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logistic_models.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b721e454d2e9e4251456b86182971c0282017377c6d9dcfff79cbc8d5c4d9c85
+size 163858041

multilabels.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7f423ab92cb9f10e49ddb3562cff87cc01d5aabe61dfae0cd148e5929f3fe64b
+size 148869

streamlit_app.py

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+import os
+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_DATA_PATH = "/app/nltk_data"
+os.makedirs(NLTK_DATA_PATH, exist_ok=True)
+os.environ["NLTK_DATA"] = NLTK_DATA_PATH
+nltk.download('punkt_tab', quiet=True)
+nltk.download('punkt_tab', download_dir=NLTK_DATA_PATH, quiet=True)
+nltk.download('punkt', download_dir=NLTK_DATA_PATH, quiet=True)
+nltk.download('averaged_perceptron_tagger', download_dir=NLTK_DATA_PATH, quiet=True)
+nltk.download('wordnet', download_dir=NLTK_DATA_PATH, quiet=True)
+nltk.download('stopwords', download_dir=NLTK_DATA_PATH, quiet=True)
+# 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', download_dir=NLTK_DATA_PATH, quiet=True)
+nltk.download('averaged_perceptron_tagger_eng', download_dir=NLTK_DATA_PATH, quiet=True)
+nltk.download('wordnet', download_dir=NLTK_DATA_PATH, quiet=True)
+nltk.download('stopwords', download_dir=NLTK_DATA_PATH, quiet=True)
+
+# === 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"src/c_d.csv")
+model = joblib.load("src/logistic_models.pkl")
+tfidf = joblib.load("src/tfidf.pkl")
+ml = joblib.load("src/multilabels.pkl")
+
+# === Streamlit UI ===
+st.title("🧠 Multi-Label Question Tag Predictor")
+
+# --- 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 = ""

tfidf.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0ae3d0aac7f2381331f394013961dec02a2ca090c88ff16234d5225beed994a9
+size 107299