from pathlib import Path
from shared_config import AgenticAIConfig, create_logger, logger
import streamlit as st
from dotenv import load_dotenv
from agent_workflows.orchestration import runAgenticWorkflow
from utilities.pdf_tools_v2 import export_to_pdf
from datetime import datetime


#2- Loading Env Variables
load_dotenv() # It will load all the Env Variables

#3- Set Page Config
st.set_page_config(
    page_title="Simplilearn Capstone Project", #Show it in the Tab
    page_icon="🤖",
    layout="wide"
)

#4- Initialize Config
if "config" not in st.session_state:
    st.session_state.config = AgenticAIConfig()
config = st.session_state.config
    
#5- Initialize Streamlit- Conversation History
if "chat_history" not in st.session_state:
    st.session_state.chat_history=[]

#6- Setting dict for Available Models
#llm_model = "gpt-5-nano"  #Output~ 0.4 US$ / 1M tokens    
#llm_model = "gpt-4o-mini"  #Output~ 0.6 US$ / 1M tokens
#llm_model = "gpt-5.4-nano"  #Output~ 1.25 US$ / 1M tokens 
#llm_model = "gpt-5-mini"  #Output~ 2 US$ / 1M tokens 
#llm_model = "gpt-5.4-mini"  #Output~ 4.50 US$ / 1M tokens 
#llm_model = "gpt-4o"  #Output~ 10 US$ / 1M tokens 
#llm_model = "gpt-5.5"  #Output~ 30 US$ / 1M tokens
MODEL_REGISTRY={
    "OpenAI":["gpt-4o-mini","gpt-5-mini","gpt-5.4-nano","gpt-4.1","gpt-4o","gpt-3.5-turbo"],
    "Ollama":["llama3.1","gemma4:e4b","gpt-oss:20b","qwen2.5-coder:14b","mistral-small3.2"],
    }

#7- Creating Sidebar
with st.sidebar:
    st.title("LLM Configuration")
    config.provider = st.selectbox(
        "LLM Provider",
        list(MODEL_REGISTRY.keys())
    )
    
    #Dynamic Model Selection Logic
    available_models = MODEL_REGISTRY[config.provider]
    
    config.model = st.selectbox(
        "Model",
        available_models
    )
    
    config.temperature= st.slider(
        "Temperature",
        min_value=0.0,
        max_value=2.0,
        value=config.temperature,
        step=0.1
    )
    
    config.max_tokens=st.slider(
        "Max Tokens",
        min_value=100,
        max_value=10000,
        value=config.max_tokens,
        step=100
    )
    
    st.divider()
    
    # Display Current Configurations Separately
    st.subheader("Current Configuration:")
    
    st.write(f"**Provider:** {config.provider}")
    st.write(f"**Model:** {config.model}")
    st.write(f"**Temperature:** {config.temperature}")
    st.write(f"**Max Token:** {config.max_tokens}")


#8- Setting Page Title
st.title("🤖 Simplilearn Agentic AI Capstone: Research and GTM Planning")

#9- Creating User Input Section
config.user_query = st.text_area(
    "Enter the product or service you want to research and plan a GTM strategy: ",
    value = config.user_query,
    height=200,
    placeholder = "Example: Electrical  bicycle, Burger shop in Tokyo, New AI-powered project management tool targeting small businesses, ..."
)
# Create Response Button
if st.button("Generate Response", type="primary"):
    if not config.user_query.strip():
        st.warning("Please enter a prompt before clicking the button")
    else:
        #Create logger
        if logger is not None:
            if logger.hasHandlers():
                logger.removeHandler(logger.handlers[0])  # Remove existing handlers
        create_logger()
        #Store the User Query for displaying in conversation History on Streamlit
        st.session_state.chat_history.append(
            {
                "role": "user",
                "message": config.user_query
            }
        )

        logger.info("Product or service to research and plan a GTM strategy: "+config.user_query)
        
        # Create a Spinner until response is generated
        with st.spinner("AI Agent is Thinking..."):
            # Call CrewAI FLow
            runAgenticWorkflow(config)

             #Export the final GTM document to PDF
            logger.info("Creating PDF...")
            pdf_fiename_prefix = "Agentic_AI_Generated_GTM_Document"
            script_dir = Path(__file__).parent 
            target_dir = script_dir / "output"
            target_dir.mkdir(parents=True, exist_ok=True)
            now = datetime.now()
            formatted_DateTime = now.strftime("%Y-%m-%d_%H%M%S")
            pdfpath = f"""output/{pdf_fiename_prefix}_{formatted_DateTime}.pdf"""
            export_to_pdf(
                llm_response=config.response,
                output_filename=pdfpath)

            logger.info("PDF Exported Successfully at: "+pdfpath)


            #Store the response for display in conversation history
            st.session_state.chat_history.append(
                {
                    "role": "assistant",
                    "message": config.response
                }
            )
            st.session_state.config= config
        

#10- Creating Response Section
st.subheader("Conversation History")

for chat in st.session_state.chat_history:
    if chat["role"]=="user":
        with st.chat_message("user"):
            st.write(chat["message"])
    else:
        with st.chat_message("assistant"):
            st.write(chat["message"])

#11- Creating the button for Users to clear all the chats from the UI
if st.sidebar.button("Clear Chat"):
    st.session_state.chat_history=[]
    st.rerun()