import streamlit as st
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import LabelEncoder
from imblearn.over_sampling import RandomOverSampler
from sklearn.model_selection import train_test_split

@st.cache_data
def load_data():
    df = pd.read_csv("SushasanSampleData.csv", encoding='utf-8')
    df = df.drop(columns=['ulbName', 'wardName'])
    df['applicationId'] = df['applicationId'].astype(str)
    df['applicationSubCategoryName'] = df['applicationSubCategoryName'].fillna("अन्य")
    return df

@st.cache_resource
def train_model(df):
    tfidf = TfidfVectorizer(max_features=5000)
    X = tfidf.fit_transform(df['applicationDetail'])

    label_encoder = LabelEncoder()
    y = label_encoder.fit_transform(df['applicationCategoryName'])

    ros = RandomOverSampler(random_state=42)
    X_resampled, y_resampled = ros.fit_resample(X, y)

    X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=0.2, random_state=42)

    model = LogisticRegression(max_iter=1000)
    model.fit(X_train, y_train)

    return model, tfidf, label_encoder

# Load and train
df = load_data()
model, tfidf, label_encoder = train_model(df)

# UI
st.title("🧾 Hindi Application Category Classifier")
st.markdown("Enter a grievance or demand in Hindi. The model will predict whether it is a **मांग** (Demand) or a **शिकायत** (Complaint).")

user_input = st.text_area("✍️ Application Detail", "")

if st.button("🔍 Predict Category"):
    if user_input.strip() == "":
        st.warning("Please enter some text.")
    else:
        input_vector = tfidf.transform([user_input])
        prediction = model.predict(input_vector)
        label = label_encoder.inverse_transform(prediction)[0]
        st.success(f"🧠 Predicted Category: **{label}**")