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| import streamlit as st | |
| import json | |
| from utils import load_and_process_data, create_time_series_plot, display_statistics, call_api | |
| from dotenv import load_dotenv | |
| 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("Short Term Energy Consumption Forecasting") | |
| st.markdown(""" | |
| This service provides short-term forecasting of energy consumption patterns. | |
| Upload your energy consumption data to generate predictions for the near future. | |
| ### 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 Short 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 forecast..."): | |
| st.session_state.api_response = call_api( | |
| st.session_state.current_file, | |
| st.session_state.api_token, | |
| "inference_consumption_short_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) |