# -*- coding: utf-8 -*-
"""Untitled27.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/16VnJw-SMttFaPZi8gMJTp4k7YDqL-rP0
"""

import streamlit as st
import os
from resume_generation_gemini_pro import Gemini_pro_main  # Import function from your model file
from similarity_score_refined import similarity_main

# Helper function to save uploaded files temporarily and return their paths
def save_uploaded_file(uploaded_file):
    # Define the temporary file path
    file_path = os.path.join("/tmp", uploaded_file.name)

    # Write the uploaded file content to the path
    with open(file_path, "wb") as f:
        f.write(uploaded_file.getbuffer())

    return file_path

# Streamlit UI layout
st.title("Resume Tailoring with Google Generative AI")
st.write("Upload your current resume and a job description to generate a tailored resume.")

# File uploaders for the current resume and job description
uploaded_resume = st.file_uploader("Upload Current Resume (.docx or .pdf)", type=["docx", "pdf"], key="resume")
uploaded_job_description = st.file_uploader("Upload Job Description (.docx or .pdf)", type=["docx", "pdf"], key="job_description")

if uploaded_resume is not None and uploaded_job_description is not None:
    # Save both uploaded files and get their paths
    resume_path = save_uploaded_file(uploaded_resume)
    job_description_path = save_uploaded_file(uploaded_job_description)
    st.write(f"Files saved at: {resume_path} and {job_description_path}")

    # Button to calculate similarity score
    if st.button("Check Similarity Score"):
        # Calculate the similarity score between resume and job description using similarity_main
        similarity_score = similarity_main(resume_path, job_description_path)

        # Display the similarity score on a slider
        st.write("Similarity Score:")
        st.slider("Similarity", min_value=0, max_value=100, value=int(similarity_score), format="%d%%")

    # Generate tailored resume button
    if st.button("Generate Tailored Resume"):
        with st.spinner("Generating resume with Google Generative AI..."):
            # Call the model function with both file paths
            generated_resume = Gemini_pro_main(resume_path, job_description_path)  # Assuming this function returns the resume text

            # Display the generated tailored resume
            st.subheader("Generated Tailored Resume:")
            st.write(generated_resume)
else:
    st.warning("Please upload both the current resume and job description files.")