Update app.py

app.py

@@ -1,6 +1,5 @@
 import pickle
 import streamlit as st
-import numpy as np
 
 # Load saved model, vectorizer, and binarizer
 with open("vectorizer (3).pkl", "rb") as f:
@@ -13,42 +12,25 @@ with open("binarizer (3).pkl", "rb") as f:
     mlb = pickle.load(f)
 
 st.title("🔖 Stack Overflow Tags Predictor")
-st.markdown("Enter a question title and description. The top 3 most relevant tags will be predicted automatically.")
+st.markdown("Enter a question title and description. Tags will be predicted automatically.")
 
-# Input fields
 title = st.text_input("📌 Enter Question Title")
 description = st.text_area("📝 Enter Question Description", height=150)
 
-# Function to predict top N tags (e.g., top 3)
-def predict_tags_top_n(title, description, top_n=3):
+def predict_tags(title, description):
+    if not title.strip() or not description.strip():
+        return []
     input_text = title + " " + description
     input_vector = vectorizer.transform([input_text])
-
-    # Get probability estimates from each classifier
-    probas = model.predict_proba(input_vector)
-
-    # Extract positive class probabilities for each tag
-    probas_array = np.array([p[0][1] for p in probas])  # shape: (n_classes,)
-
-    # Get indices of top N tags
-    top_indices = probas_array.argsort()[-top_n:][::-1]
-
-    # Build binary array for top tags
-    predicted_binary = np.zeros_like(probas_array, dtype=int)
-    predicted_binary[top_indices] = 1
-    predicted_binary = predicted_binary.reshape(1, -1)
-
-    # Convert binary to tag names
+    
+    # Predict tags using the model's internal default threshold
+    predicted_binary = model.predict(input_vector)
     tags = mlb.inverse_transform(predicted_binary)
     return tags[0] if tags else []
 
-# Predict and display
 if st.button("Predict Tags"):
-    if not title.strip() or not description.strip():
-        st.warning("⚠️ Please enter both title and description.")
+    tags = predict_tags(title, description)
+    if tags:
+        st.success("✅ Predicted Tags: " + ", ".join(tags))
     else:
-        predicted_tags = predict_tags_top_n(title, description, top_n=3)
-        if predicted_tags:
-            st.success("✅ Predicted Tags: " + ", ".join(predicted_tags))
-        else:
-            st.info("ℹ️ No tags predicted. Try refining your question.")
+        st.info("ℹ️ No tags predicted. Try refining your question.")