_config,
#             token=HF_TOKEN,
#             # use_auth_token=hf_auth
#         )
# model.eval()
# tokenizer = transformers.AutoTokenizer.from_pretrained(
#             model_id,
#             token=HF_TOKEN,
#             # use_auth_token=hf_auth
#         )
# generate_text = transformers.pipeline(
#             model=self.model, tokenizer=self.tokenizer,
#             return_full_text=True,
#             task='text-generation',
#             temperature=0.01,
#             max_new_tokens=512
#         )

"""
Setting up the stop list to define stopping criteria.
"""

stop_list = ['\nHuman:', '\n```\n']

stop_token_ids = [tokenizer(x)['input_ids'] for x in stop_list]
stop_token_ids = [torch.LongTensor(x).to(device) for x in stop_token_ids]


# define custom stopping criteria object
class StopOnTokens(StoppingCriteria):
    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
        for stop_ids in stop_token_ids:
            if torch.eq(input_ids[0][-len(stop_ids):], stop_ids).all():
                return True
        return False

stopping_criteria = StoppingCriteriaList([StopOnTokens()])


generate_text = transformers.pipeline(
    model=model,
    tokenizer=tokenizer,
    return_full_text=True,  # langchain expects the full text
    task='text-generation',
    # we pass model parameters here too
    stopping_criteria=stopping_criteria,  # without this model rambles during chat
    temperature=0.1,  # 'randomness' of outputs, 0.0 is the min and 1.0 the max
    max_new_tokens=512,  # max number of tokens to generate in the output
    repetition_penalty=1.1  # without this output begins repeating
)

llm = HuggingFacePipeline(pipeline=generate_text)

loader = DirectoryLoader('data/text/', loader_cls=TextLoader)
documents = loader.load()
print('len of documents are',len(documents))

text_splitter = RecursiveCharacterTextSplitter(chunk_size=5000, chunk_overlap=250)
all_splits = text_splitter.split_documents(documents)

model_name = "sentence-transformers/all-mpnet-base-v2"
model_kwargs = {"device": "cuda"}

embeddings = HuggingFaceEmbeddings(model_name=model_name, model_kwargs=model_kwargs)

# storing embeddings in the vector store
vectorstore = FAISS.from_documents(all_splits, embeddings)

chain = ConversationalRetrievalChain.from_llm(llm, vectorstore.as_retriever(), return_source_documents=True)

chat_history = []

def format_prompt(query):
    # Construct a clear and structured prompt to guide the LLM's response
    prompt = f"""
    You are a knowledgeable assistant with access to a comprehensive database. 
    I need you to answer my question and provide related information in a specific format.

    Here's what I need:
    1. A brief, general response to my question.
    2. A JSON-formatted output containing:
       - "question": The original question.
       - "answer": The detailed answer.
       - "related_questions": A list of related questions and their answers, each as a dictionary with the keys:
         - "question": The related question.
         - "answer": The related answer.

    Here's my question:
    {query}
    """
    return prompt


def qa_infer(query):
    formatted_prompt = format_prompt(query)
    result = chain({"question": formatted_prompt, "chat_history": chat_history})
    return result['answer']

# query = "What` is the best TS pin configuration for BQ24040 in normal battery charge mode"
# qa_infer(query)

EXAMPLES = ["What is the best TS pin configuration for BQ24040 in normal battery charge mode", 
            "Can BQ25896 support I2C interface?", 
            "Can you please provide me with Gerber/CAD file for UCC2897A"]

demo = gr.Interface(fn=qa_infer, inputs="text",allow_flagging='never', examples=EXAMPLES,
                    cache_examples=False,outputs="text")

# launch the app!
#demo.launch(enable_queue = True,share=True)
#demo.queue(default_enabled=True).launch(debug=True,share=True)
demo.launch()