app.py

# -*- coding: utf-8 -*-
"""Feedalytics Resume Intelligence - Version2 - Block 2 (Colab-Ready)"""

from docx import Document
import openai
import gradio as gr
import PyPDF2
import re

# -----------------------------
# OpenAI API Key
# -----------------------------
openai.api_key = "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"  # <-- Replace with your key

# -----------------------------
# User credentials
# -----------------------------
USER_CREDENTIALS = {"admin": "password123"}

# -----------------------------
# Functions to extract resume text
# -----------------------------
def extract_text_from_docx(file):
    doc = Document(file.name)
    return '\n'.join([para.text for para in doc.paragraphs])

def extract_text_from_pdf(file):
    try:
        reader = PyPDF2.PdfReader(file.name)
        text = ''
        for page in reader.pages:
            text += page.extract_text() or ''
        return text.strip()
    except Exception as e:
        return ""

def get_resume_text(file):
    if file.name.endswith(".docx"):
        return extract_text_from_docx(file)
    elif file.name.endswith(".pdf"):
        return extract_text_from_pdf(file)
    else:
        return ""

# -----------------------------
# Function to evaluate resumes
# -----------------------------
def evaluate_resumes(resumes, job_description, model="gpt-3.5-turbo-16k"):
    shortlisted, potential, rejected = [], [], []

    for i, resume_text in enumerate(resumes):
        if not resume_text:
            continue

        prompt = f"""
        Job Description:
        {job_description}

        Candidate Resume:
        {resume_text}

        Evaluate this resume and provide:
        1. Candidate's Name
        2. Match score (0-10), based on the below-mentioned criteria
           Score Range   Category           Interpretation\n
            "0 – 4      ❌ Poor Match      Resume content does not align with the job description. Few or no required skills, responsibilities, or relevant experience are mentioned.\n"
            "5 – 6      ⚠️ Partial Match    Resume shows some alignment, but several required skills, experiences, or educational requirements are missing. Candidate might need retraining or be applying for a mismatched role.\n"
            "6 – 7      ⚙️ Moderate Match   Resume meets most of the requirements but lacks depth or one major qualification (e.g., missing a key tool or specific experience).\n"
            "7 – 8      ✅ Good Match       Resume matches the majority of the job description. Required skills and relevant experience are clearly stated. Candidate seems capable of performing well.\n"
            "8 – 9      💪 Strong Match     Resume closely matches both skills and experience. The candidate clearly meets or exceeds most requirements, including tools, certifications, and outcomes.\n"
            "9 – 10     🌟 Excellent Match   Resume is a near-perfect fit — covers all required skills, relevant experience, and qualifications. Uses similar language and metrics to the job description.\n\n"
        3. Brief justification (2-4 sentences), solely on the basis of match and ignore errors caused by varying resume types (pdf.word.docx)
        """

        try:
            response = openai.ChatCompletion.create(
                model=model,
                messages=[{"role": "user", "content": prompt}],
                max_tokens=500,
                temperature=0
            )

            evaluation = response['choices'][0]['message']['content'].strip()

            # Extract numeric score
            match = re.search(r"(\d{1,3})\s*%", evaluation)
            score = int(match.group(1)) if match else 0

            candidate_label = f"Candidate {i+1} ({resumes[i].name})"

            if score >= 90:
                shortlisted.append(f"{candidate_label}:\n{evaluation}\n{'-'*30}")
            elif score >= 65:
                potential.append(f"{candidate_label}:\n{evaluation}\n{'-'*30}")
            else:
                rejected.append(f"{candidate_label}:\n{evaluation}\n{'-'*30}")

        except Exception as e:
            rejected.append(f"Candidate {i+1} ({resumes[i].name}): Error evaluating resume - {e}")

    return (
        "\n\n".join(shortlisted) if shortlisted else "No shortlisted candidates.",
        "\n\n".join(potential) if potential else "No potential candidates.",
        "\n\n".join(rejected) if rejected else "No rejected candidates."
    )

# -----------------------------
# Function to handle uploaded files
# -----------------------------
def process_uploaded_resumes(job_file, resume_files):
    if not job_file:
        return "Job description required.", "", ""
    if not resume_files:
        return "Upload at least one resume.", "", ""

    job_description = extract_text_from_docx(job_file)
    resume_texts = [get_resume_text(f) for f in resume_files]

    return evaluate_resumes(resume_files, job_description)

# -----------------------------
# Login function
# -----------------------------
def authenticate(username, password):
    if username in USER_CREDENTIALS and USER_CREDENTIALS[username] == password:
        return gr.update(visible=False), gr.update(visible=True)
    else:
        return gr.update(visible=True), gr.update(visible=False)

# -----------------------------
# Gradio UI
# -----------------------------
def create_interface():
    with gr.Blocks() as interface:
        # Login group
        login_group = gr.Group(visible=True)
        with login_group:
            gr.Markdown("### Login to Resume Shortlisting App")
            username = gr.Textbox(label="Username")
            password = gr.Textbox(label="Password", type="password")
            login_btn = gr.Button("Login")

        # Main app group
        app_group = gr.Group(visible=False)
        with app_group:
            gr.Markdown("### Resume Shortlisting Application")
            job_file = gr.File(label="Upload Job Description (.docx)")
            resume_files = gr.File(label="Upload Resumes (.pdf or .docx)", file_types=[".pdf", ".docx"], file_count="multiple")
            submit_btn = gr.Button("Evaluate Resumes")

            shortlisted = gr.Textbox(label="Shortlisted Candidates", lines=10)
            potential = gr.Textbox(label="Potential Candidates", lines=10)
            rejected = gr.Textbox(label="Rejected Candidates", lines=10)

            submit_btn.click(process_uploaded_resumes, [job_file, resume_files], [shortlisted, potential, rejected])

        login_btn.click(authenticate, [username, password], [login_group, app_group])

    return interface

# -----------------------------
# Launch app
# -----------------------------
iface = create_interface()
iface.launch(debug=True, share=True)