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import streamlit as st
import os
import pandas as pd
import faiss
import math
from sentence_transformers import SentenceTransformer
from dotenv import load_dotenv
import google.generativeai as genai
# Load environment variables and configure Gemini
load_dotenv()
genai.configure(api_key=os.getenv("API_KEY"))
gemini_model = genai.GenerativeModel("gemini-2.0-flash")
# Load vector index and dataframe
index = faiss.read_index("shl_vector_index.faiss")
df = pd.read_csv("shl_combined_assessments.csv")
model = SentenceTransformer("all-MiniLM-L6-v2", device="cpu")
# Helper to format the result row
def format_row(row):
def safe_cast(val, cast_type, default):
try:
if val is None or (isinstance(val, float) and math.isnan(val)):
return default
return cast_type(val)
except Exception:
return default
return {
"Assignment_Name": str(row["Assignment_Name"]),
"Assignment_Link": str(row["Assignment_Link"]),
"Test_Type": str(row["Test_Type"]),
"Approximate_Completion_Time": safe_cast(row["Approximate_Completion_Time"], int, -1),
"Remote_Testing_Support": bool(row["Remote_Testing_Support"]),
"Adaptive_IRT_Support": bool(row["Adaptive_IRT_Support"]),
"Job_Levels": str(row.get("Job_Levels", "N/A")),
}
# Streamlit UI
st.set_page_config(page_title="SHL Assessment Recommender", layout="centered")
st.title("🔍 SHL Assessment Recommender")
query = st.text_input("Enter your job role or requirement:", "")
if st.button("Search") and query:
with st.spinner("Finding best assessments..."):
query_embedding = model.encode([query]).astype("float32")
D, I = index.search(query_embedding, 10)
results = [format_row(df.iloc[idx]) for idx in I[0]]
st.subheader("🔝 Top Recommendations")
if results:
for idx, r in enumerate(results, 1):
with st.container():
st.markdown(f"### 🏆 Rank {idx}: {r['Assignment_Name']}")
st.markdown(f"[🔗 Assignment Link]({r['Assignment_Link']})")
st.markdown(f"- 🧪 **Test Type**: {r['Test_Type']}")
st.markdown(f"- ⏱️ **Duration**: {r['Approximate_Completion_Time']} mins")
st.markdown(f"- 🌐 **Remote Testing**: {r['Remote_Testing_Support']}")
st.markdown(f"- 📊 **Adaptive/IRT**: {r['Adaptive_IRT_Support']}")
st.markdown(f"- 👤 **Job Levels**: {r['Job_Levels']}")
st.markdown("---")
else:
st.warning("No results found.")
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