import streamlit as st
import pandas as pd
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt

# Page configuration
st.set_page_config(page_title="Crime Rate Predictor", layout="centered")
st.title("🔮 Crime Rate Prediction for Indian States")

# CSV path (Hosted online)
csv_path = "https://huggingface.co/spaces/MLDeveloper/crime_rate_predicition/resolve/main/RS_Session_255_AS_116.1%20(2).csv"

try:
    # Load and preprocess data
    df = pd.read_csv(csv_path)
    data = df[[
        'State/UT',
        'Number of Cases Registered - 2018-19',
        'Number of Cases Registered - 2019-20',
        'Number of Cases Registered - 2020-21',
        'Number of Cases Registered - 2021-22 (up to 31.10.2021)'
    ]].copy()
    data.columns = ['State/UT', '2018', '2019', '2020', '2021']
    for col in ['2018', '2019', '2020', '2021']:
        data[col] = pd.to_numeric(data[col], errors='coerce').fillna(0).astype(int)

    # --- User Inputs ---
    st.subheader("📝 Enter Details to Predict Future Crime Rates")
    
    # Dropdown for State selection
    state_input = st.selectbox("Select State/UT", sorted(data['State/UT'].unique()))
    
    # Slider for year selection
    year_input = st.slider("Select Starting Year", 2022, 2026, 2022)

    if state_input:
        if state_input in data['State/UT'].values:
            selected_row = data[data['State/UT'] == state_input].iloc[0]
            X_train = pd.DataFrame({'Year': [2018, 2019, 2020, 2021]})
            y_train = selected_row[['2018', '2019', '2020', '2021']].values

            # Train model and predict
            model = LinearRegression()
            model.fit(X_train, y_train)

            future_years = list(range(year_input, 2028))
            predictions = model.predict(pd.DataFrame({'Year': future_years}))

            result_df = pd.DataFrame({
                'Year': future_years,
                'Predicted Crime Cases': [max(0, int(pred)) for pred in predictions]
            })

            # # Show predictions
            st.subheader(f"📈 Predicted Crime Rate for {state_input} ({year_input} to 2027)")
            st.dataframe(result_df, use_container_width=True)

            # Plot
            fig, ax = plt.subplots()
            ax.plot(result_df['Year'], result_df['Predicted Crime Cases'], marker='o', linestyle='--', color='orangered')
            ax.set_xlabel("Year")
            ax.set_ylabel("Predicted Crime Cases")
            ax.set_title(f"{state_input} Crime Rate Prediction")
            st.pyplot(fig)
        else:
            st.warning("⚠️ Please enter a valid State/UT name from the dataset.")
    else:
        st.info("👈 Please enter a State/UT name to begin prediction.")

except FileNotFoundError:
    st.error(f"❌ File not found at path: {csv_path}. Please check the path.")