import streamlit as st
import os
from dotenv import load_dotenv
from groq import Groq
import PyPDF2

# Load environment variables
load_dotenv()

# Initialize Groq client
client = Groq(
    api_key=os.getenv("GROQ_API_KEY"),
)

def extract_text_from_pdf(file):
    reader = PyPDF2.PdfReader(file)
    text = ''
    for page_num in range(len(reader.pages)):
        page = reader.pages[page_num]
        text += page.extract_text()
    return text

def analyze_job_description(job_description):
    prompt = f"Here is a job description:\n\n{job_description}\n\nPlease extract the key requirements and skills needed for this position."
    chat_completion = client.chat.completions.create(
        messages=[
            {
                "role": "user",
                "content": prompt,
            }
        ],
        model="llama3-8b-8192",
    )
    return chat_completion.choices[0].message.content

def get_resume_feedback(content, job_requirements):
    prompt = f"Here is the content of the resume:\n\n{content}\n\nBased on the following job requirements and skills:\n\n{job_requirements}\n\nPlease provide suggestions for improving this resume, including keyword optimization and ATS compatibility checks."
    chat_completion = client.chat.completions.create(
        messages=[
            {
                "role": "user",
                "content": prompt,
            }
        ],
        model="llama3-8b-8192",
    )
    return chat_completion.choices[0].message.content

def score_resume_against_job_description(resume_text, job_description_text):
    resume_words = set(resume_text.lower().split())
    job_description_words = set(job_description_text.lower().split())
    common_words = resume_words.intersection(job_description_words)
    score = len(common_words) / len(job_description_words) * 100
    return score

def chat_with_llm(resume_text, job_description_text, user_question):
    prompt = f"Here is the content of the resume:\n\n{resume_text}\n\nHere is the job description:\n\n{job_description_text}\n\nQuestion: {user_question}\n\nAnswer:"
    chat_completion = client.chat.completions.create(
        messages=[
            {
                "role": "user",
                "content": prompt,
            }
        ],
        model="llama3-8b-8192",
    )
    return chat_completion.choices[0].message.content

def main():
    st.title("Resume Upgrader with ATS, Job Description Matching, and Chat Capabilities")

    # Upload resume files
    uploaded_resumes = st.file_uploader("Upload your resume (PDF)", type=["pdf"], accept_multiple_files=True)
    
    # Initialize job_description_text to avoid UnboundLocalError
    job_description_text = None
    
    # Upload or input job description
    job_description_option = st.radio("How would you like to provide the job description?", ("Upload PDF", "Enter Text"))

    if job_description_option == "Upload PDF":
        job_description_file = st.file_uploader("Upload job description (PDF)", type=["pdf"])
        if job_description_file:
            job_description_text = extract_text_from_pdf(job_description_file)
    else:
        job_description_text = st.text_area("Enter job description")

    if uploaded_resumes and job_description_text:
        st.write("Uploaded Resumes:")
        for uploaded_file in uploaded_resumes:
            st.write(uploaded_file.name)

        if job_description_option == "Upload PDF":
            st.write("Uploaded Job Description:")
            st.write(job_description_file.name)
        else:
            st.write("Entered Job Description:")

        # Extract text from all uploaded resumes
        combined_resume_text = ""
        for uploaded_file in uploaded_resumes:
            combined_resume_text += extract_text_from_pdf(uploaded_file) + "\n\n"

        st.write("All documents uploaded and text extracted successfully!")

        # Analyze the job description
        job_requirements = analyze_job_description(job_description_text)
        st.write("Extracted Job Requirements and Skills:")
        st.write(job_requirements)

        # Initialize session state for resume feedback history
        if "resume_feedback" not in st.session_state:
            st.session_state.resume_feedback = []

        # Provide options for the user
        option = st.selectbox("What would you like to do?", ("Get Feedback and ATS Check", "Get Score", "Chat"))

        if option == "Get Feedback and ATS Check":
            if st.button("Get Feedback"):
                feedback = get_resume_feedback(combined_resume_text, job_requirements)
                st.session_state.resume_feedback.append((feedback, None, None))

        elif option == "Get Score":
            if st.button("Get Score"):
                score = score_resume_against_job_description(combined_resume_text, job_description_text)
                st.session_state.resume_feedback.append((None, None, score))

        elif option == "Chat":
            if "chat_history" not in st.session_state:
                st.session_state.chat_history = []

            user_question = st.text_input("Enter your question:")

            if st.button("Ask"):
                if user_question:
                    response = chat_with_llm(combined_resume_text, job_description_text, user_question)
                    st.session_state.chat_history.append((user_question, response))
                    user_question = ""  # Clear input field

            if st.session_state.chat_history:
                st.subheader("Chat History")
                for i, (q, a) in enumerate(st.session_state.chat_history):
                    st.write(f"**Q{i+1}:** {q}")
                    st.write(f"**A{i+1}:** {a}")

        # Display feedback history
        if st.session_state.resume_feedback:
            st.subheader("Resume Improvement Suggestions and Scores")
            for i, (feedback, ats_feedback, score) in enumerate(st.session_state.resume_feedback):
                if feedback:
                    st.write(f"**Feedback for Resume {i+1}:**")
                    st.write(feedback)
                if score is not None:
                    st.write(f"**Match Score for Resume {i+1}:** {score:.2f}%")

if __name__ == "__main__":
    main()