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import io
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import json
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from typing import Optional
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import gradio as gr
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import PyPDF2
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from resume_ai import score, improve
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def extract_text_from_pdf(file_obj: io.IOBase) -> str:
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"""Extract text from a PDF file-like object."""
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try:
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reader = PyPDF2.PdfReader(file_obj)
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text_chunks = []
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for page in reader.pages:
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page_text = page.extract_text() or ""
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text_chunks.append(page_text)
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text = "\n".join(text_chunks).strip()
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if not text:
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raise ValueError("No extractable text found in PDF.")
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return text
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except Exception as e:
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raise ValueError(f"Error reading PDF: {e}")
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def read_resume_to_text(resume_file_path) -> str:
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"""
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Accepts a file path and returns text content.
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Supports PDF and plain text files.
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"""
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if resume_file_path is None:
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raise ValueError("Please upload a resume file.")
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filename = str(resume_file_path).lower()
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if filename.endswith(".pdf"):
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with open(resume_file_path, "rb") as f:
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return extract_text_from_pdf(f)
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else:
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with open(resume_file_path, "rb") as f:
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data = f.read()
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if not data:
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raise ValueError("Uploaded file is empty.")
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try:
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return data.decode("utf-8").strip()
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except UnicodeDecodeError:
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return data.decode("latin-1").strip()
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def score_fn(resume_file_path, job_desc: str) -> str:
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try:
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if not job_desc or not job_desc.strip():
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raise ValueError("Please paste a job description.")
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resume_text = read_resume_to_text(resume_file_path)
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result = score(resume_text, job_desc)
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return json.dumps(result, indent=2, ensure_ascii=False)
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except Exception as e:
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return f"Error: {e}"
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def improve_fn(resume_file_path, job_desc: Optional[str]) -> str:
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try:
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resume_text = read_resume_to_text(resume_file_path)
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jd_text = job_desc if job_desc and job_desc.strip() else None
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suggestions = improve(resume_text, jd_text)
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if isinstance(suggestions, (list, tuple)):
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bullets = "\n".join(f"- {s}" for s in suggestions)
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return f"### Suggestions\n{bullets}"
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elif isinstance(suggestions, dict):
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return "```json\n" + json.dumps(suggestions, indent=2, ensure_ascii=False) + "\n```"
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else:
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return str(suggestions)
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except Exception as e:
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return f"Error: {e}"
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def format_score_display(result_json) -> str:
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"""
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Takes the result JSON (as dict or str), parses it, and returns a Markdown string for display.
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"""
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if isinstance(result_json, str):
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try:
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result = json.loads(result_json)
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except Exception:
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return f"```\n{result_json}\n```"
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else:
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result = result_json
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md = f"## π ATS Compatibility Score: **{result.get('overall_score', 0)}%**\n\n"
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md += "### Category Scores\n"
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md += "| Skills | Experience | Education |\n"
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md += "|--------|------------|-----------|\n"
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cs = result.get("category_scores", {})
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md += f"| {cs.get('skills',0)}% | {cs.get('experience',0)}% | {cs.get('education',0)}% |\n\n"
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gaps = result.get("top_skill_gaps", [])
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if gaps:
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md += "### π© Top Skill Gaps\n"
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for gap in gaps:
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md += f"- {gap}\n"
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return md
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with gr.Blocks(title="Resume AI (Score & Improve)") as demo:
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gr.Markdown(
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"""
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# π Resume AI β Score & Improve
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Upload your resume (PDF or TXT), paste a Job Description, and get:
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- **Score**: A formatted breakdown of your resume's ATS compatibility
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- **Improve**: A healthy set of suggestions to enhance your resume
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"""
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)
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with gr.Row():
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resume = gr.File(label="Upload Resume (PDF or TXT)", file_types=[".pdf", ".txt"], type="filepath")
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jd = gr.Textbox(label="Job Description (paste here)", lines=10, placeholder="Paste JD text...")
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with gr.Row():
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score_btn = gr.Button("βοΈ Score Resume", variant="primary")
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improve_btn = gr.Button("β¨ Improve Resume")
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score_out = gr.Markdown(label="Score (Formatted)")
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improve_out = gr.Markdown(label="Improvement Suggestions")
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def score_fn_display(resume_file_path, job_desc: str) -> str:
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try:
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if not job_desc or not job_desc.strip():
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raise ValueError("Please paste a job description.")
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resume_text = read_resume_to_text(resume_file_path)
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result = score(resume_text, job_desc)
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return format_score_display(result)
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except Exception as e:
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return f"Error: {e}"
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score_btn.click(fn=score_fn_display, inputs=[resume, jd], outputs=score_out)
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improve_btn.click(fn=improve_fn, inputs=[resume, jd], outputs=improve_out)
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if __name__ == "__main__":
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demo.queue().launch() |