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import gradio as gr
import pandas as pd
import matplotlib.pyplot as plt
import requests
import io
import os
# Token vem dos "Repository secrets" no Hugging Face
HF_TOKEN = os.getenv("HF_TOKEN")
MODEL = "google/timesfm-2.5-200m-pytorch"
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()
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