datacellar-service-demo/interface/pages/2_Long_Term_Consumption.py

import streamlit as st
import json
from utils import load_and_process_data, create_time_series_plot, display_statistics, call_api

if 'api_token' not in st.session_state:
    st.session_state.api_token = "p2s8X9qL4zF7vN3mK6tR1bY5cA0wE3hJ"

# Clear other states
for key in ['current_file', 'json_data', 'api_response']:
    if key in st.session_state:
        del st.session_state[key]

# Initialize session state variables
if 'current_file' not in st.session_state:
    st.session_state.current_file = None
if 'json_data' not in st.session_state:
    st.session_state.json_data = None
if 'api_response' not in st.session_state:
    st.session_state.api_response = None

st.title("Long Term Energy Consumption Forecasting")

st.markdown("""
This service provides long-term forecasting of energy consumption patterns. 
Upload your historical consumption data to generate predictions for extended periods.

### Features
- Hourly consumption forecasting
- Interactive visualizations
- Statistical analysis of predictions
""")

# File upload and processing
uploaded_file = st.file_uploader("Upload JSON file", type=['json'])

if uploaded_file:
    try:
        file_contents = uploaded_file.read()
        st.session_state.current_file = file_contents
        st.session_state.json_data = json.loads(file_contents)
        
        dfs = load_and_process_data(st.session_state.json_data)
        if dfs:
            st.header("Input Data")
            tabs = st.tabs(["Visualization", "Raw JSON", "Statistics"])
            
            with tabs[0]:
                for unit, df in dfs.items():
                    st.plotly_chart(create_time_series_plot(df, unit), use_container_width=True)
            
            with tabs[1]:
                st.json(st.session_state.json_data)
                
            with tabs[2]:
                display_statistics(dfs)
            
            if st.button("Generate Long Term Forecast"):
                if not st.session_state.api_token:
                    st.error("Please enter your API token in the sidebar first.")
                else:
                    with st.spinner("Generating long-term forecast..."):
                        st.session_state.api_response = call_api(
                            st.session_state.current_file,
                            st.session_state.api_token,
                            "inference_consumption_long_term"
                        )

    except Exception as e:
        st.error(f"Error processing file: {str(e)}")

# Display API results
if st.session_state.api_response:
    st.header("Forecast Results")
    tabs = st.tabs(["Visualization", "Raw JSON", "Statistics"])
    
    with tabs[0]:
        response_dfs = load_and_process_data(
            st.session_state.api_response,
            input_data=st.session_state.json_data
        )
        if response_dfs:
            del response_dfs['Celsius']
            for unit, df in response_dfs.items():
                st.plotly_chart(create_time_series_plot(df, unit), use_container_width=True)
    
    with tabs[1]:
        st.json(st.session_state.api_response)
    
    with tabs[2]:
        if response_dfs:
            display_statistics(response_dfs)