manasvinid commited on
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6d0f67b
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1 Parent(s): 96d38bc

Update app.py

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  1. app.py +26 -1
app.py CHANGED
@@ -82,6 +82,9 @@ jobs_data_summarized = batch_summarize(jobs_data_final, 'processed_description',
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  # Summarize all 'processed_resume' in resume_data_final
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  resume_data_summarized = batch_summarize(resume_data_final, 'processed_resume', summarizer, batch_size=10, output_col='summarized_resume')
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  # Example Usage
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  encoder = SentenceTransformerEncoder(model_name='all-MiniLM-L6-v2')
@@ -92,6 +95,9 @@ jobs_data_summarized_and_encoded = encoder.encode_column(jobs_data_summarized, '
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  # Encoding the summarized resumes
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  resume_data_summarized_and_encoded = encoder.encode_column(resume_data_summarized, 'summarized_resume')
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  # Combine the jobs data
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  jobs_combined = pd.merge(
@@ -110,6 +116,11 @@ jobs_combined.reset_index(drop=True, inplace=True)
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  resume_combined.reset_index(drop=True, inplace=True)
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  #QDRANT VECTORIZER
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  vector_dimension = encoder.model.get_sentence_embedding_dimension()
@@ -126,6 +137,12 @@ def ensure_list_format(df, vector_col):
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  jobs_combined = ensure_list_format(jobs_combined, 'summarized_description_encoded')
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  resume_combined = ensure_list_format(resume_combined, 'summarized_resume_encoded')
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  given_job_vector = jobs_combined['summarized_description_encoded'].iloc[0]
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  # Now upload to Qdrant
@@ -135,10 +152,18 @@ qdrant_interface.save_to_qdrant(resume_combined, 'resumes', 'summarized_resume_e
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  # Retrieve specific records by IDs from the 'jobs' collection
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  specific_jobs_records = qdrant_interface.retrieve_specific_records('jobs', ids=[1])
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  # Find top 5 matching resumes for the example job
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  matched_resumes = qdrant_interface.match_jobs_to_resumes(given_job_vector, top_k=5)
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  for resume, score in matched_resumes:
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- print(f"Matched Resume: {resume['summarized_resume']}, Score: {score}")
 
 
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  # Summarize all 'processed_resume' in resume_data_final
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  resume_data_summarized = batch_summarize(resume_data_final, 'processed_resume', summarizer, batch_size=10, output_col='summarized_resume')
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+ st.write("SUMMARISED")
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+ st.write(jobs_data_summarized)
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+ st.write(resume_data_summarized)
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  # Example Usage
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  encoder = SentenceTransformerEncoder(model_name='all-MiniLM-L6-v2')
 
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  # Encoding the summarized resumes
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  resume_data_summarized_and_encoded = encoder.encode_column(resume_data_summarized, 'summarized_resume')
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+ st.write("SUMMARISED AND ENCODED")
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+ st.write(jobs_data_summarized_and_encoded)
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+ st.write(resume_data_summarized_and_encoded)
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  # Combine the jobs data
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  jobs_combined = pd.merge(
 
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  resume_combined.reset_index(drop=True, inplace=True)
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+ st.write("SUMMARISED AND ENCODED")
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+ st.write(jobs_combined)
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+ st.write(resume_combined)
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+
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+
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  #QDRANT VECTORIZER
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  vector_dimension = encoder.model.get_sentence_embedding_dimension()
 
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  jobs_combined = ensure_list_format(jobs_combined, 'summarized_description_encoded')
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  resume_combined = ensure_list_format(resume_combined, 'summarized_resume_encoded')
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+
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+ st.write("LIST FORMAT")
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+ st.write(jobs_combined)
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+ st.write(resume_combined)
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+
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+
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  given_job_vector = jobs_combined['summarized_description_encoded'].iloc[0]
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  # Now upload to Qdrant
 
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  # Retrieve specific records by IDs from the 'jobs' collection
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  specific_jobs_records = qdrant_interface.retrieve_specific_records('jobs', ids=[1])
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+
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+ st.write("SPECIFIC JOB RECS")
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+ st.write(specific_jobs_records)
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+
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+
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+
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  # Find top 5 matching resumes for the example job
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  matched_resumes = qdrant_interface.match_jobs_to_resumes(given_job_vector, top_k=5)
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  for resume, score in matched_resumes:
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+ st.write(f"Matched Resume: {resume['summarized_resume']}, Score: {score}")
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+
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+
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