Spaces:
Sleeping
Sleeping
| import streamlit as st | |
| import pandas as pd | |
| from src.data_preparation import load_data | |
| from src.model import initialize_openai, fine_tune_model, extract_keywords, rate_skills, compare_candidates | |
| st.title("MLE Trial Task") | |
| # Upload OpenAI API Key | |
| api_key = st.text_input("Enter your OpenAI API Key", type="password") | |
| initialize_openai(api_key) | |
| # Load training data | |
| data = load_data("data/train_dataset.csv") | |
| # Fine-tune the model | |
| fine_tuned_model = "gpt-4o-2024-08-06" | |
| # Streamlit app sections | |
| st.sidebar.title("Navigation") | |
| page = st.sidebar.radio("Go to", ["Evaluate Test Data", "Test with Random Data"]) | |
| if page == "Evaluate Test Data": | |
| st.title("Evaluate Test Data") | |
| uploaded_test_file = st.file_uploader("Upload Test Data CSV", type="csv") | |
| if uploaded_test_file: | |
| test_data = pd.read_csv(uploaded_test_file) | |
| correct_predictions = 0 | |
| total_predictions = len(test_data) | |
| for index, row in test_data.iterrows(): | |
| job_description = row['role'] | |
| candidateA_resume = row['candidateAResume'] | |
| candidateB_resume = row['candidateBResume'] | |
| candidateA_transcript = row['candidateATranscript'] | |
| candidateB_transcript = row['candidateBTranscript'] | |
| winner_id = row['winnerId'] | |
| candidateA_details = { | |
| "keywords": extract_keywords(candidateA_resume, job_description, fine_tuned_model), | |
| "skills": rate_skills(candidateA_transcript, job_description, fine_tuned_model), | |
| "candidate_id": row['candidateAId'] | |
| } | |
| candidateB_details = { | |
| "keywords": extract_keywords(candidateB_resume, job_description, fine_tuned_model), | |
| "skills": rate_skills(candidateB_transcript, job_description, fine_tuned_model), | |
| "candidate_id": row['candidateBId'] | |
| } | |
| preferred_candidate = compare_candidates(candidateA_details, candidateB_details, job_description, fine_tuned_model) | |
| print(f"Preferred Candidate: {preferred_candidate}", f"Winner ID: {winner_id}") | |
| if preferred_candidate == winner_id: | |
| correct_predictions += 1 | |
| accuracy = correct_predictions / total_predictions | |
| st.write(f"Accuracy: {accuracy}") | |
| st.write(f"Preferred Candidates: {correct_predictions} out of {total_predictions}") | |
| elif page == "Test with Random Data": | |
| st.title("Test with Random Data") | |
| job_description = st.text_area("Job Description") | |
| candidateA_resume = st.text_area("Candidate A Resume") | |
| candidateB_resume = st.text_area("Candidate B Resume") | |
| candidateA_transcript = st.text_area("Candidate A Transcript") | |
| candidateB_transcript = st.text_area("Candidate B Transcript") | |
| if st.button("Compare Candidates"): | |
| candidateA_details = { | |
| "keywords": extract_keywords(candidateA_resume, job_description, fine_tuned_model), | |
| "skills": rate_skills(candidateA_transcript, job_description, fine_tuned_model) | |
| } | |
| candidateB_details = { | |
| "keywords": extract_keywords(candidateB_resume, job_description, fine_tuned_model), | |
| "skills": rate_skills(candidateB_transcript, job_description, fine_tuned_model) | |
| } | |
| preferred_candidate = compare_candidates(candidateA_details, candidateB_details, job_description, fine_tuned_model) | |
| st.write(f"Preferred Candidate: {preferred_candidate}") |