# from huggingface_hub import InferenceClient
# import gradio as gr

# client = InferenceClient(
#     "mistralai/Mixtral-8x7B-Instruct-v0.1"
# )


# def format_prompt(message, history):
#   prompt = "<s>"
#   for user_prompt, bot_response in history:
#     prompt += f"[INST] {user_prompt} [/INST]"
#     prompt += f" {bot_response}</s> "
#   prompt += f"[INST] {message} [/INST]"
#   return prompt

# def generate(
#     prompt, history, system_prompt, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,
# ):
#     temperature = float(temperature)
#     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,
#     )

#     formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history)
#     stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
#     output = ""

#     for response in stream:
#         output += response.token.text
#         yield output
#     return output


# additional_inputs=[
#     gr.Textbox(
#         label="System Prompt",
#         max_lines=1,
#         interactive=True,
#     ),
#     gr.Slider(
#         label="Temperature",
#         value=0.9,
#         minimum=0.0,
#         maximum=1.0,
#         step=0.05,
#         interactive=True,
#         info="Higher values produce more diverse outputs",
#     ),
#     gr.Slider(
#         label="Max new tokens",
#         value=256,
#         minimum=0,
#         maximum=1048,
#         step=64,
#         interactive=True,
#         info="The maximum numbers of new tokens",
#     ),
#     gr.Slider(
#         label="Top-p (nucleus sampling)",
#         value=0.90,
#         minimum=0.0,
#         maximum=1,
#         step=0.05,
#         interactive=True,
#         info="Higher values sample more low-probability tokens",
#     ),
#     gr.Slider(
#         label="Repetition penalty",
#         value=1.2,
#         minimum=1.0,
#         maximum=2.0,
#         step=0.05,
#         interactive=True,
#         info="Penalize repeated tokens",
#     )
# ]

# examples=[["I'm planning a vacation to Japan. Can you suggest a one-week itinerary including must-visit places and local cuisines to try?", None, None, None, None, None, ],
#           ["Can you write a short story about a time-traveling detective who solves historical mysteries?", None, None, None, None, None,],
#           ["I'm trying to learn French. Can you provide some common phrases that would be useful for a beginner, along with their pronunciations?", None, None, None, None, None,],
#           ["I have chicken, rice, and bell peppers in my kitchen. Can you suggest an easy recipe I can make with these ingredients?", None, None, None, None, None,],
#           ["Can you explain how the QuickSort algorithm works and provide a Python implementation?", None, None, None, None, None,],
#           ["What are some unique features of Rust that make it stand out compared to other systems programming languages like C++?", None, None, None, None, None,],
#          ]

# gr.ChatInterface(
#     fn=generate,
#     chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
#     additional_inputs=additional_inputs,
#     title="Mixtral 46.7B",
#     examples=examples,
#     concurrency_limit=20,
# ).launch(show_api= True)


import os
import gradio as gr
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import HuggingFaceBgeEmbeddings
from langchain.vectorstores import FAISS
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
from huggingface_hub import InferenceClient

# Set the Hugging Face Hub API token
os.environ["HUGGINGFACEHUB_API_TOKEN"] = st.secrets['huggingface_token']

# Initialize the InferenceClient
client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")

def get_pdf_text(pdf_docs):
    text = ""
    for pdf in pdf_docs:
        pdf_reader = PdfReader(pdf)
        for page in pdf_reader.pages:
            text += page.extract_text()
    return text

def get_text_chunks(text):
    text_splitter = CharacterTextSplitter(
        separator="\n", chunk_size=1500, chunk_overlap=300, length_function=len
    )
    chunks = text_splitter.split_text(text)
    return chunks

def get_vectorstore(text_chunks):
    model = "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
    encode_kwargs = {"normalize_embeddings": True}
    embeddings = HuggingFaceBgeEmbeddings(
        model_name=model, encode_kwargs=encode_kwargs, model_kwargs={"device": "cpu"}
    )
    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
    return vectorstore

def format_prompt(message, history):
    prompt = "<s>"
    for user_prompt, bot_response in history:
        prompt += f"[INST] {user_prompt} [/INST]"
        prompt += f" {bot_response}</s> "
    prompt += f"[INST] {message} [/INST]"
    return prompt

def generate(prompt, history, system_prompt, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0):
    temperature = float(temperature)
    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,
    )

    formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history)
    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
    output = ""

    for response in stream:
        output += response.token.text
        yield output
    return output

def main(pdf_docs):
    # get pdf text
    raw_text = get_pdf_text(pdf_docs)

    # get the text chunks
    text_chunks = get_text_chunks(raw_text)

    # create vector store
    vectorstore = get_vectorstore(text_chunks)

    # create conversation chain
    conversation_chain = get_conversation_chain(vectorstore)

    additional_inputs=[
        gr.Textbox(
            label="System Prompt",
            max_lines=1,
            interactive=True,
        ),
        gr.Slider(
            label="Temperature",
            value=0.9,
            minimum=0.0,
            maximum=1.0,
            step=0.05,
            interactive=True,
            info="Higher values produce more diverse outputs",
        ),
        gr.Slider(
            label="Max new tokens",
            value=256,
            minimum=0,
            maximum=1048,
            step=64,
            interactive=True,
            info="The maximum numbers of new tokens",
        ),
        gr.Slider(
            label="Top-p (nucleus sampling)",
            value=0.90,
            minimum=0.0,
            maximum=1,
            step=0.05,
            interactive=True,
            info="Higher values sample more low-probability tokens",
        ),
        gr.Slider(
            label="Repetition penalty",
            value=1.2,
            minimum=1.0,
            maximum=2.0,
            step=0.05,
            interactive=True,
            info="Penalize repeated tokens",
        )
    ]

    examples=[["I'm planning a vacation to Japan. Can you suggest a one-week itinerary including must-visit places and local cuisines to try?", None, None, None, None, None, ],
              ["Can you write a short story about a time-traveling detective who solves historical mysteries?", None, None, None, None, None,],
              ["I'm trying to learn French. Can you provide some common phrases that would be useful for a beginner, along with their pronunciations?", None, None, None, None, None,],
              ["I have chicken, rice, and bell peppers in my kitchen. Can you suggest an easy recipe I can make with these ingredients?", None, None, None, None, None,],
              ["Can you explain how the QuickSort algorithm works and provide a Python implementation?", None, None, None, None, None,],
              ["What are some unique features of Rust that make it stand out compared to other systems programming languages like C++?", None, None, None, None, None,],
             ]

    gr.ChatInterface(
        fn=generate,
        chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
        additional_inputs=additional_inputs,
        title="Mixtral 46.7B",
        examples=examples,
        concurrency_limit=20,
    ).launch(show_api= True)

if __name__ == "__main__":
    main([])