Create app.py

app.py

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+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()