import streamlit as st
from ibm_watsonx_ai.foundation_models import ModelInference
from ibm_watsonx_ai import Credentials, APIClient
from ibm_watsonx_ai.metanames import GenTextParamsMetaNames as GenParams
from knowledge_bases import KNOWLEDGE_BASE_OPTIONS, SYSTEM_PROMPTS
import genparam
import time

def check_password():
    """Password protection check for the app."""
    def password_entered():
        if st.session_state["password"] == st.secrets["app_password"]:
            st.session_state["password_correct"] = True
            del st.session_state["password"]
        else:
            st.session_state["password_correct"] = False

    if "password_correct" not in st.session_state:
        st.markdown("\n\n")
        st.text_input("Enter the password", type="password", on_change=password_entered, key="password")
        st.divider()
        st.info("Designed and developed by Milan Mrdenovic © IBM Norway 2024")
        return False
    elif not st.session_state["password_correct"]:
        st.markdown("\n\n")
        st.text_input("Enter the password", type="password", on_change=password_entered, key="password")
        st.divider()
        st.error("😕 Incorrect password")
        st.info("Designed and developed by Milan Mrdenovic © IBM Norway 2024")
        return False
    else:
        return True

def initialize_session_state():
    """Initialize all session state variables."""
    if 'chat_history_1' not in st.session_state:
        st.session_state.chat_history_1 = []
    if 'chat_history_2' not in st.session_state:
        st.session_state.chat_history_2 = []
    if 'chat_history_3' not in st.session_state:
        st.session_state.chat_history_3 = []
    if 'first_question' not in st.session_state:
        st.session_state.first_question = False 
    if "counter" not in st.session_state:
        st.session_state["counter"] = 0
    if 'token_statistics' not in st.session_state:
        st.session_state.token_statistics = []
    if 'selected_kb' not in st.session_state:
        st.session_state.selected_kb = KNOWLEDGE_BASE_OPTIONS[0]
    if 'current_system_prompts' not in st.session_state:
        st.session_state.current_system_prompts = SYSTEM_PROMPTS[st.session_state.selected_kb]

def setup_client(project_id=None):
    """Setup WatsonX client with credentials."""
    credentials = Credentials(
        url=st.secrets["url"],
        api_key=st.secrets["api_key"]
    )
    project_id = project_id or st.secrets["project_id"]
    client = APIClient(credentials, project_id=project_id)
    return credentials, client

def get_active_model():
    """Get the currently active model based on configuration."""
    return genparam.SELECTED_MODEL_1 if genparam.ACTIVE_MODEL == 0 else genparam.SELECTED_MODEL_2

def get_active_prompt_template():
    """Get the currently active prompt template."""
    return genparam.PROMPT_TEMPLATE_1 if genparam.ACTIVE_MODEL == 0 else genparam.PROMPT_TEMPLATE_2

def prepare_prompt(prompt, chat_history):
    """Prepare the prompt with chat history if available."""
    if genparam.TYPE == "chat" and chat_history:
        chats = "\n".join([f"{message['role']}: \"{message['content']}\"" for message in chat_history])
        return f"Conversation History:\n{chats}\n\nNew User Input: {prompt}"
    return f"User Input: {prompt}"

def apply_prompt_syntax(prompt, system_prompt, prompt_template, bake_in_prompt_syntax):
    """Apply appropriate syntax to the prompt based on model requirements."""
    model_family_syntax = {
        "llama3-instruct (llama-3, 3.1 & 3.2) - system": """<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n""",
        "llama3-instruct (llama-3, 3.1 & 3.2) - user": """<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n""",
        "granite-13b-chat & instruct - system": """<|system|>\n{system_prompt}\n<|user|>\n{prompt}\n<|assistant|>\n\n""",
        "granite-13b-chat & instruct - user": """<|user|>\n{prompt}\n<|assistant|>\n\n""",
        "mistral & mixtral v2 tokenizer - system": """<s>[INST] System Prompt: {system_prompt} [/INST][INST] {prompt} [/INST]\n\n""",
        "mistral & mixtral v2 tokenizer - user": """<s>[INST] {prompt} [/INST]\n\n""",
        "no syntax - system": """{system_prompt}\n\n{prompt}""",
        "no syntax - user": """{prompt}"""
    }
    
    if bake_in_prompt_syntax:
        template = model_family_syntax[prompt_template]
        if system_prompt:
            return template.format(system_prompt=system_prompt, prompt=prompt)
    return prompt

def generate_response(watsonx_llm, prompt_data, params):
    """Generate streaming response from the model."""
    generated_response = watsonx_llm.generate_text_stream(prompt=prompt_data, params=params)
    for chunk in generated_response:
        yield chunk

def capture_tokens(prompt_data, response, client, bot_name):
    """Capture token usage statistics."""
    if not genparam.TOKEN_CAPTURE_ENABLED:
        return

    watsonx_llm = ModelInference(
        api_client=client, 
        model_id=genparam.SELECTED_MODEL,
        verify=genparam.VERIFY
    )

    input_tokens = watsonx_llm.tokenize(prompt=prompt_data)["result"]["token_count"]
    output_tokens = watsonx_llm.tokenize(prompt=response)["result"]["token_count"]
    total_tokens = input_tokens + output_tokens

    return {
        "bot_name": bot_name,
        "input_tokens": input_tokens,
        "output_tokens": output_tokens,
        "total_tokens": total_tokens,
        "timestamp": time.strftime("%H:%M:%S")
    }

def fetch_response(user_input, client, system_prompt, chat_history):
    """Fetch response from the model for the given input."""
    prompt = prepare_prompt(user_input, chat_history)
    prompt_data = apply_prompt_syntax(
        prompt,
        system_prompt,
        get_active_prompt_template(),
        genparam.BAKE_IN_PROMPT_SYNTAX
    )

    watsonx_llm = ModelInference(
        api_client=client, 
        model_id=get_active_model(),
        verify=genparam.VERIFY
    )

    params = {
        GenParams.DECODING_METHOD: genparam.DECODING_METHOD,
        GenParams.MAX_NEW_TOKENS: genparam.MAX_NEW_TOKENS,
        GenParams.MIN_NEW_TOKENS: genparam.MIN_NEW_TOKENS,
        GenParams.REPETITION_PENALTY: genparam.REPETITION_PENALTY,
        GenParams.STOP_SEQUENCES: genparam.STOP_SEQUENCES
    }

    stream = generate_response(watsonx_llm, prompt_data, params)
    return stream, prompt_data