Update app.py

app.py

@@ -2,7 +2,7 @@ import pickle
 import streamlit as st
 import numpy as np
 
-# Load saved model, vectorizer, and binarizer
+# --- Load saved model, vectorizer, and binarizer ---
 with open("vectorizer (3).pkl", "rb") as f:
     vectorizer = pickle.load(f)
 
@@ -12,37 +12,37 @@ with open("model (6).pkl", "rb") as f:
 with open("binarizer (3).pkl", "rb") as f:
     mlb = pickle.load(f)
 
+# --- Streamlit App UI ---
 st.title("🔖 Stack Overflow Tags Predictor")
 st.markdown("Enter a question title and description. Tags will be predicted automatically based on model confidence.")
 
 title = st.text_input("📌 Enter Question Title")
 description = st.text_area("📝 Enter Question Description", height=150)
 
-# Threshold for prediction
-threshold = 0.4  # You can tweak this value to make it more or less strict
-
+# --- Prediction Logic ---
 def predict_tags_auto(title, description, threshold=0.4):
     input_text = title + " " + description
     input_vector = vectorizer.transform([input_text])
-    
-    # Get probabilities for each tag
-    probas = model.predict_proba(input_vector)
-    
-    # Convert list of (1, n_classes) probs to array
-    probas = np.array([p[0] for p in probas])
-    
+
+    # Get class probabilities from model
+    probas_list = model.predict_proba(input_vector)  # List of arrays
+
+    # Convert list of (1, n_classes) to shape (1, n_classes)
+    proba_matrix = np.hstack([p[:, 1] if p.shape[1] == 2 else p[:, 0] for p in probas_list])
+
     # Apply threshold
-    predicted_binary = (probas >= threshold).astype(int)
-    
-    # Convert binary vector to tags
+    predicted_binary = (proba_matrix >= threshold).astype(int).reshape(1, -1)
+
+    # Decode binary predictions to tag labels
     tags = mlb.inverse_transform(predicted_binary)
-    return tags[0] if tags else []
+    return tags[0] if tags and len(tags[0]) > 0 else []
 
+# --- Predict Button ---
 if st.button("Predict Tags"):
     if not title.strip() or not description.strip():
         st.warning("⚠️ Please enter both title and description.")
     else:
-        tags = predict_tags_auto(title, description)
+        tags = predict_tags_auto(title, description, threshold=0.4)
         if tags:
             st.success("✅ Predicted Tags: " + ", ".join(tags))
         else: