Update app.py

app.py

@@ -1 +1,36 @@
-pip freeze > requirements.txt 
+iimport pickle
+import gradio as gr
+
+# Define the function to read the pickled model
+def read_pickle(path, saved_model_name):
+    with open(path + saved_model_name + '.pickle', 'rb') as to_read:
+        model = pickle.load(to_read)
+    return model
+
+# Load the pickled model
+path = './'  # Assuming the model file is in the current directory
+model = read_pickle(path, "Automatidata_gui")
+
+# 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, 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 Estimator", 
+                               description="Predicting Taxi Fare Amount Using Machine Learning."
+                               )
+
+# Launch the interface
+if __name__ == "__main__":
+    automatidata_ga.launch(share=True)