Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import joblib
+from sklearn.preprocessing import MinMaxScaler
+import plotly.graph_objects as go
+from keras.models import load_model
+from datetime import datetime, timedelta
+
+# Load the trained model
+model = joblib.load('./lstm_model.pkl')
+
+# Function to prepare the data
+def prepare_data(df, time_steps=60):
+    data = df['quantity'].values.reshape(-1, 1)
+    
+    scaler = MinMaxScaler(feature_range=(0, 1))
+    scaled_data = scaler.fit_transform(data)
+    
+    x_test = []
+    for i in range(time_steps, len(scaled_data)):
+        x_test.append(scaled_data[i - time_steps:i, 0])
+    x_test = np.array(x_test)
+    x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
+    
+    return x_test, scaler
+
+# Function to forecast the next 60 days
+def forecast(model, x_test, scaler, time_steps=60, future=60):
+    forecast_data = x_test[-1]  # Use the last sequence of the test set for forecasting
+    forecast_predictions = []
+    for _ in range(future):
+        prediction = model.predict(forecast_data.reshape(1, time_steps, 1))
+        forecast_predictions.append(prediction[0, 0])
+        forecast_data = np.append(forecast_data[1:], prediction[0, 0]).reshape(-1, 1)
+    forecast_predictions = np.array(forecast_predictions).reshape(-1, 1)
+    forecast_predictions = scaler.inverse_transform(forecast_predictions)
+    
+    return forecast_predictions
+
+# Streamlit UI
+st.title('Product Sales Forecasting')
+
+uploaded_file = st.file_uploader("Choose a file")
+if uploaded_file is not None:
+    df = pd.read_csv(uploaded_file, parse_dates=['date'])
+    st.write(df.tail())  # Display the tail of the dataframe
+
+    family = st.selectbox("Select a family", df['family'].unique())
+
+    if st.button('Predict'):
+        df_family = df[df['family'] == family]
+        
+        # Ensure df_family is not empty
+        if df_family.empty:
+            st.write("No data available for the selected family.")
+        else:
+            # Prepare data
+            x_test, scaler = prepare_data(df_family)
+            
+            # Forecast
+            forecast_predictions = forecast(model, x_test, scaler)
+            
+            # Prepare forecast dataframe
+            last_date = df_family['date'].max()
+            forecast_dates = [last_date + timedelta(days=i) for i in range(1, 61)]
+            forecast_df = pd.DataFrame({'date': forecast_dates, 'forecasted_quantity': forecast_predictions.flatten()})
+            
+            # Plot using Plotly with green line
+            fig = go.Figure()
+            fig.add_trace(go.Scatter(x=forecast_df['date'], y=forecast_df['forecasted_quantity'], mode='lines', name='Forecasted Quantity', line=dict(color='green')))
+            fig.update_layout(title=f'Sales Forecast for {family}', xaxis_title='Date', yaxis_title='Quantity Sold')
+            st.plotly_chart(fig)
+            
+            st.write(forecast_df)