app.py

import streamlit as st
import PyPDF2
from docx import Document
from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
import spacy
import pytextrank
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_core.messages import SystemMessage
from langchain_core.prompts import ChatPromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder
from langchain.memory import ConversationBufferMemory
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough, RunnableLambda
import spacy
import subprocess

# Function to check and download spaCy model
def ensure_spacy_model(model_name="en_core_web_sm"):
    try:
        spacy.load(model_name)
    except OSError:
        subprocess.run(["python", "-m", "spacy", "download", model_name])
        spacy.load(model_name)

# Function to extract text from PDF
def extract_text_from_pdf(uploaded_file):
    text = ""
    reader = PyPDF2.PdfReader(uploaded_file)
    for page in reader.pages:
        text += page.extract_text()
    return text

# Function to extract text from Word document
def extract_text_from_word(uploaded_file):
    text = ""
    doc = Document(uploaded_file)
    for paragraph in doc.paragraphs:
        text += paragraph.text + "\n"
    return text

# Function to summarize text
def summarize_text(text, max_length=1000, min_length=30):
    max_length = min(max_length, 1000)  # Ensure max_length doesn't exceed 1000

    try:
        # Initialize the summarizer pipeline
        summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
        summary = summarizer(text, max_length=max_length, min_length=min_length, do_sample=False)
        if isinstance(summary, list) and len(summary) > 0:
            return summary[0]['summary_text']
        else:
            raise ValueError("Unexpected summarizer output format")
    except Exception as e:
        return f"Error in summarization: {e}"

# Function to extract keywords using spaCy and PyTextRank
def extract_keywords(text, top_n=10):
    ensure_spacy_model("en_core_web_sm")
    nlp = spacy.load("en_core_web_sm")
    nlp.add_pipe("textrank", last=True)
    doc = nlp(text)
    keywords = [phrase.text for phrase in doc._.phrases[:top_n]]
    return keywords


# Initialize Google Generative AI chat model
def initialize_chat_model():
    with open("key.txt", "r") as f:
        GOOGLE_API_KEY = f.read().strip()

    chat_model = ChatGoogleGenerativeAI(
        google_api_key=GOOGLE_API_KEY,
        model="gemini-1.5-pro-latest",
        temperature=0.4,
        max_tokens=2000,
        timeout=120,
        max_retries=5,
        top_p=0.9,
        top_k=40,
        presence_penalty=0.6,
        frequency_penalty=0.3
    )
    return chat_model

chat_model = initialize_chat_model()

# Create Chat Template
chat_prompt_template = ChatPromptTemplate.from_messages(
    [
        SystemMessage(
            content="""  You are a language model designed to follow user instructions exactly as given.
            Do not take any actions or provide any information unless specifically directed by the user.
            Your role is to fulfill the user's requests precisely without deviating from the instructions provided."""
        ),
        MessagesPlaceholder(variable_name="chat_history"),
        HumanMessagePromptTemplate.from_template("{human_input}")
    ]
)

# Initialize the Memory
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)

# Create an Output Parser
output_parser = StrOutputParser()

# Define a chain
chain = RunnablePassthrough.assign(
            chat_history=RunnableLambda(lambda human_input: memory.load_memory_variables(human_input)['chat_history'])
        ) | chat_prompt_template | chat_model | output_parser

# Streamlit App
st.title("Interview Preparation with AI")
st.markdown("## Part-1: Upload Files, Summarize, and Extract Keywords")

# File upload section
file1 = st.file_uploader("Upload your resume (PDF or DOCX):", type=["pdf", "docx"])
file2 = st.file_uploader("Upload the job description (PDF or DOCX):", type=["pdf", "docx"])

if file1 and file2:
    try:
        # Detect file type and extract text for file 1
        if file1.name.endswith('.pdf'):
            text1 = extract_text_from_pdf(file1)
        elif file1.name.endswith('.docx'):
            text1 = extract_text_from_word(file1)
        else:
            st.error("Unsupported file type for file 1")

        # Detect file type and extract text for file 2
        if file2.name.endswith('.pdf'):
            text2 = extract_text_from_pdf(file2)
        elif file2.name.endswith('.docx'):
            text2 = extract_text_from_word(file2)
        else:
            st.error("Unsupported file type for file 2")

        # Summarize texts
        st.markdown("### Summarizing the uploaded documents...")
        summary1 = summarize_text(text1)
        summary2 = summarize_text(text2)

        # Extract keywords
        st.markdown("### Extracting keywords...")
        keywords1 = extract_keywords(text1)
        keywords2 = extract_keywords(text2)

        # Display results
        st.markdown("### Results for File 1 (Resume)")
        st.subheader("Summary:")
        st.write(summary1)
        st.subheader("Keywords:")
        st.write(", ".join(keywords1))

        st.markdown("### Results for File 2 (Job Description)")
        st.subheader("Summary:")
        st.write(summary2)
        st.subheader("Keywords:")
        st.write(", ".join(keywords2))

        # Compare keywords
        st.markdown("### Keyword Analysis")
        resume_keywords = set(keywords1)
        job_description_keywords = set(keywords2)

        st.write("**Resume Keywords:**", ", ".join(resume_keywords))
        st.write("**Job Description Keywords:**", ", ".join(job_description_keywords))

        common_keywords = resume_keywords.intersection(job_description_keywords)
        st.write("**Common Keywords:**", ", ".join(common_keywords))

        # Calculate ATS Score
        st.markdown("### ATS Score Calculation")
        query = {"human_input": f"""
You are an advanced Applicant Tracking System (ATS) designed to evaluate resumes against job descriptions with exceptional accuracy. Analyze the following keywords extracted from a job description and a resume, compare them, and calculate the match percentage.

Job Description Keywords:
{list(job_description_keywords)}

Resume Keywords:
{list(resume_keywords)}

Provide the ATS score as a percentage match between the resume and the job description in the following format:
The ATS Score of your Resume According to the Job Description is \"XX%\"
"""}

        response = chain.invoke(query)
        memory.save_context(query, {"output": response})

        st.write(response)

    except Exception as e:
        st.error(f"An error occurred: {e}")

else:
    st.info("Please upload both files to proceed.")