Update skill_matcher.py

skill_matcher.py

@@ -1,20 +1,20 @@
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.metrics.pairwise import cosine_similarity
-import json
-
-def load_job_descriptions(path='data/job_descriptions.json'):
-    with open(path, 'r') as f:
-        jobs = json.load(f)
-    return jobs
-
-def compare_with_jobs(resume_text, job_descriptions):
-    results = []
-    vectorizer = TfidfVectorizer().fit_transform([resume_text] + [job["description"] for job in job_descriptions])
-    vectors = vectorizer.toarray()
-
-    resume_vector = vectors[0]
-    for idx, job in enumerate(job_descriptions):
-        job_vector = vectors[idx + 1]
-        similarity = cosine_similarity([resume_vector], [job_vector])[0][0]
-        results.append({"role": job["role"], "match": round(similarity * 100, 2)})
-    return sorted(results, key=lambda x: x["match"], reverse=True)
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+import json
+
+def load_job_descriptions(path='job_descriptions.json'):
+    with open(path, 'r') as f:
+        jobs = json.load(f)
+    return jobs
+
+def compare_with_jobs(resume_text, job_descriptions):
+    results = []
+    vectorizer = TfidfVectorizer().fit_transform([resume_text] + [job["description"] for job in job_descriptions])
+    vectors = vectorizer.toarray()
+
+    resume_vector = vectors[0]
+    for idx, job in enumerate(job_descriptions):
+        job_vector = vectors[idx + 1]
+        similarity = cosine_similarity([resume_vector], [job_vector])[0][0]
+        results.append({"role": job["role"], "match": round(similarity * 100, 2)})
+    return sorted(results, key=lambda x: x["match"], reverse=True)