| import gradio as gr |
| import torch |
| import numpy as np |
| import pandas as pd |
| import plotly.graph_objects as go |
| from transformers import TimesFm2_5ModelForPrediction |
| from datetime import datetime, timedelta |
| import tempfile |
| import os |
|
|
| MODEL_ID = "mahwizzzz/skyloom" |
| CONTEXT_LEN = 1024 |
| MAX_HORIZON = 72 |
|
|
| print("Loading model from Hub...") |
| device = "cuda" if torch.cuda.is_available() else "cpu" |
| model = TimesFm2_5ModelForPrediction.from_pretrained( |
| MODEL_ID, |
| torch_dtype=torch.bfloat16 if device == "cuda" else torch.float32, |
| device_map=device if device == "cuda" else None |
| ) |
| model.eval() |
| print("Model ready") |
|
|
| def forecast_from_array(past_values: np.ndarray, horizon: int = 24) -> np.ndarray: |
| if len(past_values) < CONTEXT_LEN: |
| pad_len = CONTEXT_LEN - len(past_values) |
| past_values = np.pad(past_values, (pad_len, 0), mode='edge') |
| else: |
| past_values = past_values[-CONTEXT_LEN:] |
|
|
| input_tensor = torch.tensor(past_values[None, :], dtype=torch.float32).to(device) |
| with torch.no_grad(): |
| output = model(past_values=input_tensor, forecast_context_len=CONTEXT_LEN) |
| forecast = output.mean_predictions[0, :horizon].float().cpu().numpy() |
| return np.clip(forecast, 0, None) |
|
|
| def load_csv(file): |
| df = pd.read_csv(file.name) |
| if 'aqi' in df.columns: |
| values = df['aqi'].values |
| else: |
| values = df.iloc[:, 0].values |
| return values.astype(float) |
|
|
| def forecast_from_csv(file, horizon): |
| values = load_csv(file) |
| if len(values) < 2: |
| raise gr.Error("CSV must contain at least 2 AQI values.") |
| forecast = forecast_from_array(values, horizon) |
| return create_plot(values[-CONTEXT_LEN:], forecast, horizon) |
|
|
| def forecast_from_text(text, horizon): |
| try: |
| values = np.array([float(x.strip()) for x in text.split(',') if x.strip()]) |
| except: |
| raise gr.Error("Please enter a comma-separated list of numbers (e.g., 45.2, 47.1, 46.8)") |
| if len(values) < 2: |
| raise gr.Error("At least 2 values required.") |
| forecast = forecast_from_array(values, horizon) |
| return create_plot(values[-CONTEXT_LEN:], forecast, horizon) |
|
|
| def create_plot(history, forecast, horizon): |
| hist_indices = list(range(-len(history), 0)) |
| fore_indices = list(range(1, horizon+1)) |
|
|
| fig = go.Figure() |
| fig.add_trace(go.Scatter( |
| x=hist_indices, y=history, |
| mode='lines+markers', |
| name='Past AQI', |
| line=dict(color='#1f77b4', width=2), |
| marker=dict(size=4) |
| )) |
| fig.add_trace(go.Scatter( |
| x=fore_indices, y=forecast, |
| mode='lines+markers', |
| name='Forecast', |
| line=dict(color='#ff7f0e', width=3, dash='dot'), |
| marker=dict(size=6, symbol='triangle-up') |
| )) |
|
|
| fig.add_vline(x=0, line_dash="dash", line_color="gray", opacity=0.5) |
| fig.add_annotation(x=0, y=max(history[-10:]+forecast[:5]), text="Forecast start", showarrow=False, yshift=10) |
|
|
| fig.update_layout( |
| title=f"AQI Forecast – Next {horizon} hours", |
| xaxis_title="Hours relative to present (0 = current hour)", |
| yaxis_title="Air Quality Index (AQI)", |
| template="plotly_white", |
| hovermode="x unified", |
| legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1) |
| ) |
| return fig |
|
|
| with gr.Blocks(theme=gr.themes.Soft(), title="Skyloom – Karachi AQI Forecaster", css="footer {visibility: hidden}") as demo: |
| gr.Markdown(""" |
| # Skyloom: AQI Forecaster for Karachi |
| **TimesFM‑2.5** – Fine‑tuned for hourly Air Quality Index predictions. |
| """) |
|
|
| with gr.Row(): |
| with gr.Column(scale=1): |
| gr.Markdown("### Input Data") |
| input_method = gr.Radio(["Paste values", "Upload CSV"], label="Input method", value="Paste values") |
|
|
| with gr.Group(visible=True) as text_group: |
| text_input = gr.Textbox( |
| label="Past AQI values (comma‑separated, at least 24 values, oldest first)", |
| placeholder="45.2, 47.1, 46.8, 48.3, ...", |
| lines=3 |
| ) |
|
|
| with gr.Group(visible=False) as csv_group: |
| csv_input = gr.File(label="Upload CSV", file_types=[".csv"]) |
| gr.Markdown("CSV should have a column named `aqi` or the first column containing hourly AQI values.") |
|
|
| horizon_slider = gr.Slider(label="Forecast horizon (hours)", minimum=1, maximum=72, value=24, step=1) |
|
|
| forecast_btn = gr.Button("Generate Forecast", variant="primary") |
|
|
| with gr.Column(scale=2): |
| plot_output = gr.Plot(label="Forecast Chart") |
| gr.Markdown(""" |
| **Interpretation** |
| - **Past AQI** (blue): last 1024 hours of history (or the portion you provided) |
| - **Forecast** (orange dashed): predicted AQI for the next hours |
| - AQI categories: |
| - 0–50 Good |
| - 51–100 Moderate |
| - 101–150 Unhealthy for Sensitive Groups |
| - 151–200 Unhealthy |
| - 201+ Very Unhealthy / Hazardous |
| """) |
|
|
| def toggle_visibility(method): |
| return gr.update(visible=(method == "Paste values")), gr.update(visible=(method == "Upload CSV")) |
| input_method.change(toggle_visibility, inputs=input_method, outputs=[text_group, csv_group]) |
|
|
| def on_text_forecast(text, horizon): |
| if not text.strip(): |
| raise gr.Error("Please enter comma-separated AQI values.") |
| return forecast_from_text(text, horizon) |
|
|
| def on_csv_forecast(file, horizon): |
| if file is None: |
| raise gr.Error("Please upload a CSV file.") |
| return forecast_from_csv(file, horizon) |
|
|
| forecast_btn.click( |
| fn=lambda method, text, csv, horizon: |
| forecast_from_text(text, horizon) if method == "Paste values" else forecast_from_csv(csv, horizon), |
| inputs=[input_method, text_input, csv_input, horizon_slider], |
| outputs=plot_output |
| ) |
|
|
| gr.Markdown(""" |
| --- |
| **Model info** |
| - Base model: `google/timesfm-2.5-200m-transformers` |
| - Test MAE: **5.18** | sMAPE: **5.46%** | MASE: **0.47** |
| - *Disclaimer*: Forecasts are for informational purposes only. Sudden pollution events may not be captured. |
| """) |
|
|
| if __name__ == "__main__": |
| demo.launch() |
