Delete src

src/app .py

@@ -1,159 +0,0 @@
-import streamlit as st
-import joblib
-import pandas as pd
-import re
-from unidecode import unidecode
-import emoji
-import string
-import contractions
-from nltk.stem import PorterStemmer
-import numpy as np
-from sklearn.feature_extraction.text import ENGLISH_STOP_WORDS
-
-# Custom CSS Styling
-st.markdown("""
-<style>
-    .stApp {
-        background-color: #f9fbfc;
-        font-family: 'Segoe UI', sans-serif;
-    }
-    .custom-header {
-        background-color: #1e293b;
-        color: white;
-        padding: 2rem;
-        border-radius: 0.5rem;
-        text-align: center;
-        margin-bottom: 2rem;
-    }
-    .custom-header h1 {
-        font-size: 2rem;
-        margin-bottom: 0.5rem;
-    }
-    .custom-header p {
-        font-size: 1rem;
-        color: #cbd5e1;
-    }
-    .input-box, .output-box {
-        background-color: white;
-        padding: 1.5rem;
-        border-radius: 0.5rem;
-        box-shadow: 0 0 10px rgba(0, 0, 0, 0.04);
-        margin-bottom: 2rem;
-    }
-    .tag-pill {
-        display: inline-block;
-        background-color: #e0f2fe;
-        color: #0369a1;
-        padding: 0.4em 0.8em;
-        margin: 0.25em;
-        border-radius: 999px;
-        font-weight: 600;
-        font-size: 0.9rem;
-    }
-    .footer {
-        text-align: center;
-        font-size: 0.85rem;
-        color: #64748b;
-        margin-top: 2rem;
-    }
-</style>
-""", unsafe_allow_html=True)
-
-# Header
-st.markdown("""
-<div class="custom-header">
-    <h1>📌 StackOverflow Tag Predictor</h1>
-    <p>Enter a programming question to see predicted tags</p>
-</div>
-""", unsafe_allow_html=True)
-
-# Initialize components
-stemmer = PorterStemmer()
-stop_words = set(ENGLISH_STOP_WORDS)
-chat_words = {
-    "brb": "be right back", "btw": "by the way", "lol": "laugh out loud",
-    "afaik": "as far as i know", "imo": "in my opinion", "tbh": "to be honest",
-    "idk": "i don't know", "asap": "as soon as possible", "np": "no problem",
-    "thx": "thanks", "pls": "please", "fyi": "for your information"
-}
-
-def preprocess_text(text):
-    if not isinstance(text, str) or not text.strip():
-        return ""
-    try:
-        text = re.sub(r'<[^>]+>', '', text)
-        text = re.sub(r'https?://\S+|www\.\S+', '', text)
-        text = emoji.demojize(text, delimiters=(" ", " "))
-        text = unidecode(text)
-        text = contractions.fix(text)
-        text = text.lower()
-        words = text.split()
-        text = " ".join([chat_words.get(word.lower(), word) for word in words])
-        text = text.translate(str.maketrans('', '', string.punctuation))
-        tokens = re.findall(r'\b\w+\b', text)
-        tokens = [word for word in tokens if word not in stop_words]
-        tokens = [stemmer.stem(word) for word in tokens]
-        return " ".join(tokens)
-    except Exception as e:
-        st.error(f"Preprocessing error: {e}")
-        return ""
-
-@st.cache_resource
-def load_models():
-    try:
-        model = joblib.load("tag_model.joblib")
-        mlb = joblib.load("tag_binarizer.joblib")
-        return model, mlb
-    except Exception as e:
-        st.error(f"Error loading model: {e}")
-        return None, None
-
-model, mlb = load_models()
-
-# Input
-st.markdown('<div class="input-box">', unsafe_allow_html=True)
-user_input = st.text_area("✍️ Paste your programming question below:", height=200, placeholder="e.g., How to reverse a list in Python?")
-st.markdown('</div>', unsafe_allow_html=True)
-
-# Prediction
-if st.button("🚀 Predict Tags"):
-    if not user_input.strip():
-        st.warning("Please enter your question to get predictions.")
-    elif model is None or mlb is None:
-        st.error("Model loading failed.")
-    else:
-        with st.spinner("Processing..."):
-            processed = preprocess_text(user_input)
-            if processed:
-                try:
-                    input_df = pd.DataFrame({'processed_excerpt': [processed]})
-                    if hasattr(model, "predict_proba"):
-                        probs = model.predict_proba(input_df)[0]
-                        top_idx = np.argsort(probs)[-5:][::-1]
-                        tags = [mlb.classes_[i] for i in top_idx]
-                        confs = [int(probs[i] * 100) for i in top_idx]
-                    elif hasattr(model, "decision_function"):
-                        scores = model.decision_function(input_df)[0]
-                        top_idx = np.argsort(scores)[-5:][::-1]
-                        tags = [mlb.classes_[i] for i in top_idx]
-                        confs = [None] * 5
-                    else:
-                        preds = model.predict(input_df)
-                        tags = mlb.inverse_transform(preds)[0]
-                        confs = [None] * len(tags)
-
-                    # Output
-                    st.markdown('<div class="output-box"><h4>🏷️ Predicted Tags:</h4>', unsafe_allow_html=True)
-                    for tag, conf in zip(tags, confs):
-                        confidence = f" ({conf}%)" if conf is not None else ""
-                        st.markdown(f'<span class="tag-pill">{tag}{confidence}</span>', unsafe_allow_html=True)
-                    st.markdown('</div>', unsafe_allow_html=True)
-                except Exception as e:
-                    st.error(f"Prediction error: {e}")
-
-# Footer
-st.markdown("""
-<div class="footer">
-    🔎 This ML tool predicts tags based on programming question content.
-</div>
-""", unsafe_allow_html=True)

src/requirements .txt

@@ -1,9 +0,0 @@
-streamlit
-pandas
-numpy
-scikit-learn
-nltk
-joblib
-unidecode
-emoji
-contractions

src/streamlit_app.py

@@ -1,40 +0,0 @@
-import altair as alt
-import numpy as np
-import pandas as pd
-import streamlit as st
-
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))