app.py

import pandas as pd
import streamlit as st 
import pandas as pd

from functions import *

from qdrant_client import QdrantClient
from qdrant_client.http.models import VectorParams, Distance, Record, Filter
from random import uniform




backgroundPattern = """
<style>
[data-testid="stAppViewContainer"] {
    background-color: #0E1117;
    opacity: 1;
    background-image: radial-gradient(#282C34 0.75px, #0E1117 0.75px);
    background-size: 15px 15px;
}
</style>
"""

st.markdown(backgroundPattern, unsafe_allow_html=True)

st.write("""
# Resume Screening & Classification
""")

st.header('Input')
jobs_data= job_desc_pdf()
resume_data= resume_pdf()

st.write('input to df:')
st.write(jobs_data)
st.write(resume_data)

# setup_nltk_resources()

# # Unzip wordnet
# corpora_path = "/kaggle/working/nltk_data/corpora"
# wordnet_zip = os.path.join(corpora_path, "wordnet.zip")
# unzip_nltk_resource(wordnet_zip, corpora_path)

# Apply preprocessing
jobs_data['processed_description'] = jobs_data['description'].apply(preprocess_text)
jobs_data_cleaned = drop_duplicates(jobs_data, column_name='description')



resume_data['processed_resume'] = resume_data['Resume'].apply(preprocess_text)
resume_data_cleaned = drop_duplicates(resume_data, column_name='Resume')

st.write("CLEANED")
st.write(jobs_data_cleaned)
st.write(resume_data_cleaned)


jobs_data_cleaned_with_tokens = add_token_count_column(jobs_data_cleaned, column_name='processed_description')
resume_data_cleaned_with_tokens = add_token_count_column(resume_data_cleaned, column_name='processed_resume')




# Dropping unnecessary columns from jobs data
jobs_data_final = jobs_data_cleaned_with_tokens[['processed_description', 'token_count']]

# Dropping unnecessary columns from resume data
resume_data_final = resume_data_cleaned_with_tokens[['processed_resume', 'token_count']]

st.write("CLEANED WITH TOKENS")
st.write(jobs_data_final)
st.write(resume_data_final)

summarizer = TextSummarizer("geekradius/bart-large-cnn-fintetuned-samsum-repo")
st.write("sum")

# Summariz jobs description
jobs_data_summarized = batch_summarize(jobs_data_final, 'processed_description', summarizer, batch_size=10, output_col='summarized_description')

# Summarize all 'processed_resume' in resume_data_final
resume_data_summarized = batch_summarize(resume_data_final, 'processed_resume', summarizer, batch_size=10, output_col='summarized_resume')

st.write("SUMMARISED")
st.write(jobs_data_summarized)
st.write(resume_data_summarized)

# Example Usage
encoder = SentenceTransformerEncoder("all-MiniLM-L6-v2")

# Encoding the summarized job descriptions
jobs_data_summarized_and_encoded = encoder.encode_column(jobs_data_summarized, 'summarized_description')

# Encoding the summarized resumes
resume_data_summarized_and_encoded = encoder.encode_column(resume_data_summarized, 'summarized_resume')

st.write("SUMMARISED AND ENCODED")
st.write(jobs_data_summarized_and_encoded)
st.write(resume_data_summarized_and_encoded)

# Combine the jobs data
jobs_combined = pd.merge(
    jobs_data_final,
    jobs_data_summarized_and_encoded[['summarized_description', 'summarized_description_encoded']],
    left_index=True, right_index=True)

# Combine the resume data
resume_combined = pd.merge(
    resume_data_final,
    resume_data_summarized_and_encoded[['summarized_resume', 'summarized_resume_encoded']],
    left_index=True, right_index=True)

# Reset index of DataFrame
jobs_combined.reset_index(drop=True, inplace=True)
resume_combined.reset_index(drop=True, inplace=True)


st.write("SUMMARISED AND ENCODED")
st.write(jobs_combined)
st.write(resume_combined)


#QDRANT VECTORIZER

vector_dimension = encoder.model.get_sentence_embedding_dimension()
qdrant_interface = QdrantInterface(QUADRANT_ENDPOINT, QUADRANT_API_KEY, vector_dimension)
qdrant_interface.create_collection('jobs', Distance.COSINE)
qdrant_interface.create_collection('resumes', Distance.COSINE)

# Function to ensure vectors are in list format
def ensure_list_format(df, vector_col):
    df[vector_col] = df[vector_col].apply(lambda x: x.tolist() if hasattr(x, 'tolist') else x)
    return df

# Ensure vectors are in the correct format before uploading
jobs_combined = ensure_list_format(jobs_combined, 'summarized_description_encoded')
resume_combined = ensure_list_format(resume_combined, 'summarized_resume_encoded')


st.write("LIST FORMAT")
st.write(jobs_combined)
st.write(resume_combined)


given_job_vector = jobs_combined['summarized_description_encoded'].iloc[0]

# Now upload to Qdrant
qdrant_interface.save_to_qdrant(jobs_combined, 'jobs', 'summarized_description_encoded', ['processed_title', 'processed_description', 'token_count', 'summarized_description'])
qdrant_interface.save_to_qdrant(resume_combined, 'resumes', 'summarized_resume_encoded', ['processed_resume', 'token_count', 'summarized_resume'])

# Retrieve specific records by IDs from the 'jobs' collection
specific_jobs_records = qdrant_interface.retrieve_specific_records('jobs', ids=[1])


st.write("SPECIFIC JOB RECS")
st.write(specific_jobs_records)



# Find top 5 matching resumes for the example job
matched_resumes = qdrant_interface.match_jobs_to_resumes(given_job_vector, top_k=5)
for resume, score in matched_resumes:
    st.write(f"Matched Resume: {resume['summarized_resume']}, Score: {score}")