app.py

import pysqlite3
import sys
sys.modules["sqlite3"] = sys.modules.pop("pysqlite3")

import streamlit as st
from uuid import uuid4
from langchain_cohere import CohereEmbeddings
from langchain_chroma import Chroma
from langchain_core.documents import Document
from langchain_cohere import ChatCohere
import random

# Streamlit app
st.set_page_config(page_title="JobFit CV/Resume Matcher 💼", page_icon="💼", layout="wide")

# Sidebar design
def apply_sidebar_colors():
    st.markdown(
        """
        <style>
        .sidebar .sidebar-content {
            background: linear-gradient(45deg, #ff6f61, #ffcc33, #6f9dff, #93f5a3);
            color: white;
        }
        </style>
        """,
        unsafe_allow_html=True
    )

apply_sidebar_colors()

# App title with emojis
st.title("💼 JobFit CV/Resume Matcher")

# Sidebar for API key, job description, and user details
with st.sidebar:
    st.header("🔑 Enter Your Details")
    
    with st.form(key="user_details_form", clear_on_submit=True):
        # Field for API key
        st.subheader("🌐 Cohere API Key")
        st.markdown("You can get your API key from [Cohere's website](https://dashboard.cohere.com/).")
        api_key = st.text_input("Enter your Cohere API Key", type="password")

        # Job description input
        st.subheader("📝 Job Description")
        job_description = st.text_area("Paste the Job Description here", placeholder="Enter the job description you're applying for...")

        # Check if user data is already in session state
        if "name" not in st.session_state:
            st.session_state["name"] = ""
        if "contact" not in st.session_state:
            st.session_state["contact"] = ""

        if "user_id" not in st.session_state:
            st.session_state["user_id"] = str(random.randint(1000000000, 9999999999))

        user_id = st.session_state["user_id"]

        # User details inputs
        st.subheader("👤 Personal Information")
        name = st.text_input("Name", st.session_state["name"], placeholder="Enter your full name...")
        contact = st.text_input("Contact Information", st.session_state["contact"], placeholder="Email, phone number, or LinkedIn...")

        st.subheader("💼 Professional Background")
        skills = st.text_area("Skills", placeholder="List your skills (one per line)...")
        experience = st.text_area("Experience", placeholder="Work experience details (one per line)...")
        education = st.text_area("Education", placeholder="Educational qualifications (one per line)...")
        projects = st.text_area("Projects", placeholder="Notable projects (one per line)...")

        submit_button = st.form_submit_button(label="Save Details")

    # Save the data if all fields are filled
    if submit_button:
        if all([api_key, job_description, name, contact, skills, experience, education, projects]):
            st.session_state["api_key"] = api_key
            st.session_state["name"] = name
            st.session_state["contact"] = contact
            st.session_state["job_description"] = job_description
            st.session_state["skills"] = skills
            st.session_state["experience"] = experience
            st.session_state["education"] = education
            st.session_state["projects"] = projects
            st.success("✅ Details Saved Successfully!")
        else:
            st.warning("⚠️ Please fill out all fields to proceed.")

# Check if the user has saved the details
if "api_key" in st.session_state:
    # Main layout for generating the resume
    st.subheader("🛠️ Generate Your Custom Resume")

    # Convert input fields to lists
    skills_list = st.session_state["skills"].splitlines()
    experience_list = st.session_state["experience"].splitlines()
    education_list = st.session_state["education"].splitlines()
    project_list = st.session_state["projects"].splitlines()

    # Initialize embeddings and vector store
    embedding_model = CohereEmbeddings(cohere_api_key=st.session_state["api_key"], model="embed-english-light-v3.0")
    vector_store = Chroma(collection_name="user_data", embedding_function=embedding_model, persist_directory="chroma")

    # Create documents for vector store
    skill_documents = [Document(page_content=skill, metadata={"user_id": user_id, "type": "skill"}, id=str(uuid4())) for skill in skills_list]
    experience_documents = [Document(page_content=exp, metadata={"user_id": user_id, "type": "experience"}, id=str(uuid4())) for exp in experience_list]
    education_documents = [Document(page_content=edu, metadata={"user_id": user_id, "type": "education"}, id=str(uuid4())) for edu in education_list]
    project_documents = [Document(page_content=proj, metadata={"user_id": user_id, "type": "project"}, id=str(uuid4())) for proj in project_list]

    # Add documents to the vector store
    vector_store.add_documents(skill_documents + experience_documents + education_documents + project_documents)

    # Button to generate the resume
    if st.button("📝 Generate Resume"):
        def remove_duplicates(results):
            seen = set()
            unique_results = []
            for result in results:
                if result.page_content not in seen:
                    unique_results.append(result)
                    seen.add(result.page_content)
            return unique_results

        # Search for relevant information based on the job description
        relevant_skills = vector_store.similarity_search_by_vector(embedding_model.embed_query(st.session_state["job_description"]), k=10, filter={"type": "skill"})
        relevant_experience = vector_store.similarity_search_by_vector(embedding_model.embed_query(st.session_state["job_description"]), k=4, filter={"type": "experience"})
        relevant_education = vector_store.similarity_search_by_vector(embedding_model.embed_query(st.session_state["job_description"]), k=4, filter={"type": "education"})
        relevant_projects = vector_store.similarity_search_by_vector(embedding_model.embed_query(st.session_state["job_description"]), k=4, filter={"type": "project"})

        # Format the results
        skills_content = "\n".join([doc.page_content for doc in remove_duplicates(relevant_skills)])
        experience_content = "\n".join([doc.page_content for doc in remove_duplicates(relevant_experience)])
        education_content = "\n".join([doc.page_content for doc in remove_duplicates(relevant_education)])
        projects_content = "\n".join([doc.page_content for doc in remove_duplicates(relevant_projects)])

        # Create a prompt to generate the resume
        system_prompt = f"""
        Generate a well-structured resume for {st.session_state['name']} with the following details:
        
        Job Description: {st.session_state['job_description']}
        Name: {st.session_state['name']}
        Contact: {st.session_state['contact']}
        Skills: {skills_content}
        Experience: {experience_content}
        Education: {education_content}
        Projects: {projects_content}

        Follow this structure:
        - Name and contact at the top.
        - Skills, experience, education, and projects listed clearly.
        - Use bullet points and headings where needed.
        """

        # Initialize ChatCohere
        chat_model = ChatCohere(cohere_api_key=st.session_state["api_key"])

        # Generate resume
        resume_output = chat_model.invoke(input=system_prompt)

        # Display the generated resume and a copy button
        st.subheader("📄 Generated Resume")
        st.text_area("Your Resume", resume_output.content, height=400)
        st.button("📋 Copy Resume to Clipboard")
else:
    st.info("👈 Please enter your details and click Save to generate your resume.")