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

# 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 extract_text_from_pdf(pdf_file):
    """Extract text from the uploaded PDF resume."""
    with pdfplumber.open(pdf_file) as pdf:
        text = ""
        for page in pdf.pages:
            text += page.extract_text()
    return text

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']]

    # 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": ", ".join(skills),
    }

    return parsed_data

def process_resumes(pdf_files):
    """Process multiple resumes and output a single Excel file."""
    all_parsed_data = []

    # Loop through each uploaded PDF file and parse the data
    for pdf_file in pdf_files:
        resume_text = extract_text_from_pdf(pdf_file)
        parsed_info = parse_resume(resume_text)
        all_parsed_data.append(parsed_info)

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

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

    return output_file

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