Update app.py

app.py

@@ -1,15 +1,68 @@
 import gradio as gr
 import pandas as pd
+import matplotlib.pyplot as plt
+import requests
+import io
+import os
 
-def analisar_csv(file):
-    df = pd.read_csv(file.name)
-    resumo = df.describe(include="all").to_string()
-    return resumo
+# Token vem dos "Repository secrets" no Hugging Face
+HF_TOKEN = os.getenv("HF_TOKEN")
+MODEL = "google/timesfm-2.5-200m-pytorch"
 
-demo = gr.Interface(
-    fn=analisar_csv,
-    inputs=gr.File(file_types=[".csv", ".xlsx"]),
-    outputs="text"
-)
+API_URL = f"https://api-inference.huggingface.co/models/{MODEL}"
+headers = {"Authorization": f"Bearer {HF_TOKEN}"}
+
+def forecast(file, date_col, value_col, steps):
+    # Lê CSV ou Excel
+    if file.name.endswith(".csv"):
+        df = pd.read_csv(file.name)
+    else:
+        df = pd.read_excel(file.name)
+
+    # Converte coluna de datas
+    df[date_col] = pd.to_datetime(df[date_col])
+    df = df.sort_values(by=date_col)
+
+    series = df[value_col].tolist()
+
+    # Payload para a API
+    payload = {
+        "inputs": series,
+        "parameters": {"prediction_length": steps}
+    }
+
+    response = requests.post(API_URL, headers=headers, json=payload)
+
+    if response.status_code != 200:
+        return f"Erro na API: {response.text}", None
+
+    preds = response.json().get("prediction", series[-steps:])
+
+    # Gráfico
+    fig, ax = plt.subplots()
+    ax.plot(df[date_col], df[value_col], label="Histórico")
+    future_dates = pd.date_range(start=df[date_col].iloc[-1], periods=steps+1, freq="D")[1:]
+    ax.plot(future_dates, preds, label="Previsão", linestyle="--")
+    ax.legend()
+    plt.title("📊 Previsão de Vendas (TimesFM)")
+
+    buf = io.BytesIO()
+    plt.savefig(buf, format="png")
+    buf.seek(0)
+
+    return "✅ Previsão concluída!", buf
+
+with gr.Blocks() as demo:
+    gr.Markdown("## 📈 Previsão de Vendas com TimesFM (Hugging Face)")
+
+    file = gr.File(label="Envie seu arquivo (.csv ou .xlsx)", file_types=[".csv", ".xlsx"])
+    date_col = gr.Textbox(label="Nome da coluna de datas")
+    value_col = gr.Textbox(label="Nome da coluna de valores")
+    steps = gr.Slider(1, 90, value=30, label="Quantos dias prever?")
+    output_text = gr.Textbox(label="Resultado")
+    output_plot = gr.Image(type="pil", label="Gráfico")
+
+    btn = gr.Button("Gerar Previsão")
+    btn.click(forecast, inputs=[file, date_col, value_col, steps], outputs=[output_text, output_plot])
 
 demo.launch()