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diff --git a/M4_pretrained_models/app.py b/M4_pretrained_models/app.py
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-
-import streamlit as st
-import pandas as pd
-import matplotlib.pyplot as plt
-import pytorch_lightning as pl
-from neuralforecast.core import NeuralForecast
-from neuralforecast.models import NHITS, TimesNet, LSTM, TFT
-from neuralforecast.losses.pytorch import HuberMQLoss
-import time
-
-# Paths for saving models
-nhits_paths = {
-    'D': './results/M4/NHITS/daily',
-    'M': './results/M4/NHITS/monthly',
-    'H': './results/M4/NHITS/hourly',
-    'W': './results/M4/NHITS/weekly',
-    'Y': './results/M4/NHITS/yearly'
-}
-
-timesnet_paths = {
-    'D': './results/M4/TimesNet/daily',
-    'M': './results/M4/TimesNet/monthly',
-    'H': './results/M4/TimesNet/hourly',
-    'W': './results/M4/TimesNet/weekly',
-    'Y': './results/M4/TimesNet/yearly'
-}
-
-lstm_paths = {
-    'D': './results/M4/LSTM/daily',
-    'M': './results/M4/LSTM/monthly',
-    'H': './results/M4/LSTM/hourly',
-    'W': './results/M4/LSTM/weekly',
-    'Y': './results/M4/LSTM/yearly'
-}
-
-tft_paths = {
-    'D': './results/M4/TFT/daily',
-    'M': './results/M4/TFT/monthly',
-    'H': './results/M4/TFT/hourly',
-    'W': './results/M4/TFT/weekly',
-    'Y': './results/M4/TFT/yearly'
-}
-
-@st.cache_resource
-def load_model(path, freq):
-    nf = NeuralForecast.load(path=path)
-    return nf
-
-nhits_models = {freq: load_model(path, freq) for freq, path in nhits_paths.items()}
-timesnet_models = {freq: load_model(path, freq) for freq, path in timesnet_paths.items()}
-lstm_models = {freq: load_model(path, freq) for freq, path in lstm_paths.items()}
-tft_models = {freq: load_model(path, freq) for freq, path in tft_paths.items()}
-
-def generate_forecast(model, df):
-    forecast_df = model.predict(df=df)
-    return forecast_df
-
-def determine_frequency(df):
-    df['ds'] = pd.to_datetime(df['ds'])
-    df = df.set_index('ds')
-    freq = pd.infer_freq(df.index)
-    return freq
-
-def plot_forecasts(forecast_df, train_df, title):
-    fig, ax = plt.subplots(1, 1, figsize=(20, 7))
-    plot_df = pd.concat([train_df, forecast_df]).set_index('ds')
-    historical_col = 'y'
-    forecast_col = next((col for col in plot_df.columns if 'median' in col), None)
-    lo_col = next((col for col in plot_df.columns if 'lo-90' in col), None)
-    hi_col = next((col for col in plot_df.columns if 'hi-90' in col), None)
-    if forecast_col is None:
-        raise KeyError("No forecast column found in the data.")
-    plot_df[[historical_col, forecast_col]].plot(ax=ax, linewidth=2, label=['Historical', 'Forecast'])
-    if lo_col and hi_col:
-        ax.fill_between(
-            plot_df.index,
-            plot_df[lo_col],
-            plot_df[hi_col],
-            color='blue',
-            alpha=0.3,
-            label='90% Confidence Interval'
-        )
-    ax.set_title(title, fontsize=22)
-    ax.set_ylabel('Value', fontsize=20)
-    ax.set_xlabel('Timestamp [t]', fontsize=20)
-    ax.legend(prop={'size': 15})
-    ax.grid()
-    st.pyplot(fig)
-
-def select_model_based_on_frequency(freq, nhits_models, timesnet_models, lstm_models, tft_models):
-    if freq == 'D':
-        return nhits_models['D'], timesnet_models['D'], lstm_models['D'], tft_models['D']
-    elif freq == 'M':
-        return nhits_models['M'], timesnet_models['M'], lstm_models['M'], tft_models['M']
-    elif freq == 'H':
-        return nhits_models['H'], timesnet_models['H'], lstm_models['H'], tft_models['H']
-    elif freq in ['W', 'W-SUN']:
-        return nhits_models['W'], timesnet_models['W'], lstm_models['W'], tft_models['W']
-    elif freq in ['Y', 'Y-DEC']:
-        return nhits_models['Y'], timesnet_models['Y'], lstm_models['Y'], tft_models['Y']
-    else:
-        raise ValueError(f"Unsupported frequency: {freq}")
-
-def select_model(horizon, model_type, max_steps=200):
-    if model_type == 'NHITS':
-        return NHITS(input_size=5 * horizon,
-                     h=horizon,
-                     max_steps=max_steps,
-                     stack_types=3*['identity'],
-                     n_blocks=3*[1],
-                     mlp_units=[[256, 256] for _ in range(3)],
-                     n_pool_kernel_size=3*[1],
-                     batch_size=32,
-                     scaler_type='standard',
-                     n_freq_downsample=[12, 4, 1],
-                     loss=HuberMQLoss(level=[90]))
-    elif model_type == 'TimesNet':
-        return TimesNet(h=horizon,
-                        input_size=horizon * 5,
-                        hidden_size=16,
-                        conv_hidden_size=32,
-                        loss=HuberMQLoss(level=[90]),
-                        scaler_type='standard',
-                        learning_rate=1e-3,
-                        max_steps=max_steps,
-                        val_check_steps=200,
-                        valid_batch_size=64,
-                        windows_batch_size=128,
-                        inference_windows_batch_size=512)
-    elif model_type == 'LSTM':
-        return LSTM(h=horizon,
-                    input_size=horizon * 5,
-                    loss=HuberMQLoss(level=[90]),
-                    scaler_type='standard',
-                    encoder_n_layers=2,
-                    encoder_hidden_size=64,
-                    context_size=10,
-                    decoder_hidden_size=64,
-                    decoder_layers=2,
-                    max_steps=max_steps)
-    elif model_type == 'TFT':
-        return TFT(h=horizon,
-                   input_size=horizon,
-                   hidden_size=16,
-                   loss=HuberMQLoss(level=[90]),
-                   learning_rate=0.005,
-                   scaler_type='standard',
-                   windows_batch_size=128,
-                   max_steps=max_steps,
-                   val_check_steps=200,
-                   valid_batch_size=64,
-                   enable_progress_bar=True)
-    else:
-        raise ValueError(f"Unsupported model type: {model_type}")
-
-def forecast_time_series(df, model_type, freq, horizon, max_steps=200):
-    start_time = time.time()  # Start timing
-    if freq:
-        df['ds'] = pd.date_range(start='1970-01-01', periods=len(df), freq=freq)
-    else:
-        freq = determine_frequency(df)
-        st.write(f"Determined frequency: {freq}")
-    df['ds'] = pd.to_datetime(df['ds'], errors='coerce')
-    df = df.dropna(subset=['ds'])
-    model = select_model(horizon, model_type, max_steps)
-    forecast_results = {}
-    st.write(f"Generating forecast using {model_type} model...")
-    forecast_results[model_type] = generate_forecast(model, df, freq)
-
-    for model_name, forecast_df in forecast_results.items():
-        plot_forecasts(forecast_df, df, f'{model_name} Forecast Comparison')
-        
-    end_time = time.time()  # End timing
-    time_taken = end_time - start_time
-    st.success(f"Time taken for {model_type} forecast: {time_taken:.2f} seconds")
-
-# Streamlit App
-st.title("Dynamic and Automatic Time Series Forecasting")
-
-# Upload dataset
-uploaded_file = st.file_uploader("Upload your time series data (CSV)", type=["csv"])
-if uploaded_file:
-    df = pd.read_csv(uploaded_file)
-else:
-    st.warning("Using default data")
-    df = AirPassengersDF.copy()
-
-# Model selection and forecasting
-st.subheader("Transfer Learning Forecasting")
-model_choice = st.selectbox("Select model", ["NHITS", "TimesNet", "LSTM", "TFT"])
-horizon = st.slider("Forecast horizon", 1, 100, 10)
-
-# Determine frequency of data
-frequency = determine_frequency(df)
-st.write(f"Detected frequency: {frequency}")
-
-# Load pre-trained models
-nhits_model, timesnet_model, lstm_model, tft_model = select_model_based_on_frequency(frequency, nhits_models, timesnet_models, lstm_models, tft_models)
-forecast_results = {}
-
-start_time = time.time()  # Start timing
-if model_choice == "NHITS":
-    forecast_results['NHITS'] = generate_forecast(nhits_model, df)
-elif model_choice == "TimesNet":
-    forecast_results['TimesNet'] = generate_forecast(timesnet_model, df)
-elif model_choice == "LSTM":
-    forecast_results['LSTM'] = generate_forecast(lstm_model, df)
-elif model_choice == "TFT":
-    forecast_results['TFT'] = generate_forecast(tft_model, df)
-
-for model_name, forecast_df in forecast_results.items():
-    plot_forecasts(forecast_df, df, f'{model_name} Forecast')
-    
-end_time = time.time()  # End timing
-time_taken = end_time - start_time
-st.success(f"Time taken for {model_choice} forecast: {time_taken:.2f} seconds")
-
-# Dynamic forecasting
-st.subheader("Dynamic Forecasting")
-dynamic_model_choice = st.selectbox("Select model for dynamic forecasting", ["NHITS", "TimesNet", "LSTM", "TFT"], key="dynamic_model_choice")
-dynamic_horizon = st.slider("Forecast horizon for dynamic forecasting", 1, 100, 10, key="dynamic_horizon")
-forecast_time_series(df, dynamic_model_choice, frequency, dynamic_horizon)