Update app.py

app.py

@@ -9,7 +9,15 @@ from Levenshtein import distance as levenshtein_distance
 from textdistance import jaro_winkler, damerau_levenshtein, cosine
 from sklearn.feature_extraction.text import CountVectorizer
 from sklearn.preprocessing import normalize
+import pandas as pd
 
+# -----------------------
+# 🎨 Streamlit App Layout
+# -----------------------
+
+st.title("🔍 String Similarity & Model Calibration App")
+st.sidebar.header("📌 Select an Option")
+option = st.sidebar.radio("Choose a Task:", ["String Similarity", "Model Calibration"])
 
 # -----------------------
 # ℹ️ PROJECT INFORMATION & EDUCATION SECTION
@@ -80,6 +88,23 @@ if option == "String Similarity":
         st.write(f"**Cosine Similarity:** {cosine_sim:.4f}")
         st.write(f"**Q-Gram Similarity:** {qgram_sim:.4f}")
 
+    # -----------------------
+    # 📊 STRING SIMILARITY EXAMPLES TABLE
+    # -----------------------
+
+    st.subheader("📊 Example Word Comparisons")
+    data = {
+        "Word 1": ["MARTHA", "HOUSE", "SUNDAY", "NIGHT", "FLIGHT"],
+        "Word 2": ["MARHTA", "HORSE", "MONDAY", "KNIGHT", "FIGHT"],
+        "Levenshtein Distance": [1, 2, 2, 2, 1],
+        "Jaro-Winkler Similarity": [0.9611, 0.8375, 0.8222, 0.9444, 0.9740],
+        "Damerau-Levenshtein Distance": [1, 1, 2, 1, 1],
+        "Cosine Similarity": [0.8333, 0.7500, 0.6667, 0.8000, 0.9500],
+        "Q-Gram Similarity": [0.8571, 0.7143, 0.6667, 0.7778, 0.9231],
+    }
+    df = pd.DataFrame(data)
+    st.table(df)
+
 # -----------------------
 # 2️⃣ MODEL CALIBRATION (RELIABILITY DIAGRAM)
 # -----------------------