Compat: Chronos-T5 + chronos-forecasting==1.5.3

app.py

@@ -1,35 +1,23 @@
+@'
 import gradio as gr
 import pandas as pd
 import numpy as np
 import torch
 import matplotlib.pyplot as plt
-
-# 🔽 Import del pipeline de Chronos (lib 'chronos-forecasting')
 from chronos import ChronosPipeline
 
-# ANTES:
-# MODEL_ID = "amazon/chronos-bolt-base"
-
-# AHORA:
+# Modelo compatible con chronos-forecasting==1.5.3
 MODEL_ID = "amazon/chronos-t5-base"
 
-# Cargar el modelo UNA sola vez
 PIPELINE = ChronosPipeline.from_pretrained(
     MODEL_ID,
     device_map="auto",
-    dtype=torch.float32,   # ← antes usábamos torch_dtype (deprecado)
+    dtype=torch.float32,   # usar "dtype" (no "torch_dtype")
 )
 
 def _prepare_series(df: pd.DataFrame, freq: str | None):
-    """
-    Espera columnas: date,value
-    - Ordena por fecha
-    - Infere o aplica frecuencia
-    - Interpola huecos
-    """
     if "date" not in df.columns or "value" not in df.columns:
         raise gr.Error("El CSV debe tener columnas: date,value")
-
     df = df.copy()
     df["date"] = pd.to_datetime(df["date"])
     df = df.sort_values("date")
@@ -39,13 +27,11 @@ def _prepare_series(df: pd.DataFrame, freq: str | None):
     else:
         inferred = pd.infer_freq(df["date"])
         if inferred is None:
-            # fallback: tamaño de paso por mediana en días
             step = max(int((df["date"].diff().median() / pd.Timedelta(days=1)) or 1), 1)
             df = df.set_index("date").asfreq(f"{step}D").reset_index()
         else:
             df = df.set_index("date").asfreq(inferred).reset_index()
 
-    # Rellenar faltantes
     df["value"] = pd.to_numeric(df["value"], errors="coerce")
     df["value"] = df["value"].interpolate("linear").bfill().ffill()
     return df
@@ -56,15 +42,12 @@ def forecast_fn(file, horizon: int = 12, freq: str = "MS"):
     df = pd.read_csv(file.name)
     df = _prepare_series(df, freq.strip() or None)
 
-    # Serie a tensor
     y = torch.tensor(df["value"].values, dtype=torch.float32)
 
-    # Predicción probabilística (múltiples trayectorias -> cuantiles)
     samples = PIPELINE.predict(y, prediction_length=horizon, num_samples=200)  # [1, N, H]
-    samples = samples[0].numpy()  # [N, H]
+    samples = samples[0].numpy()
     p10, p50, p90 = np.quantile(samples, [0.10, 0.50, 0.90], axis=0)
 
-    # Fechas futuras
     inferred = pd.infer_freq(df["date"])
     if inferred is None:
         step = max(int((df["date"].diff().median() / pd.Timedelta(days=1)) or 1), 1)
@@ -79,19 +62,18 @@ def forecast_fn(file, horizon: int = 12, freq: str = "MS"):
         "p90": np.round(p90, 4),
     })
 
-    # Gráfica
     fig = plt.figure(figsize=(8, 4))
     plt.plot(df["date"], df["value"], label="Histórico")
     plt.plot(out["date"], out["p50"], label="Pronóstico (P50)")
     plt.fill_between(out["date"], out["p10"], out["p90"], alpha=0.3, label="Banda P10–P90")
-    plt.title("Pronóstico con Chronos-Bolt (P10 / P50 / P90)")
+    plt.title("Pronóstico con Chronos-T5 (P10 / P50 / P90)")
     plt.xlabel("Fecha"); plt.ylabel("Valor")
     plt.legend()
 
     return out, fig
 
-with gr.Blocks(title="Pronóstico de Demanda (Chronos-Bolt)") as demo:
-    gr.Markdown("## Análisis predictivo de mercado (Hugging Face + Chronos-Bolt)\nSube un CSV con columnas **date,value**. Elige horizonte y frecuencia.")
+with gr.Blocks(title="Pronóstico de Demanda (Chronos-T5)") as demo:
+    gr.Markdown("## Análisis predictivo de mercado (Hugging Face + Chronos-T5)\nSube un CSV con **date,value**. Elige horizonte y frecuencia.")
     with gr.Row():
         file = gr.File(label="CSV: date,value", file_types=[".csv"])
         horizon = gr.Slider(1, 36, value=12, step=1, label="Horizonte (pasos)")
@@ -100,8 +82,8 @@ with gr.Blocks(title="Pronóstico de Demanda (Chronos-Bolt)") as demo:
     btn = gr.Button("Generar pronóstico")
     out_table = gr.Dataframe(label="Tabla de pronóstico")
     out_plot = gr.Plot(label="Gráfica")
-    # api_name → te da un endpoint gratis para consumir como API
     btn.click(forecast_fn, inputs=[file, horizon, freq], outputs=[out_table, out_plot], api_name="/forecast")
 
 if __name__ == "__main__":
     demo.queue().launch()
+'@ | Set-Content app.py -Encoding UTF8

requirements.txt

@@ -1,6 +1,9 @@
+@'
 gradio>=4.44.0
 pandas>=2.0.0
 numpy>=1.24.0
 matplotlib>=3.7.0
 torch>=2.2.0
-chronos-forecasting>=1.7.0
+chronos-forecasting==1.5.3
+'@ | Set-Content requirements.txt -Encoding UTF8
+