import streamlit as st
import os
from dotenv import load_dotenv
from langchain_community.llms import OpenAI
from langchain_google_genai import ChatGoogleGenerativeAI

# Load environment variables
load_dotenv()


def analyze_job_description(job_description,model):
    prompt = f"""
    Analyze the following job description and extract the following information:
    - Skills
    - Roles
    - Topics
    - Level of Understanding
    - Difficulty
    - Domain of company
    - Industry of company

    Job Description:
    {job_description}
    .provide the answer in json format 
    """

    
    if model == "Open AI":
        llm = OpenAI(temperature=0.7, openai_api_key=st.secrets["OPENAI_API_KEY"])
        analysis = llm(prompt)
        
        
    elif model == "Gemini":
        llm = ChatGoogleGenerativeAI(model="gemini-pro", google_api_key=st.secrets["GOOGLE_API_KEY"])
        analysis = llm.invoke(prompt)
        analysis = analysis.content
    
    
    return analysis

def app():
    st.title("Job Description Analysis")

    st.header("Select AI:")
    model = st.radio("Model", [ "Gemini","Open AI",])
    st.write("Selected option:", model)
    
    # Input: Job description
    job_description = st.text_area("Enter Job Description:")
    
    # Analyze button
    if st.button("Analyze"):
        if job_description:
            # Use the model to analyze the job description
            analysis = analyze_job_description(job_description,model)
            st.write("Analysis:")
            st.text(analysis)

     # Store analysis in session state for use in Project Suggestions page
            st.session_state['analysis'] = analysis
            model = model
        
            
        else:
            st.error("Please enter a job description.")