Create app.py

app.py

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+import os
+import warnings
+warnings.filterwarnings("ignore")
+
+import gradio as gr
+import pandas as pd
+import numpy as np
+import yfinance as yf
+import matplotlib.pyplot as plt
+
+import torch
+from gluonts.dataset.common import ListDataset
+
+# Moirai 2.0 via Uni2TS (per Salesforce's example)
+# https://www.salesforce.com/blog/moirai-2-0/
+from uni2ts.model.moirai2 import Moirai2Forecast, Moirai2Module  # type: ignore
+
+MODEL_ID = "Salesforce/moirai-2.0-R-small"
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Load the Moirai 2.0 module once at startup
+_MODULE = None
+def load_module():
+    global _MODULE
+    if _MODULE is None:
+        _MODULE = Moirai2Module.from_pretrained(MODEL_ID)
+    return _MODULE
+
+def fetch_series(ticker: str, years: int) -> pd.Series:
+    """Fetch daily close price and align to business-day frequency."""
+    data = yf.download(
+        ticker,
+        period=f"{years}y",
+        interval="1d",
+        auto_adjust=True,
+        progress=False,
+        threads=True,
+    )
+    if data is None or data.empty:
+        raise gr.Error(f"No price data found for '{ticker}'.")
+    # Prefer 'Close' after auto_adjust; fall back to 'Adj Close' if needed
+    col = "Close" if "Close" in data.columns else "Adj Close"
+    y = data[col].rename(ticker)
+    y.index = pd.DatetimeIndex(y.index).tz_localize(None)
+
+    # Business-day index, forward-fill market holidays
+    bidx = pd.bdate_range(y.index.min(), y.index.max())
+    y = y.reindex(bidx).ffill()
+    if y.isna().all():
+        raise gr.Error(f"Only missing values for '{ticker}'.")
+    return y
+
+def forecast_ticker(ticker: str,
+                    horizon: int,
+                    lookback_years: int,
+                    context_hint: int):
+    ticker = (ticker or "").strip().upper()
+    if not ticker:
+        raise gr.Error("Please enter a ticker symbol (e.g., AAPL).")
+    if horizon < 1:
+        raise gr.Error("Forecast horizon must be at least 1.")
+
+    # 1) Get history
+    y = fetch_series(ticker, lookback_years)
+    if len(y) < 50:
+        raise gr.Error("Not enough history to forecast (need at least 50 points).")
+
+    # 2) Build dataset for GluonTS-style predictor
+    #    Use business-day freq ('B'); pick a context <= history length.
+    default_ctx = 1680  # from Moirai 2.0 examples
+    ctx = int(np.clip(context_hint or default_ctx, 32, len(y)))
+    target = y.values[-ctx:]
+    start_idx = y.index[-ctx]
+
+    ds = ListDataset([{"start": start_idx, "target": target}], freq="B")
+
+    # 3) Create forecast wrapper and predictor
+    module = load_module()
+    model = Moirai2Forecast(
+        module=module,
+        prediction_length=int(horizon),
+        context_length=ctx,
+        target_dim=1,
+        feat_dynamic_real_dim=0,
+        past_feat_dynamic_real_dim=0,
+    )
+    predictor = model.create_predictor(batch_size=32, device=DEVICE)
+
+    # 4) Predict
+    forecast = next(iter(predictor.predict(ds)))
+
+    # 5) Extract a reasonable central estimate
+    if hasattr(forecast, "mean"):
+        yhat = np.asarray(forecast.mean)
+    elif hasattr(forecast, "quantile"):
+        # 50th percentile as point
+        yhat = np.asarray(forecast.quantile(0.5))
+    elif hasattr(forecast, "samples"):
+        yhat = np.asarray(forecast.samples).mean(axis=0)
+    else:
+        # very defensive fallback
+        yhat = np.asarray(forecast)
+
+    # Guard length (some forecast objects can be slightly longer)
+    yhat = np.asarray(yhat).ravel()[:horizon]
+
+    # 6) Assemble dates & outputs
+    # Next business days after the last historical date
+    future_idx = pd.bdate_range(y.index[-1] + pd.tseries.offsets.BDay(), periods=horizon)
+    pred = pd.Series(yhat, index=future_idx, name="predicted_close")
+
+    # 7) Plot
+    fig = plt.figure(figsize=(10, 5))
+    plt.plot(y.index, y.values, label="history")
+    plt.plot(pred.index, pred.values, label="forecast")
+    plt.title(f"{ticker} close price forecast (Moirai 2.0 R-small)")
+    plt.xlabel("Date"); plt.ylabel("Price"); plt.legend(); plt.tight_layout()
+
+    # 8) Table
+    out_df = pd.DataFrame({"date": pred.index, "predicted_close": pred.values})
+    return fig, out_df
+
+with gr.Blocks(title="Moirai 2.0 — Stock Price Forecast (Research)") as demo:
+    gr.Markdown(
+        """
+# Moirai 2.0 — Stock Price Forecast (Research)
+Enter a ticker to fetch recent daily prices and generate a short-term forecast using **Salesforce/moirai-2.0-R-small**.
+> **Important**: For **research/educational** use only. Not investment advice. Model license is **CC-BY-NC-4.0 (non-commercial)**.
+        """
+    )
+    with gr.Row():
+        ticker = gr.Textbox(label="Ticker", value="AAPL", placeholder="e.g., AAPL, MSFT, TSLA")
+        horizon = gr.Slider(5, 120, value=30, step=1, label="Forecast horizon (business days)")
+    with gr.Row():
+        lookback = gr.Slider(1, 10, value=5, step=1, label="Lookback window (years of history)")
+        ctx = gr.Slider(64, 2000, value=1680, step=16, label="Context length (points)")
+
+    run = gr.Button("Run forecast", variant="primary")
+    plot = gr.Plot(label="History + Forecast")
+    table = gr.Dataframe(label="Forecast table", interactive=False)
+
+    run.click(forecast_ticker, inputs=[ticker, horizon, lookback, ctx], outputs=[plot, table])
+
+if __name__ == "__main__":
+    demo.launch()