import json
import os
import gradio as gr
import time
from pydantic import BaseModel, Field
from typing import Any, Optional, Dict, List, Union
from huggingface_hub import InferenceClient
from langchain.llms.base import LLM, EmbeddingFunction, Embeddings
from langchain.Images import Images
from langchain.llms.base import LLM
from langchain.embeddings import HuggingFaceInstructEmbeddings

from langchain.Documents import Documents
from langchain.vectorstores import Chroma
from dotenv import load_dotenv
from transformers import AutoTokenizer, AutoModel, Tool

load_dotenv()

path_work = "."
hf_token = os.getenv("HF")

class HuggingFaceInstructEmbeddings(EmbeddingFunction):
    def __init__(self, model_name: str, model_kwargs: Optional[Dict[str, Any]] = None):
        self.model = AutoModel.from_pretrained(model_name, **(model_kwargs or {}))
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)

    def __call__(self, input: Union[Documents, Images]) -> Embeddings:
        if isinstance(input, Documents):
            texts = [doc.text for doc in input]
            embeddings = self._embed_text(texts)
        else:
            # Handle image embeddings if needed
            pass

        return embeddings

    def _embed_text(self, texts: List[str]) -> Embeddings:
        # Your existing logic for text embeddings using Hugging Face models...
        inputs = self.tokenizer(texts, return_tensors="pt", padding=True, truncation=True)
        with torch.no_grad():
            outputs = self.model(**inputs)
        embeddings = outputs.last_hidden_state.mean(dim=1)  # Adjust this based on your specific model

        return embeddings


vectordb = Chroma(
    persist_directory=path_work + '/new_papers',
    embedding_function=HuggingFaceInstructEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", model_kwargs={"device": "cpu"})
)

retriever = vectordb.as_retriever(search_kwargs={"k": 2})#5


class KwArgsModel(BaseModel):
    kwargs: Dict[str, Any] = Field(default_factory=dict)

class CustomInferenceClient(LLM, KwArgsModel):
    model_name: str
    inference_client: InferenceClient

    def __init__(self, model_name: str, hf_token: str, kwargs: Optional[Dict[str, Any]] = None):
        inference_client = InferenceClient(model=model_name, token=hf_token)
        super().__init__(
            model_name=model_name,
            hf_token=hf_token,
            kwargs=kwargs,
            inference_client=inference_client
        )

    def _call(
        self,
        prompt: str,
        stop: Optional[List[str]] = None
    ) -> str:
        if stop is not None:
            raise ValueError("stop kwargs are not permitted.")
        response_gen = self.inference_client.text_generation(prompt, **self.kwargs, stream=True)
        response = ''.join(response_gen)
        return response

    @property
    def _llm_type(self) -> str:
        return "custom"

    @property
    def _identifying_params(self) -> dict:
        return {"model_name": self.model_name}

kwargs = {"max_new_tokens": 256, "temperature": 0.9, "top_p": 0.6, "repetition_penalty": 1.3, "do_sample": True}

model_list = [
    "meta-llama/Llama-2-13b-chat-hf",
    "HuggingFaceH4/zephyr-7b-alpha",
    "meta-llama/Llama-2-70b-chat-hf",
    "tiiuae/falcon-180B-chat"
]

qa_chain = None

def load_model(model_selected):
    global qa_chain
    model_name = model_selected
    llm = CustomInferenceClient(model_name=model_name, hf_token=hf_token, kwargs=kwargs)

    from langchain.chains import RetrievalQA
    qa_chain = RetrievalQA.from_chain_type(
        llm=llm,
        chain_type="stuff",
        retriever=retriever,
        return_source_documents=True,
        verbose=True,
    )
    return qa_chain

load_model("meta-llama/Llama-2-70b-chat-hf")

##########
#####
#########


###
###
###

def predict(message, temperature=0.9, max_new_tokens=512, top_p=0.6, repetition_penalty=1.3):
    temperature = float(temperature)
    if temperature < 1e-2: temperature = 1e-2
    top_p = float(top_p)

    llm_response = qa_chain(message)
    res_result = llm_response['result']

    res_relevant_doc = [source.metadata['source'] for source in llm_response["source_documents"]]
    response = f"{res_result}" + "\n\n" + "[Answer Source Documents (Ctrl + Click!)] :" + "\n" + f" \n {res_relevant_doc}"
    print("response: =====> \n", response, "\n\n")
    tokens = response.split('\n')
    token_list = []
    for idx, token in enumerate(tokens):
        token_dict = {"id": idx + 1, "text": token}
        token_list.append(token_dict)
    response = {"data": {"token": token_list}}
    response = json.dumps(response, indent=4)

    response = json.loads(response)
    data_dict = response.get('data', {})
    token_list = data_dict.get('token', [])

    partial_message = ""
    for token_entry in token_list:
        if token_entry:
            try:
                token_id = token_entry.get('id', None)
                token_text = token_entry.get('text', None)

                if token_text:
                    for char in token_text:
                        partial_message += char
                        yield partial_message
                        time.sleep(0.01)
                else:
                    print(f"Warning ==> The key 'text' does not exist or is None in this token entry: {token_entry}")
                    pass

            except KeyError as e:
                gr.Warning(f"KeyError: {e} occurred for token entry: {token_entry}")
                continue

class TextGeneratorTool(Tool):
    name = "vector_retriever"
    description = "This tool searches in a vector store based on a given prompt."
    inputs = ["prompt"]
    outputs = ["generated_text"]


    def __init__(self):
        #self.retriever = db.as_retriever(search_kwargs={"k": 1})
        pass  # You might want to add some initialization logic here
		
    def __call__(self, prompt: str):
        result = predict(prompt,  0.9, 512, 0.6, 1.4)
        return result