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SushasanSampleData.csv

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+applicationId,applicationDetail,applicationCategoryName,applicationSubCategoryName,ulbName,wardName
+1,पानी की पाइपलाइन टूटी हुई है,शिकायत,जल आपूर्ति,नगर पालिका,वार्ड 1
+2,नई स्ट्रीट लाइट लगाने की मांग,मांग,बिजली व्यवस्था,नगर पालिका,वार्ड 2

app.py

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+
+import streamlit as st
+import pandas as pd
+import torch
+from transformers import AutoTokenizer, AutoModel
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder
+from imblearn.over_sampling import RandomOverSampler
+
+@st.cache_resource
+def load_model_and_tokenizer():
+    tokenizer = AutoTokenizer.from_pretrained("ai4bharat/indic-bert")
+    model = AutoModel.from_pretrained("ai4bharat/indic-bert")
+    return tokenizer, model
+
+def get_embeddings(texts, tokenizer, model):
+    inputs = tokenizer(texts, return_tensors="pt", padding=True, truncation=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+    embeddings = outputs.last_hidden_state[:, 0, :]  # CLS token
+    return embeddings
+
+@st.cache_data
+def load_data():
+    df = pd.read_csv("SushasanSampleData.csv", encoding="utf-8")
+    df['applicationDetail'] = df['applicationDetail'].fillna("")
+    df['applicationCategoryName'] = df['applicationCategoryName'].fillna("अन्य")
+    return df
+
+@st.cache_resource
+def preprocess_and_train(df):
+    tokenizer, model = load_model_and_tokenizer()
+    text_embeddings = get_embeddings(df['applicationDetail'].tolist(), tokenizer, model)
+    text_embeddings = text_embeddings.cpu().numpy()
+
+    label_encoder = LabelEncoder()
+    labels = label_encoder.fit_transform(df['applicationCategoryName'])
+
+    ros = RandomOverSampler(random_state=42)
+    X_resampled, y_resampled = ros.fit_resample(text_embeddings, labels)
+
+    X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=0.2, random_state=42)
+
+    clf = LogisticRegression(max_iter=1000)
+    clf.fit(X_train, y_train)
+
+    return clf, tokenizer, model, label_encoder
+
+df = load_data()
+clf, tokenizer, model, label_encoder = preprocess_and_train(df)
+
+# Streamlit UI
+st.title("🇮🇳 Hindi Category Classifier (IndicBERT Powered)")
+
+user_input = st.text_area("✍️ Enter Application Detail", "")
+
+if st.button("🔍 Predict"):
+    if user_input.strip() == "":
+        st.warning("Please write something.")
+    else:
+        user_emb = get_embeddings([user_input], tokenizer, model)
+        user_emb = user_emb.cpu().numpy()
+        prediction = clf.predict(user_emb)
+        label = label_encoder.inverse_transform(prediction)[0]
+        st.success(f"🧠 Predicted Category: **{label}**")

requirements.txt

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+
+streamlit
+pandas
+scikit-learn==1.3.2
+imbalanced-learn==0.11.0
+transformers
+torch
+sentencepiece