app.py

import streamlit as st
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import PyPDF2
from dotenv import load_dotenv
import os

# Load environment variables from .env
load_dotenv()

# Job API keys and endpoints
access_token = os.getenv("API_KEY")

# Streamlit App Title
st.title("Job Description and CV-Based Email Generator")
st.write("""
This app uses Hugging Face's Gemma model to generate a professional email based on a pre-parsed CV and a job description.
Upload your CV once in the sidebar, and the system will reuse the parsed details for generating emails.
""")

# Sidebar for Settings and CV Upload
st.sidebar.title("Settings and CV Upload")
access_token = st.sidebar.text_input("Enter your Hugging Face Access Token", type="password")

# File Upload for CV in Sidebar
uploaded_file = st.sidebar.file_uploader("Upload your CV (PDF format):", type=["pdf"])

if "parsed_cv" not in st.session_state:
    st.session_state.parsed_cv = None

if uploaded_file is not None:
    try:
        # Extract text from PDF
        pdf_reader = PyPDF2.PdfReader(uploaded_file)
        cv_text = "".join([page.extract_text() for page in pdf_reader.pages])
        st.sidebar.success("CV uploaded and text extracted successfully!")

        # Parse CV details and save to session state
        def parse_cv(cv_text):
            # Basic parsing logic (can be extended for specific details)
            return f"""
            Name: [Extracted Name]
            Contact Information: [Extracted Contact Info]
            Skills: [Extracted Skills]
            Experience: [Extracted Experience]
            Education: [Extracted Education]
            Summary: {cv_text[:500]}...  # Truncated summary of the CV
            """

        st.session_state.parsed_cv = parse_cv(cv_text)
        st.sidebar.success("CV parsed successfully!")
    except Exception as e:
        st.sidebar.error(f"Failed to extract text from CV: {e}")

if st.session_state.parsed_cv:
    st.sidebar.write("### Parsed CV Details:")
    st.sidebar.text(st.session_state.parsed_cv)

# Ensure Access Token is Provided
if access_token:
    @st.cache_resource
    def initialize_pipeline(access_token):
        tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it", token=access_token)
        model = AutoModelForCausalLM.from_pretrained(
            "google/gemma-2b-it",
            torch_dtype="bfloat16",
            token=access_token
        )
        return pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512)

    text_gen_pipeline = initialize_pipeline(access_token)

    @st.cache_resource
    def initialize_chain():
        memory = ConversationBufferMemory()
        return ConversationChain(llm=None, memory=memory)  # No LLM; handled by pipeline

    conversation_chain = initialize_chain()

    # Input job description
    job_description = st.text_area("Enter the job description:", "")

    # Display generated email
    if st.button("Generate Email"):
        if st.session_state.parsed_cv and job_description.strip():
            # Prompt for email generation
            prompt = (
                f"Based on the following CV details:\n\n{st.session_state.parsed_cv}\n\n"
                f"And the following job description:\n\n{job_description}\n\n"
                f"Write a professional email expressing interest in the job. "
                f"Make it concise, polite, and tailored to the job."
            )

            # Generate email using Hugging Face pipeline
            response = text_gen_pipeline(prompt)[0]['generated_text']

            # Update memory with job description and response
            conversation_chain.memory.save_context({"job_description": job_description}, {"email": response})

            # Display response
            st.subheader("Generated Email:")
            st.write(response)

            # Display conversation history
            st.subheader("History:")
            st.write(conversation_chain.memory.buffer)
        else:
            st.warning("Please upload your CV in the sidebar and enter a job description.")
else:
    st.warning("Please enter your Hugging Face access token in the sidebar to use the app.")