DreamStream-1 commited on
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696a781
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1 Parent(s): d5951d6

Update app.py

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  1. app.py +3 -17
app.py CHANGED
@@ -155,13 +155,13 @@ def analyze_resume(resume_text):
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  f"This resume shows strong managerial responsibilities: {resume_text}",
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  f"This resume demonstrates excellent leadership skills: {resume_text}",
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  f"This resume indicates significant work experience: {resume_text}",
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- f"This resume highlights at least 2 years of relevant experience: {resume_text}"
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  ]
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- # Analyze each prompt using the model
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  results = []
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  for prompt in prompts:
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- inputs = tokenizer(prompt, return_tensors="pt", truncation=True) # Truncate if needed
 
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  outputs = model(**inputs)
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  predicted_class = torch.argmax(outputs.logits).item()
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  results.append(predicted_class)
@@ -208,20 +208,6 @@ if uploaded_file and job_description:
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  data['Email'] = email if email != "Not Available" else "Not Available"
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  data['Contact'] = contact if contact != "Not Available" else "Not Available"
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- # Extract team leadership and management experience
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- team_leadership_years = extract_experience_years(resume_text)
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- management_years = extract_experience_years(resume_text)
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- data['Direct_Team_Leadership_Experience_Years'] = team_leadership_years if team_leadership_years > 0 else "Not Available"
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- data['Direct_Management_Experience_Years'] = management_years if management_years > 0 else "Not Available"
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-
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- # Extract skills using the NER model
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- relevant_skills = extract_skills(resume_text)
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- data['Relevant_Skills_and_Qualifications'] = relevant_skills if relevant_skills != "Not Available" else "Not Available"
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-
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- # Extract education using the NER model or regex
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- educational_background = extract_education(resume_text)
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- data['Educational_Background'] = educational_background if educational_background != "Not Available" else "Not Available"
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-
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  # Calculate match percentage dynamically
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  match_percentage = calculate_match_percentage(resume_text, job_description)
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  data['Match_Percentage'] = match_percentage
 
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  f"This resume shows strong managerial responsibilities: {resume_text}",
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  f"This resume demonstrates excellent leadership skills: {resume_text}",
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  f"This resume indicates significant work experience: {resume_text}",
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+ f"This resume indicates at least 2 years of relevant experience: {resume_text}"
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  ]
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  results = []
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  for prompt in prompts:
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+ # Tokenize the prompt with truncation
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+ inputs = tokenizer(prompt, return_tensors="pt", truncation=True)
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  outputs = model(**inputs)
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  predicted_class = torch.argmax(outputs.logits).item()
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  results.append(predicted_class)
 
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  data['Email'] = email if email != "Not Available" else "Not Available"
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  data['Contact'] = contact if contact != "Not Available" else "Not Available"
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  # Calculate match percentage dynamically
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  match_percentage = calculate_match_percentage(resume_text, job_description)
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  data['Match_Percentage'] = match_percentage