create app.py

app.py

@@ -0,0 +1,30 @@
+import gradio as gr
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+from sklearn.preprocessing import MinMaxScaler
+
+# Load the saved model
+model = load_model('lstm_model')
+
+# Dummy scaler (replace this with the actual scaler used in training)
+scaler = MinMaxScaler(feature_range=(0, 1))
+
+# Function for Prediction
+def predict_sales(input_data):
+    # Reshape and scale input data
+    input_data = np.array(input_data).reshape(1, 30, 1)
+    scaled_input = scaler.transform(input_data)
+
+    # Predict
+    prediction = model.predict(scaled_input)
+    actual_prediction = scaler.inverse_transform(prediction)
+
+    return actual_prediction[0][0]
+
+# Gradio Interface
+inputs = gr.inputs.Dataframe(headers=["Sales"], type="numpy", row_count=30, col_count=1)
+outputs = gr.outputs.Textbox()
+
+interface = gr.Interface(fn=predict_sales, inputs=inputs, outputs=outputs, title="LSTM Sales Forecasting")
+interface.launch()