app.py

import gradio as gr
import torch
from transformers import pipeline
import pandas as pd
import re

# Load pre-trained model for Named Entity Recognition (NER) to extract details
nlp = pipeline("ner", model="dbmdz/bert-large-cased-finetuned-conll03-english", framework="pt")

def parse_resume(resume_text):
    """Parse the resume and extract details like name, email, phone, and skills."""
    # Define regex for phone and email extraction
    phone_pattern = r'\(?\d{3}\)?[-.\s]?\d{3}[-.\s]?\d{4}'
    email_pattern = r'[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}'

    # Extract phone and email using regex
    phone = re.findall(phone_pattern, resume_text)
    email = re.findall(email_pattern, resume_text)

    # Extract named entities for skills
    entities = nlp(resume_text)
    skills = [entity['word'] for entity in entities if 'MISC' in entity['entity']]

    # Handle case if no skills found
    skills = ", ".join(skills) if skills else "No skills found"

    # Create a dictionary of parsed data (exclude Experience, Education, Certifications)
    parsed_data = {
        "Phone": phone[0] if phone else "Not found",
        "Email": email[0] if email else "Not found",
        "Skills": skills,
    }

    return parsed_data

def process_resumes(csv_file):
    """Process a CSV file of resumes and output a single Excel file."""
    # Read the CSV file
    df = pd.read_csv(csv_file.name)

    # Ensure the column with resume text is named 'Resume' (you can adjust this as needed)
    if 'Resume' not in df.columns:
        return "Error: The CSV file must contain a 'Resume' column."

    all_parsed_data = []

    # Loop through each row in the CSV and parse the resume text
    for _, row in df.iterrows():
        resume_text = row['Resume']  # Assuming the column name is 'Resume'
        parsed_info = parse_resume(resume_text)
        all_parsed_data.append(parsed_info)

    # Convert the parsed data into a pandas DataFrame
    parsed_df = pd.DataFrame(all_parsed_data)

    # Save the DataFrame to an Excel file
    output_file = "parsed_resumes.xlsx"
    parsed_df.to_excel(output_file, index=False)

    return output_file

# Define Gradio interface
gr.Interface(
    fn=process_resumes,
    inputs=gr.File(file_count="single", label="Upload Resume CSV"),
    outputs=gr.File(label="Download Parsed Data (Excel)"),
    title="AI Resume Parser",
    description="Upload a CSV file containing resume texts to extract details like Name, Email, Phone, and Skills. The results will be saved in an Excel file."
).launch()