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feat: streamlit frontend, rudimentary
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,158 @@ | ||
| import streamlit as st | ||
| import numpy as np | ||
| import pandas as pd | ||
| import plotly.graph_objects as go | ||
| import json | ||
| import tensorflow as tf | ||
| import torch | ||
| from sklearn.preprocessing import MinMaxScaler | ||
| from datetime import datetime | ||
|
|
||
| # Helper functions | ||
| def load_scaler(path): | ||
| with open(path, 'r') as f: | ||
| scaler_data = json.load(f) | ||
| scaler = MinMaxScaler() | ||
| scaler.min_ = np.array(scaler_data['min_']) | ||
| scaler.scale_ = np.array(scaler_data['scale_']) | ||
| return scaler | ||
|
|
||
| def load_lstm_model(): | ||
| return tf.keras.load_model('models/best_lstm_model.keras') | ||
|
|
||
| def load_transformer_model(params_path, model_path): | ||
| with open(params_path, 'r') as f: | ||
| params = json.load(f) | ||
| model = TimeSeriesTransformer( | ||
| input_size=params['d_model'], | ||
| num_layers=params['num_layers'], | ||
| num_heads=params['num_heads'], | ||
| d_model=params['d_model'], | ||
| dim_feedforward=params['dim_feedforward'] | ||
| ) | ||
| model.load_state_dict(torch.load(model_path)) | ||
| return model, params | ||
|
|
||
| # TimeSeriesTransformer class definition (same as in your training code) | ||
| class TimeSeriesTransformer(torch.nn.Module): | ||
| def __init__(self, input_size, num_layers, num_heads, d_model, dim_feedforward): | ||
| super(TimeSeriesTransformer, self).__init__() | ||
| self.encoder_layer = torch.nn.TransformerEncoderLayer( | ||
| d_model=d_model, nhead=num_heads, dim_feedforward=dim_feedforward, batch_first=True | ||
| ) | ||
| self.transformer_encoder = torch.nn.TransformerEncoder(self.encoder_layer, num_layers=num_layers) | ||
| self.fc = torch.nn.Linear(d_model, 1) | ||
|
|
||
| def forward(self, x): | ||
| x = self.transformer_encoder(x) | ||
| x = self.fc(x[:, -1, :]) # Output of the last time step | ||
| return x | ||
|
|
||
| # UI Setup | ||
| st.set_page_config( | ||
| page_title="Café ML Demo", | ||
| layout="centered", | ||
| initial_sidebar_state="expanded", | ||
| ) | ||
|
|
||
| # Style Settings | ||
| st.markdown(""" | ||
| <style> | ||
| body { | ||
| background-color: #f4f1ea; /* Cream color */ | ||
| color: #4e342e; /* Espresso brown */ | ||
| } | ||
| .stButton>button { | ||
| background-color: #4e342e; | ||
| color: white; | ||
| } | ||
| .stButton>button:hover { | ||
| background-color: #6d4c41; | ||
| color: white; | ||
| } | ||
| </style> | ||
| """, unsafe_allow_html=True) | ||
|
|
||
| st.title("☕ Café ML Demo") | ||
| st.sidebar.title("⚙️ Settings") | ||
|
|
||
| # Sidebar options | ||
| model_type = st.sidebar.selectbox("Select Model Type", ["LSTM", "Transformer"]) | ||
| seq_length = st.sidebar.number_input("Sequence Length", min_value=5, max_value=50, value=10, step=1) | ||
| uploaded_file = st.sidebar.file_uploader("Upload Test Data (CSV)", type=["csv"]) | ||
| dark_mode = st.sidebar.checkbox("Enable Dark Mode") | ||
|
|
||
| # Dark Mode Styling | ||
| if dark_mode: | ||
| st.markdown(""" | ||
| <style> | ||
| body { | ||
| background-color: #2c2c2c; | ||
| color: #f4f1ea; | ||
| } | ||
| .stButton>button { | ||
| background-color: #6d4c41; | ||
| color: white; | ||
| } | ||
| </style> | ||
| """, unsafe_allow_html=True) | ||
|
|
||
| # Load Data | ||
| if uploaded_file: | ||
| data = pd.read_csv(uploaded_file) | ||
| st.sidebar.write(f"Data preview:") | ||
| st.sidebar.write(data.head()) | ||
| else: | ||
| st.sidebar.warning("Upload a CSV file to proceed.") | ||
|
|
||
| # Load Models | ||
| scaler_path = 'models/scaler.json' | ||
| scaler = load_scaler(scaler_path) | ||
| lstm_model = None | ||
| transformer_model = None | ||
| if model_type == "LSTM": | ||
| lstm_model = load_lstm_model() | ||
| else: | ||
| transformer_model, transformer_params = load_transformer_model( | ||
| 'params/best_ts_transformer_params.json', | ||
| 'models/best_ts_transformer_model.pt' | ||
| ) | ||
|
|
||
| # Inference | ||
| if st.button("Run Inference"): | ||
| if not uploaded_file: | ||
| st.error("Please upload a test data file first.") | ||
| else: | ||
| # Scale and process data | ||
| scaled_data = scaler.transform(data.values) | ||
| sequences = [ | ||
| scaled_data[i : i + seq_length] | ||
| for i in range(len(scaled_data) - seq_length) | ||
| ] | ||
| sequences = np.array(sequences) | ||
|
|
||
| # Predict | ||
| if model_type == "LSTM": | ||
| predictions = lstm_model.predict(sequences) | ||
| else: | ||
| sequences_torch = torch.FloatTensor(sequences).unsqueeze(-1) # Add feature dim | ||
| predictions = transformer_model(sequences_torch).detach().numpy() | ||
|
|
||
| # Rescale predictions | ||
| predictions_rescaled = scaler.inverse_transform(predictions) | ||
|
|
||
| # Visualization | ||
| st.success("Inference complete! Here are the results:") | ||
| fig = go.Figure() | ||
| fig.add_trace(go.Scatter(y=data.values.flatten(), name="Actual", mode="lines")) | ||
| fig.add_trace(go.Scatter(y=predictions_rescaled.flatten(), name="Predicted", mode="lines")) | ||
| fig.update_layout( | ||
| title="Actual vs Predicted", | ||
| xaxis_title="Time Steps", | ||
| yaxis_title="Values", | ||
| template="plotly_dark" if dark_mode else "plotly_white", | ||
| ) | ||
| st.plotly_chart(fig) | ||
|
|
||
| # Footer | ||
| st.markdown("#### Made with ❤️ for CaféCast") |
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