Spaces:
Sleeping
Sleeping
File size: 2,274 Bytes
83a1f22 60cde03 83a1f22 60cde03 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | import streamlit as st
import joblib
import pandas as pd
MODEL_PATH = 'src/prophet_birth_model.joblib'
@st.cache_resource
def load_prophet_model():
try:
model = joblib.load(MODEL_PATH)
return model
except Exception as e:
st.error(f"Error loading the Prophet model. Check if '{MODEL_PATH}' is uploaded and if the 'prophet' library is installed. Error: {e}")
return None
# --- Streamlit Interface ---
st.set_page_config(page_title="Births Forecast App", layout="centered")
st.title("👶 Daily Births Forecasting (Prophet Model)")
st.markdown("Enter the number of future days you want to predict.")
model = load_prophet_model()
if model is not None:
st.sidebar.header("Prediction Settings")
n_periods = st.sidebar.slider("Future Days to Forecast:", min_value=1, max_value=365, value=30)
if st.button(f"Generate Forecast for {n_periods} Days"):
with st.spinner(f'Generating forecast...'):
try:
# 1. Generate future dates (using Prophet's built-in method)
future = model.make_future_dataframe(periods=n_periods)
# 2. Predict the values
forecast = model.predict(future)
# Filter to show only the new forecasted data (the last N rows)
forecasted_data = forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail(n_periods)
st.success("Forecast Successful!")
# --- Visualization ---
st.subheader("Predicted Daily Births")
# Prepare data for Streamlit's simple line chart
plot_data = forecasted_data[['ds', 'yhat']].rename(columns={'ds': 'Date', 'yhat': 'Predicted Births'})
plot_data = plot_data.set_index('Date')
st.line_chart(plot_data)
# --- Data Display ---
st.subheader("Raw Forecast Data (Next 5 Days)")
st.dataframe(forecasted_data[['ds', 'yhat', 'yhat_upper']].head(), hide_index=True)
except Exception as e:
st.error(f"Error during forecasting process. Check the model input requirements. Error: {e}") |