Upload app.py

app.py

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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")
+
+# 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.")