Update app.py

app.py

@@ -69,15 +69,15 @@ def extract_ner_info(text, nlp):
     age = None
 
     for i in range(len(ner_results)):
-        if ner_results[i]['entity'] == 'B-PER':
+        if ner_results[i]['entity'] == 'B-pers':
             full_name = ner_results[i]['word']
             for j in range(i+1, len(ner_results)):
-                if ner_results[j]['entity'].startswith('I-PER'):
+                if ner_results[j]['entity'].startswith('I-pers'):
                     full_name += ner_results[j]['word'].replace('##', '')
                 else:
                     break
         
-        if ner_results[i]['entity'] == 'B-LOC' and not loc:
+        if ner_results[i]['entity'] == 'I-fac' and not loc:
             loc = ner_results[i]['word']
 
     age_match = re.search(r'سن\s*:\s*(\d+)', text)
@@ -86,6 +86,7 @@ def extract_ner_info(text, nlp):
     
     return full_name, loc, age
 
+
 def process_text(input_text):
     # مسیر فایل اکسل‌ها را وارد کنید
     job_excel_file_path = 'jobs_output.xlsx'
@@ -167,7 +168,7 @@ def process_text(input_text):
     skill_score, common_skills = compare_skills(skills_in_fixed_text, skills_in_input_text)
 
     # تنظیم و آماده‌سازی مدل NER
-    model_name_or_path = "HooshvareLab/distilbert-fa-zwnj-base-ner"
+    model_name_or_path = "NLPclass/Named-entity-recognition"
     tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
     model = AutoModelForTokenClassification.from_pretrained(model_name_or_path)  # Pytorch
     nlp = pipeline("ner", model=model, tokenizer=tokenizer)