Create app.py

app.py

@@ -0,0 +1,36 @@
+import pickle
+import gradio as gr
+
+# Load the pickled model
+with open('./Automatidata_gui.pickle', 'rb') as file:
+    model = pickle.load(file)
+
+# Define the function for making predictions
+def automatidata(VendorID, passenger_count, Distance, Duration, rush_hour):
+    inputs = [[VendorID, passenger_count, Distance, Duration, rush_hour]]
+    prediction = model.predict(inputs) 
+    prediction_value = prediction[0][0]
+    return f"Fare amount(approx.) = {round(prediction_value, 2)} $"
+
+# Create the Gradio interface
+automatidata_ga = gr.Interface(fn=automatidata, 
+                               inputs=[
+                                   gr.Number(1, 2, label="VendorID - [1 or 2]"),
+                                   gr.Number(0, 6, label="Passenger Count - [1 to 6]"),
+                                   gr.Number(label="Distance in miles"),
+                                   gr.Number(label="Duration in mins"),
+                                   gr.Number(0, 1, label="Rush Hour - [0 or 1]")
+                               ],
+                               outputs="text", title="New York City Taxi and Limousine Commission (TLC) - Taxi Fares Estimator",
+                               examples = [
+                                            [2,1,2.33,15.09,0],
+                                            [1,2,4.22,24.29,0],
+                                            [1,1,0.71,6.66,0],
+                                            [2,1,0.97,8.37,0],
+                                            [2,3,1.48,8.92,0],
+                                    ],                                                                                 
+                               description="Predicting Taxi Fare Amount Using Machine Learning.",
+                               theme='dark'
+                               )
+
+automatidata_ga.launch(share=True)