Delete app

app

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-
-import gradio as gr
-import pandas as pd
-import joblib
-
-# Load model
-model = joblib.load("demand_forecasting_model.pkl")
-
-# Ordinal encoder (harus sama urutannya dengan saat training)
-from sklearn.preprocessing import OrdinalEncoder
-encoder = OrdinalEncoder()
-encoder.fit([
-    ['A', 'New York'], ['B', 'Los Angeles'], ['C', 'Chicago'], ['D', 'Houston'], ['E', 'Phoenix']
-])  # dummy untuk encode ulang di input manual
-
-# Fungsi prediksi untuk input manual
-def predict_demand(product_type, price, stock, location, lead_time):
-    data = pd.DataFrame([[product_type, price, stock, location, lead_time]],
-                        columns=["Product type", "Price", "Stock levels", "Location", "Lead time"])
-    data[["Product type", "Location"]] = encoder.transform(data[["Product type", "Location"]])
-    prediction = model.predict(data)
-    return round(prediction[0], 2)
-
-# Fungsi prediksi dari file CSV
-def predict_from_csv(file):
-    df = pd.read_csv(file.name)
-    df[["Product type", "Location"]] = encoder.transform(df[["Product type", "Location"]])
-    preds = model.predict(df[["Product type", "Price", "Stock levels", "Location", "Lead time"]])
-    df["Predicted Demand"] = preds
-    return df
-
-# Gradio antarmuka
-with gr.Blocks() as demo:
-    gr.Markdown("# 📦 Demand Forecasting App")
-    with gr.Tab("Input Manual"):
-        product_type = gr.Dropdown(["A", "B", "C", "D", "E"], label="Product Type")
-        price = gr.Number(label="Price")
-        stock = gr.Number(label="Stock Levels")
-        location = gr.Dropdown(["New York", "Los Angeles", "Chicago", "Houston", "Phoenix"], label="Location")
-        lead_time = gr.Number(label="Lead Time (days)")
-        predict_btn = gr.Button("Predict Demand")
-        output = gr.Textbox(label="Predicted Demand")
-        predict_btn.click(fn=predict_demand, inputs=[product_type, price, stock, location, lead_time], outputs=output)
-
-    with gr.Tab("Upload CSV"):
-        csv_input = gr.File(label="Upload CSV", file_types=[".csv"])
-        csv_output = gr.Dataframe(label="CSV with Predictions")
-        csv_input.change(fn=predict_from_csv, inputs=csv_input, outputs=csv_output)
-
-demo.launch()