import os
import streamlit as st
from features import (
    ats, analyzer, company_recommend, cover_letter, enhance, improve,
    interview, linkedin, newresume, recommend, review
)
from components import docLoader
from dotenv import load_dotenv
import google.generativeai as genai
from langchain_google_genai import ChatGoogleGenerativeAI
import asyncio

# Load environment variables
load_dotenv()

class CareerNavigator:
    def __init__(self, title="Career Navigator"):
        self.title = title

    @staticmethod
    async def async_model():
        genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
        return ChatGoogleGenerativeAI(model="gemini-pro")

    def model(self):
        return asyncio.run(self.async_model())

# Initialize CareerNavigator instance
navigator = CareerNavigator()

# Set Streamlit page configuration
st.set_page_config(page_title=navigator.title, page_icon='🧑‍💼', layout='wide')

# Main title
st.title("Welcome to Career Navigator")

# Load and display document
text = docLoader.load_doc()
st.session_state['doc_text'] = text

jd_col, doc_col = st.columns(2)
with jd_col:
    jd = st.text_area("Enter Job Description:", key="input")
if text:
    with doc_col:
        st.text_area("Extracted Data From Resume:", value=st.session_state['doc_text'], height=300)

role = st.text_input("Desired Role:", placeholder="e.g., Software Engineer")
st.session_state['role'] = role

# Sidebar options
with st.sidebar:
    st.title('Career Navigator Menu')
    st.subheader('Choose an Option:')
    option = st.radio(
        "Navigate to:",
        (
            "Calculate ATS Score", "Review Resume", "Enhance Resume",
            "Improve Resume", "Get Recommendations", "Analyze Keywords",
            "Generate Cover Letter", "Generate Resume",
            "Update LinkedIn Profile", "Prepare for Interview",
            "Company Recommendations"
        )
    )

    # Load model
    with st.spinner("Initializing Model..."):
        llm = navigator.model()

# Option-specific configurations
if option == "Calculate ATS Score":
    calculation_method = st.radio(
        "Select ATS Score Calculation Method:",
        ("Using AI", "Manually (Cosine Similarity)"),
        horizontal=True
    )

elif option == "Get Recommendations":
    recommendation_type = st.radio(
        "Select Recommendation Type:",
        ("Entire Resume", "Section Wise"),
        horizontal=True
    )

elif option == "Analyze Keywords":
    analyz_type = st.radio(
        "Select Keywords Function:",
        ("Analyze Keywords", "Keyword Synonyms"),
        horizontal=True
    )

# Dictionary mapping options to functions
option_functions = {
    "Calculate ATS Score": ats.run_ats,
    "Review Resume": review.run_review,
    "Enhance Resume": enhance.run_enhance,
    "Improve Resume": improve.run_improve,
    "Get Recommendations": recommend.run_recommend,
    "Analyze Keywords": analyzer.run_analyzer,
    "Generate Cover Letter": cover_letter.run_letter,
    "Generate Resume": newresume.run_newresume,
    "Update LinkedIn Profile": linkedin.run_linkedin,
    "Prepare for Interview": interview.run_interview,
    "Company Recommendations": company_recommend.run_company
}

# Handle the selected option
if option in option_functions:
    func = option_functions[option]
    if option == "Calculate ATS Score":
        if calculation_method == "Manually (Cosine Similarity)":
            func(llm, st.session_state['doc_text'], jd, manual=True)
        else:
            func(llm, st.session_state['doc_text'], jd)
    elif option == "Get Recommendations":
        if recommendation_type == "Entire Resume":
            func(llm, st.session_state['doc_text'], jd, section=True)
        else:
            func(llm, st.session_state['doc_text'], jd)
    elif option == "Analyze Keywords":
        if analyz_type == "Analyze Keywords":
            func(llm, st.session_state['doc_text'], jd, analysis=True)
        else:
            func(llm, st.session_state['doc_text'], jd)
    else:
        func(llm, st.session_state['doc_text'], jd, role=st.session_state['role'])