| import streamlit as st
|
| from recommender_api import SHLRecommender
|
| import time
|
|
|
| def main():
|
| st.set_page_config(
|
| page_title="SHL- Assessment Recommender System",
|
| page_icon="📊",
|
| layout="wide"
|
| )
|
|
|
|
|
| try:
|
| recommender = SHLRecommender()
|
| except Exception as e:
|
| st.error(f"Failed to initialize recommender: {str(e)}")
|
| st.stop()
|
|
|
|
|
| st.sidebar.title("Filters")
|
| category = st.sidebar.selectbox(
|
| "Assessment Category",
|
| options=["All"] + recommender.get_categories()
|
| )
|
| duration_filter = st.sidebar.slider(
|
| "Maximum Duration (minutes)",
|
| min_value=15,
|
| max_value=120,
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| value=60,
|
| step=5
|
| )
|
|
|
|
|
| st.title("SHL Assessment Recommendation System")
|
| st.write("Find the perfect SHL assessment for your hiring needs")
|
|
|
|
|
| query = st.text_area(
|
| "Describe your needs:",
|
| placeholder="e.g., We need a cognitive test for software engineers under 45 minutes",
|
| height=150
|
| )
|
|
|
| if st.button("Get Recommendations"):
|
| if not query.strip():
|
| st.warning("Please enter a description of your needs")
|
| else:
|
| with st.spinner("Finding the best assessments..."):
|
| try:
|
| start_time = time.time()
|
| recommendations = recommender.recommend(
|
| query,
|
| category=None if category == "All" else category,
|
| duration_max=duration_filter
|
| )
|
| elapsed = time.time() - start_time
|
|
|
| if not recommendations:
|
| st.warning("No matching assessments found. Try broadening your filters.")
|
| else:
|
| st.success(f"Found {len(recommendations)} recommendations in {elapsed:.2f} seconds")
|
|
|
| for i, rec in enumerate(recommendations, 1):
|
| with st.expander(f"{i}. {rec['name']} (Score: {rec['score']:.2f})"):
|
| cols = st.columns([1, 3])
|
| with cols[0]:
|
| st.markdown(f"**Test Link**: {rec['url']}")
|
| st.markdown(f"**Category**: {rec['category']}")
|
| st.markdown(f"**Duration**: {rec['duration']}")
|
| st.markdown(f"**Remote**: {'Yes' if rec['remote'] else 'No'}")
|
| st.markdown(f"**Adaptive**: {'Yes' if rec['adaptive'] else 'No'}")
|
|
|
| with cols[1]:
|
| st.markdown(f"**Description**: {rec['description']}")
|
| if rec.get('skills_tested'):
|
| st.markdown(f"**Skills Tested**: {', '.join(rec['skills_tested'])}")
|
| if rec.get('use_cases'):
|
| st.markdown(f"**Best For**: {', '.join(rec['use_cases'])}")
|
|
|
| st.markdown(f"[View Details]({rec['url']})")
|
|
|
| except Exception as e:
|
| st.error(f"Error generating recommendations: {str(e)}")
|
|
|
| if __name__ == "__main__":
|
| main() |
