import streamlit as st import pickle import pandas as pd import matplotlib.pyplot as plt st.title("Future crime pattern prediction ") # Load the trained ARIMA model with open(r"C:\Users\hp\Desktop\project\model\arima\arima_model.pkl", 'rb') as f: model_fit = pickle.load(f) # Define number of future periods for forecasting future_steps = st.number_input("Enter number of months to forecast:", value=12, min_value=1, step=1) #start_date_input = st.date_input("Enter start date for the forecast:", value=pd.Timestamp.today()) # Convert the user input date to a pandas Timestamp for compatibility start_date = pd.to_datetime("2024-03-2") # Forecast future crime patterns future_forecast = model_fit.forecast(steps=future_steps) # Get the dates for the forecasted period future_dates = pd.date_range(start=start_date, periods=future_steps + 1, freq='ME')[1:] # Plot forecasted values plt.figure(figsize=(10, 6)) plt.plot(future_dates, future_forecast, label="Forecast") #plt.fill_between(future_dates, conf_int[:, 0], conf_int[:, 1], color='gray', alpha=0.3, label='95% Confidence Interval') plt.legend() plt.title("Future Crime Forecast") plt.xlabel("Date") plt.ylabel("Predicted Crime Count") st.pyplot(plt) # Display forecasted values st.write(f"Forecasted values for the next {future_steps} months:") st.write(future_forecast)