Create app.py

app.py

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+import numpy as np
+import pandas as pd
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, LSTM
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import MinMaxScaler
+
+# Function to preprocess data
+def preprocess_data(data):
+    # Convert date column to datetime
+    data['date'] = pd.to_datetime(data['date'])
+    
+    # Create a sequence feature
+    data['sequence'] = np.arange(len(data))
+    
+    # Scale the demand values
+    scaler = MinMaxScaler()
+    data['demand_scaled'] = scaler.fit_transform(data['demand'].values.reshape(-1, 1))
+    
+    return data, scaler
+
+# Function for demand forecasting
+def forecast_demand(data, model_type='LSTM'):
+    data, scaler = preprocess_data(data)
+    
+    X = data[['sequence']]
+    y = data['demand_scaled']
+    
+    # Split the data into train and test sets
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    
+    if model_type == 'LSTM':
+        # Reshape the input data for LSTM
+        X_train = X_train.values.reshape(-1, 1, 1)
+        X_test = X_test.values.reshape(-1, 1, 1)
+        
+        model = Sequential()
+        model.add(LSTM(64, input_shape=(1, 1)))
+        model.add(Dense(1))
+        model.compile(optimizer='adam', loss='mean_squared_error')
+        model.fit(X_train, y_train, epochs=50, batch_size=32, verbose=0)
+        forecast = model.predict(X_test)
+        
+        # Inverse scale the forecasted values
+        forecast = scaler.inverse_transform(forecast)
+    elif model_type == 'RandomForest':
+        model = RandomForestRegressor(n_estimators=100, random_state=42)
+        model.fit(X_train, y_train)
+        forecast = model.predict(X_test)
+        
+        # Inverse scale the forecasted values
+        forecast = scaler.inverse_transform(forecast.reshape(-1, 1)).squeeze()
+    
+    # Inverse scale the actual test values
+    y_test = scaler.inverse_transform(y_test.values.reshape(-1, 1)).squeeze()
+    
+    return forecast, y_test
+
+# Gradio interface
+import gradio as gr
+
+def run_app():
+    with gr.Blocks() as demo:
+        gr.Markdown("# Intelligent Inventory and Supply Chain Management")
+
+        with gr.Tab("Demand Forecasting"):
+            with gr.Row():
+                model_type = gr.Radio(["LSTM", "RandomForest"], label="Model Type")
+            with gr.Row():
+                data_upload = gr.File(label="Upload Data")
+                forecast_button = gr.Button("Forecast Demand")
+            with gr.Row():
+                forecast_plot = gr.Plot()
+                forecast_output = gr.Dataframe(label="Forecasted Demand")
+
+        forecast_button.click(forecast_demand_wrapper, inputs=[data_upload, model_type], outputs=[forecast_plot, forecast_output])
+
+    demo.launch()
+
+if __name__ == "__main__":
+    run_app()