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f36dc0e cb95dc4 1d25616 f36dc0e 1d25616 f36dc0e 9ef44b0 f36dc0e 3c6eb62 f36dc0e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 | import streamlit as st
import pandas as pd
import joblib
import numpy as np
import string
import nltk
from nltk.corpus import stopwords as stp
from nltk import pos_tag, word_tokenize as w, sent_tokenize as s
from nltk.stem import WordNetLemmatizer as wl
nltk.download('punkt_tab')
# Download necessary NLTK data
#nltk.download('punkt', quiet=True)
#nltk.download('averaged_perceptron_tagger', quiet=True)
#nltk.download('wordnet', quiet=True)
#nltk.download('stopwords', quiet=True)
nltk.download('punkt')
nltk.download('averaged_perceptron_tagger_eng')
nltk.download('wordnet')
nltk.download('stopwords')
# === Cleaning Function ===
def sahi_karneka_function(x):
nouns=[]
li=[]
lem=wl()
l=s(x)
for i in l:
d=w(i.lower())
for k in d:
li.append(k)
lw=len(li)
j=0
while j<lw:
if li[j] in string.punctuation:
li.remove(li[j])
lw=len(li)
j=0
elif li[j] in stp.words("english"):
li.remove(li[j])
lw=len(li)
j=0
else:
j=j+1
tags=pos_tag(li)
for word,tag in tags:
if tag.startswith("NN") or tag.startswith("V"):
nouns.append(word)
semi_final_words=[lem.lemmatize(m,pos="n") if tagg.startswith("NN") else lem.lemmatize(m,pos="v") for m,tagg in pos_tag(nouns)]
final_sentence=" ".join(semi_final_words)
return final_sentence
# === Load Data and Models ===
df = pd.read_csv(r"c_d.csv")
model = joblib.load("logistic_models.pkl")
tfidf = joblib.load("tfidf.pkl")
ml = joblib.load("multilabels.pkl")
# === Streamlit UI ===
st.title("🧠 Enhancing Developer Support: Automated Tagging on Stack Overflow")
# --- Select a URL for context ---
selected_url = st.selectbox("Select a question URL (for context):", df['questions_url'])
st.markdown(f"🔗 [Open selected question]({selected_url})")
# --- Session State ---
if "user_input" not in st.session_state:
st.session_state["user_input"] = ""
if "clear_input" not in st.session_state:
st.session_state["clear_input"] = False
# --- Clear input if flagged (AFTER rerun) ---
if st.session_state.clear_input:
st.session_state.user_input = ""
st.session_state.clear_input = False
# --- Input box ---
st.text_area("✍️ Type your question here:", key="user_input", height=150)
# --- Predict button ---
if st.button("Predict Tags"):
final_question = st.session_state.user_input.strip()
if not final_question:
st.warning("⚠️ Please enter a question.")
else:
with st.spinner("🔍 Predicting tags..."):
# Step 1: Clean input
cleaned = sahi_karneka_function(final_question)
# Step 2: TF-IDF
f=[]
f.append(cleaned)
x_tfidf = tfidf.transform(f)
# Step 3: Predict
y_probs = model.predict_proba(x_tfidf)
threshold = 0.55
y_predd=model.predict(x_tfidf)
probs_column1 = np.array([i[:, 1] for i in y_probs]).T
y_pred = (probs_column1 >= threshold).astype(int)
# Step 4: Decode
predicted_tags = ml.inverse_transform(y_predd)
# Step 5: Display results
st.success("✅ Predicted Tags:")
if predicted_tags and predicted_tags[0]:
for tag in predicted_tags[0]:
st.markdown(f"🔹 **`{tag}`**")
else:
st.info("No tags matched the threshold.")
# Step 6: Show a "Clear" button
if st.button("Clear Input"):
st.session_state.user_input = ""
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