app.py

import os
import tempfile
import re
import streamlit as st
import docx
import textract
from sentence_transformers import SentenceTransformer, util

#####################################
# 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])
        except Exception as e:
            text = f"Error processing DOCX file: {e}"
    elif ext == ".doc":
        try:
            # textract requires a file name; save the file temporarily.
            with tempfile.NamedTemporaryFile(delete=False, suffix=".doc") as tmp:
                tmp.write(file_obj.read())
                tmp.flush()
                tmp_filename = tmp.name
            text = textract.process(tmp_filename).decode("utf-8")
        except Exception as e:
            text = f"Error processing DOC file: {e}"
        finally:
            try:
                os.remove(tmp_filename)
            except Exception:
                pass
    else:
        text = "Unsupported file type."
    return text

#####################################
# Function: Extract Basic Resume Information
#####################################
def extract_basic_resume_info(text):
    """
    Parse the extracted text to extract/summarize:
    - Name
    - Age
    - Job Experience (capturing the block under the "experience" section)
    - Skills
    - Education

    Returns a dictionary with the extracted elements.
    """
    info = {
        "Name": None,
        "Age": None,
        "Job Experience": None,
        "Skills": None,
        "Education": None,
    }

    # Extract Name (e.g., "CONG, An Dong" from the first line)
    name_match = re.search(r"^([A-Z]+)[,\s]+([A-Z][a-z]+(?:\s+[A-Z][a-z]+)?)", text, re.MULTILINE)
    if name_match:
        info["Name"] = f"{name_match.group(1)} {name_match.group(2)}"
    else:
    # Fallback heuristic: assume a line with two or three capitalized words might be the candidate's name.
        potential_names = re.findall(r"\b[A-Z][a-z]+(?:\s+[A-Z][a-z]+){1,2}\b", text)
        if potential_names:
            info["Name"] = potential_names[0]

    # Extract Age (e.g., "Age: 28")
    age_match = re.search(r"[Aa]ge[:\-]\s*(\d{1,3})", text)
    if age_match:
        info["Age"] = age_match.group(1)

    # Extract Job Experience using the "experience" section.
    # Capture everything after the word "experience" until a new section or the end.
    experience_match = re.search(
        r"experience\s*(.*?)(?:\n\s*\n|additional information|skills|education|$)",
        text,
        re.IGNORECASE | re.DOTALL,
    )
    if experience_match:
        job_experience = experience_match.group(1).strip()
        info["Job Experience"] = " ".join(job_experience.split())
    else:
        # Fallback if not a labeled section.
        exp_match = re.search(
            r"(\d+)\s+(years|yrs)\s+(?:of\s+)?experience", text, re.IGNORECASE
        )
        if exp_match:
            info["Job Experience"] = f"{exp_match.group(1)} {exp_match.group(2)}"

    # Extract Skills (e.g., "Skills: Python, Java, SQL")
    skills_match = re.search(r"(Skills|Technical Skills)[:\-]\s*(.+)", text, re.IGNORECASE)
    if skills_match:
        skills_str = skills_match.group(2).strip()
        info["Skills"] = skills_str.rstrip(".")

    # Extract Education (e.g., "Education: ...")
    edu_match = re.search(
        r"education\s*(.*?)(?:\n\s*\n|experience|$)", text, re.IGNORECASE | re.DOTALL
    )
    if edu_match:
        education_block = edu_match.group(1).strip()
        info["Education"] = " ".join(education_block.split())
    else:
        # Fallback: search for common degree identifiers.
        edu_match = re.search(r"(Bachelor|Master|B\.Sc|M\.Sc|Ph\.D)[^\n]+", text)
        if edu_match:
            info["Education"] = edu_match.group(0)

    return info

#####################################
# Function: Summarize Basic Info into a Paragraph
#####################################
def summarize_basic_info(info):
    """
    Combine the extracted resume elements into a concise summary paragraph.
    """
    parts = []

    if info.get("Name"):
        parts.append(f"Candidate {info['Name']}")
    else:
        parts.append("The candidate")
    
    if info.get("Age"):
        parts.append(f"aged {info['Age']}")
    
    if info.get("Job Experience"):
        parts.append(f"with job experience: {info['Job Experience']}")
    
    if info.get("Skills"):
        parts.append(f"skilled in {info['Skills']}")
    
    if info.get("Education"):
        parts.append(f"and educated in {info['Education']}")
    
    summary_paragraph = ", ".join(parts) + "."
    return summary_paragraph

#####################################
# Function: Compare Candidate Summary to Company Prompt
#####################################
def compute_suitability(candidate_summary, company_prompt, model):
    """
    Compute the cosine similarity between candidate summary and company prompt embeddings.
    Returns a score in the range [0, 1].
    """
    candidate_embed = model.encode(candidate_summary, convert_to_tensor=True)
    company_embed = model.encode(company_prompt, convert_to_tensor=True)
    cosine_sim = util.cos_sim(candidate_embed, company_embed)
    score = float(cosine_sim.item())
    return score

#####################################
# Main Resume Processing Logic
#####################################
def process_resume(file_obj):
    resume_text = extract_text_from_file(file_obj)
    basic_info = extract_basic_resume_info(resume_text)
    summary_paragraph = summarize_basic_info(basic_info)
    return summary_paragraph

#####################################
# Load the Sentence-BERT Model
#####################################
@st.cache_resource(show_spinner=False)
def load_model():
    return SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")

model = load_model()

#####################################
# Streamlit Interface
#####################################
st.title("Resume Analyzer and Company Suitability Checker")
st.markdown(
    """
Upload your resume file in **.doc** or **.docx** format. The app extracts key details such as name, age, job experience, skills, 
and education, and summarizes them into a single paragraph. Then, it compares the candidate summary with a company profile
(using a pre-defined prompt for Google LLC) to produce a suitability score.
"""
)

# File uploader for resume
uploaded_file = st.file_uploader("Upload Resume", type=["doc", "docx"])

# Button to process the resume and store the summary in session state.
if st.button("Process Resume"):
    if uploaded_file is None:
        st.error("Please upload a resume file first.")
    else:
        with st.spinner("Processing resume..."):
            candidate_summary = process_resume(uploaded_file)
            st.session_state["candidate_summary"] = candidate_summary
        st.subheader("Candidate Summary")
        st.markdown(candidate_summary)

# Pre-define the 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"):
    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, model)
            st.success(f"Suitability Score: {score:.2f} (range 0 to 1)")