Create resume_ranker.py

Author: Pathuz21

resume_ranker.py

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+import os
+import argparse
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+import PyPDF2
+
+def extract_text_from_pdf(pdf_path):
+    text = ""
+    try:
+        with open(pdf_path, "rb") as file:
+            reader = PyPDF2.PdfReader(file)
+            for page in reader.pages:
+                text += page.extract_text() or ""
+    except Exception as e:
+        print(f"Error reading PDF: {e}")
+    return text
+
+def read_text_file(path):
+    try:
+        with open(path, "r", encoding="utf-8") as f:
+            return f.read()
+    except Exception as e:
+        print(f"Error reading file: {e}")
+        return ""
+
+def calculate_similarity(resume_text, jd_text):
+    tfidf = TfidfVectorizer(stop_words='english')
+    tfidf_matrix = tfidf.fit_transform([resume_text, jd_text])
+    score = cosine_similarity(tfidf_matrix[0:1], tfidf_matrix[1:2])[0][0]
+    return round(score * 100, 2)  # percentage match
+
+def main():
+    parser = argparse.ArgumentParser(description="AI Resume Ranker")
+    parser.add_argument('--resume', required=True, help="Path to resume file (.txt or .pdf)")
+    parser.add_argument('--jd', required=True, help="Path to job description (.txt)")
+
+    args = parser.parse_args()
+
+    resume_path = args.resume
+    jd_path = args.jd
+
+    # Load resume
+    if resume_path.lower().endswith(".pdf"):
+        resume_text = extract_text_from_pdf(resume_path)
+    elif resume_path.lower().endswith(".txt"):
+        resume_text = read_text_file(resume_path)
+    else:
+        print("Unsupported resume file type. Use .pdf or .txt")
+        return
+
+    # Load job description
+    jd_text = read_text_file(jd_path)
+
+    if not resume_text.strip() or not jd_text.strip():
+        print("Both resume and job description must contain text.")
+        return
+
+    score = calculate_similarity(resume_text, jd_text)
+    print(f"\n🔍 Resume Match Score: {score}%")
+
+    if score > 80:
+        print("✅ Strong match! Consider applying.")
+    elif score > 60:
+        print("⚠️ Decent match. Tailoring may help.")
+    else:
+        print("❌ Low match. Consider improving the resume.")
+
+if __name__ == "__main__":
+    main()