import streamlit as st
import pandas as pd
import pickle
import matplotlib.pyplot as plt

# --------------------
# LOAD TRAINED MODEL
# --------------------
MODEL_PATH = "prophet_model.pkl"  # updated to match your file

try:
    with open(MODEL_PATH, "rb") as f:
        model = pickle.load(f)
    st.success("✅ Loaded saved Prophet model.")
except FileNotFoundError:
    model = None
    st.warning("⚠ No saved model found. You can still upload CSV for chart display.")

# --------------------
# UPLOAD CSV FILE
# --------------------
st.title("📊 Prophet Forecast Viewer")
uploaded_file = st.file_uploader("Upload your CSV file", type=["csv"])

if uploaded_file is not None:
    df = pd.read_csv(uploaded_file)

    st.subheader("Data Preview")
    st.write(df.head())

    # Prepare data for Prophet if model exists
    if model is not None:
        try:
            # Ensure Prophet format
            df = df.rename(columns={df.columns[0]: 'ds'})
            df['ds'] = pd.to_datetime(df['ds'])

            predictions = model.predict(df)
            st.subheader("Predictions")
            st.write(predictions[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].head())

            # Plot
            fig, ax = plt.subplots(figsize=(10, 5))
            ax.plot(df['ds'], predictions['yhat'], label="Forecast", color="blue")
            ax.fill_between(df['ds'], predictions['yhat_lower'], predictions['yhat_upper'], alpha=0.2)
            ax.set_title("Forecast Chart")
            ax.set_xlabel("Date")
            ax.set_ylabel("Value")
            ax.legend()
            st.pyplot(fig)

        except Exception as e:
            st.error(f"Prediction failed: {e}")

else:
    st.info("📂 Please upload a CSV file to begin.")