Create app.py

app.py

@@ -0,0 +1,56 @@
+import pickle
+import streamlit as st
+import os
+import numpy as np
+
+# 💡 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 files
+try:
+    with open("vectorizer (3).pkl", "rb") as f:
+        vectorizer = pickle.load(f)
+
+    with open("model (6).pkl", "rb") as f:
+        model = pickle.load(f)
+
+    with open("binarizer (3).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:
+        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)
+        st.write(predicted_tags)
+        if predicted_tags and predicted_tags[0]:
+            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)}"
+
+# 🚀 Streamlit UI
+st.title("🔖 Stack Overflow Tags Predictor")
+st.markdown("Enter a question title and description to predict relevant tags.")
+
+title = st.text_input("📌 Enter Question Title")
+description = st.text_area("📝 Enter Question Description", height=150)
+
+if st.button("Predict Tags"):
+    result = predict_tags(title, description)
+    st.markdown(result)