app.py

import os
import tempfile
import streamlit as st
import docx
import textract
from transformers import pipeline

# Set page title
st.set_page_config(page_title="Resume Analyzer and Company Suitability Checker")

#####################################
# Preload Models
#####################################
@st.cache_resource(show_spinner=True)
def load_models():
    """Load all models at startup"""
    with st.spinner("Loading AI models... This may take a minute on first run."):
        models = {}
        # Load summarization model
        models['summarizer'] = pipeline("summarization", model="google/pegasus-xsum")
        # Load similarity model
        models['similarity'] = pipeline("sentence-similarity", model="sentence-transformers/all-MiniLM-L6-v2")
        return models

# Preload models immediately when app starts
models = load_models()

#####################################
# Function: Extract Text from File
#####################################
def extract_text_from_file(file_obj):
    """
    Extract text from .doc and .docx files.
    Returns the extracted text or an error message if extraction fails.
    """
    filename = file_obj.name
    ext = os.path.splitext(filename)[1].lower()
    text = ""

    if ext == ".docx":
        try:
            document = docx.Document(file_obj)
            text = "\n".join(para.text for para in document.paragraphs if para.text.strip())
        except Exception as e:
            text = f"Error processing DOCX file: {e}"
    elif ext == ".doc":
        try:
            with tempfile.NamedTemporaryFile(delete=False, suffix=".doc") as tmp:
                tmp.write(file_obj.read())
                tmp_filename = tmp.name
            text = textract.process(tmp_filename).decode("utf-8")
            os.unlink(tmp_filename)
        except Exception as e:
            text = f"Error processing DOC file: {e}"
    else:
        text = "Unsupported file type."
    return text

#####################################
# Function: Summarize Resume Text
#####################################
def summarize_resume_text(resume_text, models):
    """
    Generates a concise summary of the resume text using the summarization model.
    """
    summarizer = models['summarizer']
    
    # Handle long text
    max_input_length = 1024  # PEGASUS-XSUM limit
    
    if len(resume_text) > max_input_length:
        # Process in chunks if text is too long
        chunks = [resume_text[i:i+max_input_length] for i in range(0, min(len(resume_text), 3*max_input_length), max_input_length)]
        summaries = []
        
        for chunk in chunks:
            chunk_summary = summarizer(chunk, max_length=100, min_length=30, do_sample=False)[0]['summary_text']
            summaries.append(chunk_summary)
        
        candidate_summary = " ".join(summaries)
        if len(candidate_summary) > max_input_length:
            candidate_summary = summarizer(candidate_summary[:max_input_length], max_length=150, min_length=40, do_sample=False)[0]['summary_text']
    else:
        candidate_summary = summarizer(resume_text, max_length=150, min_length=40, do_sample=False)[0]['summary_text']
    
    return candidate_summary

#####################################
# Function: Compare Candidate Summary to Company Prompt
#####################################
def compute_suitability(candidate_summary, company_prompt, models):
    """
    Compute the similarity between candidate summary and company prompt.
    Returns a score in the range [0, 1].
    """
    similarity_pipeline = models['similarity']
    
    # The pipeline expects a document and a list of candidates to compare to
    result = similarity_pipeline(
        candidate_summary,
        [company_prompt]
    )
    
    # Extract the similarity score from the result
    score = result[0]['score']
    return score

#####################################
# Streamlit Interface
#####################################
st.title("Resume Analyzer and Company Suitability Checker")
st.markdown(
    """
Upload your resume file in **.doc** or **.docx** format. The app performs the following tasks:
1. Extracts text from the resume.
2. Uses a transformer-based model to generate a concise candidate summary.
3. Compares the candidate summary with a company profile to produce a suitability score.
"""
)

# Use two columns with equal width
col1, col2 = st.columns(2)

with col1:
    # File uploader for resume
    uploaded_file = st.file_uploader("Upload Resume", type=["doc", "docx"])
    
    if uploaded_file is not None:
        st.write(f"{uploaded_file.name}  {uploaded_file.size/1024:.1f}KB")
    
    # Button to process the resume
    if st.button("Process Resume", type="primary", use_container_width=True):
        if uploaded_file is None:
            st.error("Please upload a resume file first.")
        else:
            with st.status("Processing resume...") as status:
                status.update(label="Extracting text from resume...")
                resume_text = extract_text_from_file(uploaded_file)
                
                if not resume_text or resume_text.strip() == "":
                    status.update(label="Error: No text could be extracted", state="error")
                else:
                    status.update(label=f"Extracted {len(resume_text)} characters. Generating summary...")
                    candidate_summary = summarize_resume_text(resume_text, models)
                    st.session_state["candidate_summary"] = candidate_summary
                    status.update(label="Processing complete!", state="complete")
    
    # Display candidate summary if available
    if "candidate_summary" in st.session_state:
        st.subheader("Candidate Summary")
        st.markdown(st.session_state["candidate_summary"])

with col2:
    # Pre-defined company prompt for Google LLC.
    default_company_prompt = (
        "Google LLC, a global leader in technology and innovation, specializes in internet services, cloud computing, "
        "artificial intelligence, and software development. As part of Alphabet Inc., Google seeks candidates with strong "
        "problem-solving skills, adaptability, and collaboration abilities. Technical roles require proficiency in programming "
        "languages such as Python, Java, C++, Go, or JavaScript, with expertise in data structures, algorithms, and system design. "
        "Additionally, skills in AI, cybersecurity, UX/UI design, and digital marketing are highly valued. Google fosters a culture "
        "of innovation, expecting candidates to demonstrate creativity, analytical thinking, and a passion for cutting-edge technology."
    )

    # Company prompt text area.
    company_prompt = st.text_area(
        "Enter company details:",
        value=default_company_prompt,
        height=150,
    )

    # Button to compute the suitability score.
    if st.button("Compute Suitability Score", type="primary", use_container_width=True):
        if "candidate_summary" not in st.session_state:
            st.error("Please process the resume first!")
        else:
            candidate_summary = st.session_state["candidate_summary"]
            if candidate_summary.strip() == "":
                st.error("Candidate summary is empty; please check your resume file.")
            elif company_prompt.strip() == "":
                st.error("Please enter the company information.")
            else:
                with st.spinner("Computing suitability score..."):
                    score = compute_suitability(candidate_summary, company_prompt, models)
                
                # Display score with a progress bar for visual feedback
                st.success(f"Suitability Score: {score:.2f} (range 0 to 1)")
                st.progress(score)
                
                # Add interpretation of score
                if score > 0.75:
                    st.info("Excellent match! Your profile appears very well suited for this company.")
                elif score > 0.5:
                    st.info("Good match. Your profile aligns with many aspects of the company's requirements.")
                elif score > 0.3:
                    st.info("Moderate match. Consider highlighting more relevant skills or experience.")
                else:
                    st.info("Low match. Your profile may need significant adjustments to better align with this company.")