Create app.py

app.py

@@ -0,0 +1,181 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from amazon_chronos import ChronosModel, TimeSeriesDataSet
+import tempfile
+import os
+
+class TimeSeriesForecaster:
+    def __init__(self):
+        self.model = None
+        self.dataset = None
+        self.original_series = None
+
+    def preprocess_data(self, df, date_column, value_column, context_length=30, prediction_length=7):
+        """
+        Persiapkan data time series dari DataFrame
+        """
+        # Pastikan data terurut berdasarkan tanggal
+        df = df.sort_values(by=date_column)
+        
+        # Konversi kolom tanggal ke datetime
+        df[date_column] = pd.to_datetime(df[date_column])
+        
+        # Set index ke tanggal
+        df.set_index(date_column, inplace=True)
+        
+        # Ekstrak series numerik
+        time_series = df[value_column].values
+        
+        # Buat dataset Chronos
+        self.original_series = time_series
+        self.dataset = TimeSeriesDataSet.from_series(
+            time_series, 
+            context_length=context_length, 
+            prediction_length=prediction_length
+        )
+        
+        return self.dataset
+
+    def train_model(self, model_id='chronos-t5-small'):
+        """
+        Latih model Chronos
+        """
+        self.model = ChronosModel.from_pretrained(model_id)
+        self.model.fit(self.dataset)
+        return self.model
+
+    def forecast(self, n_samples=100):
+        """
+        Lakukan prediksi
+        """
+        if not self.model or not self.dataset:
+            raise ValueError("Model belum dilatih. Latih model terlebih dahulu.")
+        
+        forecasts = self.model.predict(self.dataset, num_samples=n_samples)
+        return forecasts
+
+    def visualize_forecast(self, forecasts):
+        """
+        Buat visualisasi prediksi
+        """
+        plt.figure(figsize=(12, 6))
+        
+        # Plot series asli
+        plt.plot(self.original_series, label='Data Historis', color='blue')
+        
+        # Plot prediksi
+        forecast_mean = forecasts.mean(axis=0)
+        forecast_lower = np.percentile(forecasts, 10, axis=0)
+        forecast_upper = np.percentile(forecasts, 90, axis=0)
+        
+        forecast_start = len(self.original_series)
+        plt.plot(
+            range(forecast_start, forecast_start + len(forecast_mean)), 
+            forecast_mean, 
+            label='Prediksi Rata-rata', 
+            color='red'
+        )
+        plt.fill_between(
+            range(forecast_start, forecast_start + len(forecast_mean)), 
+            forecast_lower, 
+            forecast_upper, 
+            alpha=0.3, 
+            color='red'
+        )
+        
+        plt.title('Peramalan Time Series dengan Amazon Chronos')
+        plt.xlabel('Indeks Waktu')
+        plt.ylabel('Nilai')
+        plt.legend()
+        
+        return plt
+
+def main():
+    st.title('🕰️ Time Series Forecasting dengan Amazon Chronos')
+    
+    # Sidebar untuk upload dan konfigurasi
+    st.sidebar.header('Pengaturan Prediksi')
+    
+    # Upload file CSV
+    uploaded_file = st.sidebar.file_uploader(
+        "Unggah File CSV", 
+        type=['csv'], 
+        help="Pastikan file CSV memiliki kolom tanggal dan nilai numerik"
+    )
+    
+    # Pilihan kolom
+    if uploaded_file is not None:
+        # Baca CSV
+        df = pd.read_csv(uploaded_file)
+        
+        # Pilih kolom
+        date_column = st.sidebar.selectbox(
+            'Pilih Kolom Tanggal', 
+            options=df.columns
+        )
+        value_column = st.sidebar.selectbox(
+            'Pilih Kolom Nilai', 
+            options=[col for col in df.columns if col != date_column]
+        )
+        
+        # Parameter prediksi
+        context_length = st.sidebar.slider(
+            'Panjang Konteks', 
+            min_value=10, 
+            max_value=100, 
+            value=30
+        )
+        prediction_length = st.sidebar.slider(
+            'Panjang Prediksi', 
+            min_value=1, 
+            max_value=30, 
+            value=7
+        )
+        
+        # Tombol proses
+        if st.sidebar.button('Lakukan Prediksi'):
+            try:
+                # Inisiasi forecaster
+                forecaster = TimeSeriesForecaster()
+                
+                # Preprocessing
+                dataset = forecaster.preprocess_data(
+                    df, 
+                    date_column, 
+                    value_column, 
+                    context_length, 
+                    prediction_length
+                )
+                
+                # Latih model
+                model = forecaster.train_model()
+                
+                # Lakukan prediksi
+                forecasts = forecaster.forecast()
+                
+                # Tampilkan hasil
+                st.subheader('Visualisasi Prediksi')
+                plt = forecaster.visualize_forecast(forecasts)
+                st.pyplot(plt)
+                
+                # Tampilkan detail prediksi
+                forecast_mean = forecasts.mean(axis=0)
+                forecast_lower = np.percentile(forecasts, 10, axis=0)
+                forecast_upper = np.percentile(forecasts, 90, axis=0)
+                
+                prediction_df = pd.DataFrame({
+                    'Prediksi Rata-rata': forecast_mean,
+                    'Batas Bawah (10%)': forecast_lower,
+                    'Batas Atas (90%)': forecast_upper
+                })
+                
+                st.subheader('Detail Prediksi')
+                st.dataframe(prediction_df)
+                
+            except Exception as e:
+                st.error(f"Terjadi kesalahan: {str(e)}")
+
+if __name__ == '__main__':
+    main()