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app.py

@@ -1,60 +1,49 @@
 import pandas as pd
-import numpy as np
-import gradio as gr
-from sklearn.ensemble import RandomForestRegressor
-from sklearn.compose import ColumnTransformer
-from sklearn.pipeline import Pipeline
-from sklearn.preprocessing import OneHotEncoder, StandardScaler
 import joblib
+import gradio as gr
 
-# Load dataset
-# NOTE: Saat deploy, pastikan file CSV ini disertakan dalam repo Hugging Face
-DATA_PATH = "supply_chain_data.csv"
-df = pd.read_csv(DATA_PATH)
-
-target = "Number of products sold"
-X = df.drop(columns=["Number of products sold", "Revenue generated", "SKU"])
-y = df[target]
-
-categorical_cols = X.select_dtypes(include=["object"]).columns.tolist()
-numerical_cols = X.select_dtypes(include=["int64", "float64"]).columns.tolist()
-
-preprocessor = ColumnTransformer(transformers=[
-    ("num", StandardScaler(), numerical_cols),
-    ("cat", OneHotEncoder(handle_unknown="ignore"), categorical_cols)
-])
-
-model = Pipeline(steps=[
-    ("preprocessor", preprocessor),
-    ("regressor", RandomForestRegressor(random_state=42))
-])
-
-# Train model
-model.fit(X, y)
-
-# Fungsi prediksi tunggal
-def predict_single(**inputs):
-    input_df = pd.DataFrame([inputs])
-    prediction = model.predict(input_df)[0]
-    return f"Prediksi jumlah produk terjual: {prediction:.0f} unit"
-
-# Buat input gradio otomatis dari fitur
-inputs = []
-for col in X.columns:
-    if col in categorical_cols:
-        values = df[col].unique().tolist()
-        inputs.append(gr.Dropdown(choices=values, label=col))
-    else:
-        inputs.append(gr.Number(label=col))
-
-# Gradio app
-with gr.Blocks() as demo:
-    gr.Markdown("# Prediksi Permintaan Produk (Supply Chain Forecasting)")
-    gr.Markdown("Masukkan detail produk dan faktor supply chain untuk memprediksi jumlah produk yang akan terjual.")
-
-    input_row = gr.Row(inputs)
-    output = gr.Textbox(label="Hasil Prediksi")
-    submit = gr.Button("Prediksi")
-    submit.click(fn=predict_single, inputs=inputs, outputs=output)
+# Load model dan data contoh
+model = joblib.load("demand_forecasting_model.pkl")
+data = pd.read_csv("supply_chain_data.csv")
+
+# Kolom input yang digunakan model
+input_cols = ['product_category', 'price', 'stock', 'location', 'lead_time']
+
+# Fungsi prediksi satuan (input manual)
+def predict_demand(product_category, price, stock, location, lead_time):
+    df = pd.DataFrame([[product_category, price, stock, location, lead_time]], columns=input_cols)
+    prediction = model.predict(df)[0]
+    return round(prediction, 2)
+
+# Fungsi prediksi batch dari CSV
+def predict_from_csv(file):
+    df = pd.read_csv(file.name)
+    if not set(input_cols).issubset(df.columns):
+        return "❌ CSV harus mengandung kolom: " + ", ".join(input_cols)
+    preds = model.predict(df[input_cols])
+    df['predicted_demand'] = preds
+    return df
+
+# UI Gradio
+with gr.Blocks(title="SmartDemand") as demo:
+    gr.Markdown("# 📦 SmartDemand - Supply Chain Demand Forecasting")
+    gr.Markdown("Masukkan informasi produk untuk memprediksi permintaan, atau upload file CSV.")
+
+    with gr.Tab("Prediksi Manual"):
+        with gr.Row():
+            product_category = gr.Dropdown(choices=data['product_category'].unique().tolist(), label="Product Category")
+            price = gr.Number(label="Price")
+            stock = gr.Number(label="Stock")
+        with gr.Row():
+            location = gr.Dropdown(choices=data['location'].unique().tolist(), label="Location")
+            lead_time = gr.Number(label="Lead Time (days)")
+        output_manual = gr.Number(label="Predicted Demand")
+        btn_manual = gr.Button("Predict")
+        btn_manual.click(fn=predict_demand, inputs=[product_category, price, stock, location, lead_time], outputs=output_manual)
+
+    with gr.Tab("Prediksi dari File CSV"):
+        csv_input = gr.File(label="Upload CSV", file_types=[".csv"])
+        output_csv = gr.Dataframe(label="Hasil Prediksi")
+        csv_input.change(fn=predict_from_csv, inputs=csv_input, outputs=output_csv)
 
 demo.launch()

requirements.txt

@@ -1,4 +1,4 @@
-gradio
-scikit-learn
 pandas
-numpy
+scikit-learn
+gradio==4.16.0
+joblib