app.py

import gradio as gr
import pdfplumber
import textstat
import spacy
from transformers import pipeline
import language_tool_python

# Load tools
sentiment_pipeline = pipeline("sentiment-analysis")
nlp = spacy.load("en_core_web_sm")
tool = language_tool_python.LanguageTool('en-US')

def analyze_resume(uploaded_pdf, keywords):
    try:
        # Read PDF content
        with pdfplumber.open(uploaded_pdf.name) as pdf:
            text = ""
            for page in pdf.pages:
                text += page.extract_text() or ""

        # Basic stats
        word_count = len(text.split())
        readability_score = textstat.flesch_reading_ease(text)

        # Sentiment
        sentiment = sentiment_pipeline(text[:512])[0]  # Limit text to avoid overload

        # Keyword matching
        keyword_list = [kw.strip().lower() for kw in keywords.split(',')]
        matched_keywords = [kw for kw in keyword_list if kw in text.lower()]

        # NER
        doc = nlp(text)
        named_entities = [(ent.text, ent.label_) for ent in doc.ents]

        # Grammar & Spelling
        matches = tool.check(text)
        grammar_issues = len(matches)

        # Resume grade (mock logic)
        resume_grade = "Excellent" if readability_score > 60 and grammar_issues < 5 else "Needs Improvement"

        return (
            readability_score,
            sentiment['label'],
            word_count,
            f"{len(matched_keywords)} matched: {', '.join(matched_keywords)}",
            str(named_entities),
            f"{grammar_issues} issues",
            resume_grade
        )

    except Exception as e:
        return [f"Error: {str(e)}"] * 7


# Gradio UI
iface = gr.Interface(
    fn=analyze_resume,
    inputs=[
        gr.File(label="Upload Resume (PDF)"),
        gr.Textbox(label="Job Keywords (comma-separated)")
    ],
    outputs=[
        gr.Textbox(label="Readability Score"),
        gr.Textbox(label="Sentiment"),
        gr.Textbox(label="Word Count"),
        gr.Textbox(label="Keyword Match and Matched Keywords"),
        gr.Textbox(label="Named Entities (NER)"),
        gr.Textbox(label="Grammar and Spelling Suggestions"),
        gr.Textbox(label="Resume Grade")
    ],
    title="Resume Analyzer"
)

iface.launch()