Create model.py

model.py

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+import pdfplumber
+import spacy
+import re
+
+# Load NLP Model
+nlp = spacy.load("en_core_web_sm")
+
+# Predefined ATS-friendly keywords
+REQUIRED_SKILLS = {"Python", "Machine Learning", "NLP", "Deep Learning", "AI", "Data Science", "Cloud", "Hugging Face"}
+
+def extract_text_from_pdf(pdf_path):
+    text = ""
+    with pdfplumber.open(pdf_path) as pdf:
+        for page in pdf.pages:
+            text += page.extract_text() + "\n"
+    return text.strip()
+
+def analyze_resume(text):
+    doc = nlp(text)
+
+    # Extract skills and education
+    words = set([token.text for token in doc if token.is_alpha])
+    matched_skills = REQUIRED_SKILLS.intersection(words)
+    missing_skills = REQUIRED_SKILLS - matched_skills
+
+    # Basic ATS Check
+    ats_score = (len(matched_skills) / len(REQUIRED_SKILLS)) * 100
+
+    return {
+        "matched_skills": list(matched_skills),
+        "missing_skills": list(missing_skills),
+        "ats_score": round(ats_score, 2)
+    }