Update app.py

app.py

@@ -1 +1,31 @@
-pip install pickle torch numpy
+import gradio as gr
+import pickle 
+
+def read_pickle(path, saved_model_name:str):
+    with open(path + saved_model_name + '.pickle', 'rb') as to_read:
+        model = pickle.load(to_read)
+    return model
+
+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)}  $"
+
+path = 'F:/Case study/Interview preparation/01.Project/1. Automatidata/Final/'
+model = read_pickle(path,'Automatidata_gui')
+
+automatidata_ga = gr.Interface(fn=automatidata, 
+                               inputs = [
+                                         gr.Number(1,2, label="VendorID - [1, 2]"),
+                                         gr.Number(0,6, label="Passenger Count"),
+                                         gr.Number(label="Distance"),
+                                         gr.Number(label="Duration"),
+                                         gr.Number(0,1, label="Rush Hour")
+                                        ],
+                              outputs = "text",title="Taxi Fares Estimater", 
+                              description="Predicting Taxi Fare Amount Using Machine Learning.",
+                               )
+
+if __name__ == "__main__":
+    automatidata_ga.launch(share=True)