Update app.py

app.py

@@ -1,68 +1,56 @@
-import streamlit as st
 import pickle
+import streamlit as st
+import os
+import numpy as np
 
-# ✅ Set page config FIRST
-st.set_page_config(page_title="Stack Overflow Tag Predictor", page_icon="🔖")
+# 💡 Define the custom tokenizer exactly as used during training
+def custom_tokenizer(text):
+    # Modify this function to match your original tokenizer logic
+    return text.lower().split()
 
-# --- Load Model Components ---
-@st.cache_resource
-def load_components():
-    try:
-        with open("vectorizer.pkl", "rb") as f:
-            vectorizer = pickle.load(f)
-        with open("model (2).pkl", "rb") as f:
-            model = pickle.load(f)
-        with open("binarizer.pkl", "rb") as f:
-            mlb = pickle.load(f)
-        return vectorizer, model, mlb
-    except Exception as e:
-        st.error(f"🚨 Failed to load model files: {e}")
-        st.stop()
 
-vectorizer, model, mlb = load_components()
 
-# --- Custom Tokenizer (if used during training) ---
-def custom_tokenizer(text):
-    return text.lower().split()
+# 🔃 Load model files
+try:
+    with open("vectorizer.pkl", "rb") as f:
+        vectorizer = pickle.load(f)
 
-# --- Tag Prediction ---
-def predict_tags(title, description):
-    if not title.strip() or not description.strip():
-        return "⚠️ Please enter both title and description."
+    with open("model (2).pkl", "rb") as f:
+        model = pickle.load(f)
+
+    with open("binarizer.pkl", "rb") as f:
+        mlb = pickle.load(f)
+
+except Exception as e:
+    st.error(f"❌ Error loading model files: {str(e)}")
+    st.stop()
 
+# 🧠 Prediction function
+def predict_tags(title, description):
     try:
-        input_text = title.strip() + " " + description.strip()
+        if not title.strip() or not description.strip():
+            return "⚠️ Please enter both title and description."
+
+        input_text = title + " " + description
         input_vector = vectorizer.transform([input_text])
         prediction = model.predict(input_vector)
         predicted_tags = mlb.inverse_transform(prediction)
 
         if predicted_tags and predicted_tags[0]:
-            tag_list = ", ".join(predicted_tags[0])
-            return f"🎯 **Predicted Tags:** `{tag_list}`"
+            return "✅ Predicted Tags: " + ", ".join(predicted_tags[0])
         else:
             return "ℹ️ No tags predicted. Try refining your question."
 
     except Exception as e:
         return f"❌ Error during prediction: {str(e)}"
 
-# --- UI Styling and Layout ---
-st.markdown(
-    """
-    <style>
-    .title { font-size: 36px; font-weight: 700; color: #4A90E2; }
-    .desc { font-size: 18px; margin-bottom: 20px; }
-    .result-box { background-color: #f9f9f9; padding: 15px; border-radius: 8px; margin-top: 20px; }
-    </style>
-    """,
-    unsafe_allow_html=True
-)
-
-st.markdown('<div class="title">🔖 Stack Overflow Tag Predictor</div>', unsafe_allow_html=True)
-st.markdown('<div class="desc">Enter a Stack Overflow question title and description to get the most relevant tags.</div>', unsafe_allow_html=True)
+# 🚀 Streamlit UI
+st.title("🔖:red[ Stack Overflow Tags Predictor]")
+st.markdown(":blue[Enter a question title and description to predict relevant tags.]")
 
-title = st.text_input("📌 Question Title")
-description = st.text_area("📝 Question Description", height=150)
+title = st.text_input("📌 Enter Question Title")
+description = st.text_area("📝 Enter Question Description", height=150)
 
-if st.button("🚀 Predict Tags"):
+if st.button("Predict Tags"):
     result = predict_tags(title, description)
-    st.markdown(f'<div class="result-box">{result}</div>', unsafe_allow_html=True)
+    st.markdown(result)