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Delete app.py

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  1. app.py +0 -62
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- # ---
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- # jupyter:
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- # jupytext:
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- # text_representation:
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- # extension: .py
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- # format_name: light
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- # format_version: '1.5'
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- # jupytext_version: 1.16.4
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- # kernelspec:
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- # display_name: Python 3 (ipykernel)
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- # language: python
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- # name: python3
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- # ---
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-
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- # +
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- import streamlit as st
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- import pandas as pd
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- import numpy as np
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- import faiss
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- from sentence_transformers import SentenceTransformer
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- from huggingface_hub import hf_hub_download
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- import warnings
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- warnings.filterwarnings('ignore')
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-
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- @st.cache_resource
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- def load_artifacts():
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- repo_id = "PankhuriSharma9795/SHL_Model_Assets"
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-
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- # Load SBERT model
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- model_dir = hf_hub_download(repo_id="PankhuriSharma9795/SHL_model_Asset", filename="config.json", repo_type="model")
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- model = SentenceTransformer(model_dir.replace("config.json", ""))
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-
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- # Load FAISS index
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- faiss_path = hf_hub_download(repo_id="PankhuriSharma9795/SHL_model_Asset", filename="faiss_index.index", repo_type="model")
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- index = faiss.read_index(faiss_path)
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-
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- # Load CSV
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- csv_path = hf_hub_download(repo_id="PankhuriSharma9795/SHL_model_Asset", filename="assessment_data.csv", repo_type="model")
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- df = pd.read_csv(csv_path)
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-
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- return model, index, df
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-
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- def recommend_assessments(profile_text, model, index, df, top_n=10):
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- profile_embedding = model.encode([profile_text]).astype('float32')
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- _, indices = index.search(profile_embedding, top_n)
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- return df.iloc[indices[0]]
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-
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- # Streamlit UI
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- st.title("🔍 SHL Assessment Recommender")
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-
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- profile = st.text_area("✍️ Enter your job role or career aspiration:",
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- "Looking for a leadership role in financial planning and client management")
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-
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- if st.button("Get Recommendations"):
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- model, index, df = load_artifacts()
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- results = recommend_assessments(profile, model, index, df, top_n=10)
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- st.subheader("🧠 Top 10 Matching Assessments")
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- st.dataframe(results[['Assesment Name', 'cleaned_text', 'Duration',
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- 'Remote Testing Support', 'URL', 'Adaptive/IRT', 'Job Type']].reset_index(drop=True))
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- # -
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-
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-