import streamlit as st
import pickle
import re
import numpy as np

# ✅ Page Config
st.set_page_config(page_title="Stack Overflow Tags Predictor", layout="wide")

# ✅ Enhanced CSS for a modern UI
st.markdown("""
    <style>
    body {
        background: linear-gradient(to right, #e3f2fd, #ffffff);
    }

    .main-container {
        max-width: 850px;
        margin: auto;
        padding: 2rem;
        border-radius: 16px;
        background: rgba(255, 255, 255, 0.9);
        box-shadow: 0 8px 24px rgba(0, 0, 0, 0.1);
    }

    .title-text {
        font-size: 2.4rem;
        font-weight: bold;
        text-align: center;
        color: #1e3a8a;
        margin-bottom: 0.5rem;
    }

    .description-text {
        font-size: 1.1rem;
        text-align: center;
        color: #333;
        margin-bottom: 2rem;
    }

    .stTextInput input, .stTextArea textarea {
        border-radius: 8px;
        padding: 12px;
        border: 1px solid #ccc;
        background-color: #fafafa;
    }

    .stButton button {
        background: linear-gradient(to right, #1e88e5, #1976d2);
        color: white;
        border-radius: 8px;
        padding: 0.6rem 1.4rem;
        font-weight: 600;
        transition: 0.3s ease;
        border: none;
    }

    .stButton button:hover {
        background: linear-gradient(to right, #1565c0, #0d47a1);
    }

    .tag {
        display: inline-block;
        margin: 5px 6px 0 0;
        background-color: #0ea5e9;
        padding: 6px 14px;
        border-radius: 20px;
        font-size: 0.85rem;
        color: white;
        font-weight: 500;
    }

    .result-container {
        text-align: center;
        margin-top: 2rem;
    }
    </style>
""", unsafe_allow_html=True)

# ✅ Text Preprocessing
def clean_text(text):
    text = re.sub(r"<.*?>", " ", text)
    text = re.sub(r"\W", " ", text)
    text = re.sub(r"\s+", " ", text.lower()).strip()
    return text

# ✅ Load Pickled Artifacts
@st.cache_resource
def load_artifacts():
    with open("model12.pkl", "rb") as f:
        model = pickle.load(f)
    with open("tfidf12.pkl", "rb") as f:
        vectorizer = pickle.load(f)
    with open("mlb12.pkl", "rb") as f:
        mlb = pickle.load(f)
    return model, vectorizer, mlb

model, vectorizer, mlb = load_artifacts()

# ✅ UI Container
with st.container():
    st.markdown("<div class='main-container'>", unsafe_allow_html=True)

    st.markdown("<div class='title-text'>🔖 Stack Overflow Tags Predictor</div>", unsafe_allow_html=True)
    st.markdown("<div class='description-text'>Enter your question title and description to generate the most relevant tags using Machine Learning.</div>", unsafe_allow_html=True)

    with st.form(key="tag_prediction_form"):
        title = st.text_input("📝 Enter Question Title")
        body = st.text_area("📄 Enter Question Description", height=180)
        threshold = st.slider("🔧 Tag Confidence Threshold", min_value=0.1, max_value=0.9, value=0.3, step=0.05)
        submitted = st.form_submit_button("🔍 Predict Tags")

    if submitted:
        if not title.strip() or not body.strip():
            st.warning("⚠️ Please fill in both the title and description.")
        else:
            with st.spinner("🔍 Predicting the most relevant tags..."):
                input_text = clean_text(title + " " + body)
                X_input = vectorizer.transform([input_text])

                try:
                    y_prob = model.predict_proba(X_input)
                    y_pred = (y_prob >= threshold).astype(int)
                except AttributeError:
                    y_pred = model.predict(X_input)

                predicted_tags = mlb.inverse_transform(y_pred)

            with st.container():
                st.markdown("<div class='result-container'>", unsafe_allow_html=True)
                if predicted_tags and predicted_tags[0]:
                    st.success("✅ Tags Predicted Successfully!")
                    tag_html = "".join([f"<span class='tag'>{tag}</span>" for tag in predicted_tags[0]])
                    st.markdown(tag_html, unsafe_allow_html=True)
                else:
                    st.info("🤔 No tags predicted. Try refining your input or lowering the threshold.")
                st.markdown("</div>", unsafe_allow_html=True)

    st.markdown("</div>", unsafe_allow_html=True)