from langchain.docstore.document import Document
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.retrievers import BM25Retriever

# Importing required libraries
import warnings
warnings.filterwarnings("ignore")
import datasets
import os
import json
import subprocess
import sys
import joblib
from llama_cpp import Llama
from llama_cpp_agent import LlamaCppAgent
from llama_cpp_agent import MessagesFormatterType
from llama_cpp_agent.providers import LlamaCppPythonProvider
from llama_cpp_agent.chat_history import BasicChatHistory
from llama_cpp_agent.chat_history.messages import Roles
import gradio as gr
from huggingface_hub import hf_hub_download
from typing import List, Tuple,Dict,Optional
from logger import logging
from exception import CustomExceptionHandling

from smolagents.gradio_ui import GradioUI
from smolagents import (
    CodeAgent,
    GoogleSearchTool,
    Model,
    Tool,
    LiteLLMModel,
    ToolCallingAgent,
    ChatMessage,tool,MessageRole
)

cache_file = "docs_processed.joblib"
if os.path.exists(cache_file):
    docs_processed = joblib.load(cache_file)
    print("Loaded docs_processed from cache.")
else:
    knowledge_base = datasets.load_dataset("m-ric/huggingface_doc", split="train")
    source_docs = [
        Document(page_content=doc["text"], metadata={"source": doc["source"].split("/")[1]}) for doc in knowledge_base
    ]

    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=1000,
        chunk_overlap=50,
        add_start_index=True,
        strip_whitespace=True,
        separators=["\n\n", "\n", ".", " ", ""],
    )
    docs_processed = text_splitter.split_documents(source_docs)
    joblib.dump(docs_processed, cache_file)
    print("Created and saved docs_processed to cache.")

class RetrieverTool(Tool):
    name = "retriever"
    description = "Uses semantic search to retrieve the parts of documentation that could be most relevant to answer your query."
    inputs = {
        "query": {
            "type": "string",
            "description": "The query to perform. This should be semantically close to your target documents. Use the affirmative form rather than a question.",
        }
    }
    output_type = "string"

    def __init__(self, docs, **kwargs):
        super().__init__(**kwargs)

        self.retriever = BM25Retriever.from_documents(
            docs,
            k=7,  
        )

    def forward(self, query: str) -> str:
        assert isinstance(query, str), "Your search query must be a string"

        docs = self.retriever.invoke(
            query,
        )
        return "\nRetrieved documents:\n" + "".join(
            [
                f"\n\n===== Document {str(i)} =====\n" + str(doc.page_content)
                for i, doc in enumerate(docs)
            ]
        )

retriever_tool = RetrieverTool(docs_processed)
# Download gguf model files
huggingface_token = os.getenv("HUGGINGFACE_TOKEN")

hf_hub_download(
    repo_id="bartowski/google_gemma-3-1b-it-GGUF",
    filename="google_gemma-3-1b-it-Q6_K.gguf",
    local_dir="./models",
)
hf_hub_download(
    repo_id="bartowski/google_gemma-3-1b-it-GGUF",
    filename="google_gemma-3-1b-it-Q5_K_M.gguf",
    local_dir="./models",
)

# Set the title and description
title = "Gemma Llama.cpp"
description = """Gemma 3 is a family of lightweight, multimodal open models that offers advanced capabilities like large context windows and multilingual support, enabling diverse applications on various devices."""


llm = None
llm_model = None


query_system = """
You are a query rewriter. Your task is to convert a user's question into a concise search query suitable for information retrieval.
The goal is to identify the most important keywords for a search engine.

Here are some examples:

User Question: What is transformer?
Search Query: transformer

User Question: How does a transformer model work in natural language processing?
Search Query: transformer model natural language processing

User Question: What are the advantages of using transformers over recurrent neural networks?
Search Query: transformer vs recurrent neural network advantages

User Question: Explain the attention mechanism in transformers.
Search Query: transformer attention mechanism

User Question: What are the different types of transformer architectures?
Search Query: transformer architectures

User Question: What is the history of the transformer model?
Search Query: transformer model history
"""
def to_query(provider,question):
    try:
        agent = LlamaCppAgent(
                provider,
                system_prompt=f"{query_system}",
                predefined_messages_formatter_type=MessagesFormatterType.GEMMA_2,
                debug_output=True,
            )

        message="""
Now, rewrite the following question:
User Question: %s
Search Query:
    """%question
        print("<message>")
        print(message)
        settings = provider.get_provider_default_settings()
        messages = BasicChatHistory()
        result = agent.get_chat_response(
                message,
                llm_sampling_settings=settings,
                chat_history=messages,
                returns_streaming_generator=False,
                print_output=False,
            )
        return result
    except Exception as e:
        # Custom exception handling
        raise CustomExceptionHandling(e, sys) from e

def respond(
    message: str,
    history: List[Tuple[str, str]],
    model: str,
    system_message: str,
    max_tokens: int,
    temperature: float,
    top_p: float,
    top_k: int,
    repeat_penalty: float,
):
    """
    Respond to a message using the Gemma3 model via Llama.cpp.

    Args:
        - message (str): The message to respond to.
        - history (List[Tuple[str, str]]): The chat history.
        - model (str): The model to use.
        - system_message (str): The system message to use.
        - max_tokens (int): The maximum number of tokens to generate.
        - temperature (float): The temperature of the model.
        - top_p (float): The top-p of the model.
        - top_k (int): The top-k of the model.
        - repeat_penalty (float): The repetition penalty of the model.

    Returns:
        str: The response to the message.
    """
    try:
        # Load the global variables
        global llm
        global llm_model

        # Load the model
        if llm is None or llm_model != model:
            llm = Llama(
                model_path=f"models/{model}",
                flash_attn=False,
                n_gpu_layers=0,
                n_batch=8,
                n_ctx=2048,
                n_threads=2,
                n_threads_batch=2,
            )
            llm_model = model
        provider = LlamaCppPythonProvider(llm)

        query = to_query(provider,message)
        print("<query>")
        print(f"from {message} to {query}")
        text = retriever_tool(query=f"{query}")

        retriever_system="""
        You are an AI assistant that answers questions based on documents provided by the user.  Wait for the user to send a document. Once you receive the document, carefully read its contents and then answer the following question:

Question: %s

[Wait for user's message containing the document]
        """ % message

        
        # Create the agent
        agent = LlamaCppAgent(
            provider,
            system_prompt=f"{retriever_system}",
            predefined_messages_formatter_type=MessagesFormatterType.GEMMA_2,
            debug_output=True,
        )

        # Set the settings like temperature, top-k, top-p, max tokens, etc.
        settings = provider.get_provider_default_settings()
        settings.temperature = temperature
        settings.top_k = top_k
        settings.top_p = top_p
        settings.max_tokens = max_tokens
        settings.repeat_penalty = repeat_penalty
        settings.stream = True

        messages = BasicChatHistory()

        # Add the chat history
        for msn in history:
            user = {"role": Roles.user, "content": msn[0]}
            assistant = {"role": Roles.assistant, "content": msn[1]}
            messages.add_message(user)
            messages.add_message(assistant)

        # Get the response stream
        stream = agent.get_chat_response(
            text,
            llm_sampling_settings=settings,
            chat_history=messages,
            returns_streaming_generator=True,
            print_output=False,
        )

        # Log the success
        logging.info("Response stream generated successfully")

        # Generate the response
        outputs = ""
        for output in stream:
            outputs += output
            yield outputs

    # Handle exceptions that may occur during the process
    except Exception as e:
        # Custom exception handling
        raise CustomExceptionHandling(e, sys) from e


# Create a chat interface
demo = gr.ChatInterface(
    respond,
    examples=[["What is the Transformer?"], ["Tell me About Huggingface."], ["How to upload dataset?"]],
    additional_inputs_accordion=gr.Accordion(
        label="⚙️ Parameters", open=False, render=False
    ),
    additional_inputs=[
        gr.Dropdown(
            choices=[
                "google_gemma-3-1b-it-Q6_K.gguf",
                "google_gemma-3-1b-it-Q5_K_M.gguf",
            ],
            value="google_gemma-3-1b-it-Q5_K_M.gguf",
            label="Model",
            info="Select the AI model to use for chat",
        ),
        gr.Textbox(
            value="You are a helpful assistant.",
            label="System Prompt",
            info="Define the AI assistant's personality and behavior",
            lines=2,visible=False
        ),
        gr.Slider(
            minimum=512,
            maximum=2048,
            value=1024,
            step=1,
            label="Max Tokens",
            info="Maximum length of response (higher = longer replies)",
        ),
        gr.Slider(
            minimum=0.1,
            maximum=2.0,
            value=0.7,
            step=0.1,
            label="Temperature",
            info="Creativity level (higher = more creative, lower = more focused)",
        ),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p",
            info="Nucleus sampling threshold",
        ),
        gr.Slider(
            minimum=1,
            maximum=100,
            value=40,
            step=1,
            label="Top-k",
            info="Limit vocabulary choices to top K tokens",
        ),
        gr.Slider(
            minimum=1.0,
            maximum=2.0,
            value=1.1,
            step=0.1,
            label="Repetition Penalty",
            info="Penalize repeated words (higher = less repetition)",
        ),
    ],
    theme="Ocean",
    submit_btn="Send",
    stop_btn="Stop",
    title=title,
    description=description,
    chatbot=gr.Chatbot(scale=1, show_copy_button=True),
    flagging_mode="never",
)


# Launch the chat interface
if __name__ == "__main__":
    demo.launch(debug=False)