Add application file

app.py

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+import gradio as gr
+import numpy as np
+import pickle
+
+# Load the trained model
+with open("model.pkl", "rb") as f:
+    model = pickle.load(f)
+
+# Define the mappings for 'Type of Travel' and 'Class' before using them
+type_of_travel_map = {'Personal Travel': 0, 'Business travel': 1}
+class_map = {'Eco Plus': 0, 'Business': 1, 'Eco': 2}
+
+def predict_satisfaction(online_boarding, type_of_travel, inflight_entertainment, seat_comfort,
+                         onboard_service, flight_class, leg_room_service, cleanliness, flight_distance, inflight_wifi_service):
+    try:
+        # Check the inputs
+        print(f"Received inputs: {online_boarding}, {type_of_travel}, {inflight_entertainment}, {seat_comfort}, "
+              f"{onboard_service}, {flight_class}, {leg_room_service}, {cleanliness}, {flight_distance}, {inflight_wifi_service}")
+
+        # Map the inputs to the expected model format
+        features = [
+            online_boarding,
+            type_of_travel_map[type_of_travel],  # map 'Type of Travel'
+            inflight_entertainment,
+            seat_comfort,
+            onboard_service,
+            class_map[flight_class],  # map 'Class'
+            leg_room_service,
+            cleanliness,
+            flight_distance,
+            inflight_wifi_service
+        ]
+
+        # Get probabilities of satisfaction (class 0) and dissatisfaction (class 1)
+        probabilities = model.predict_proba([features])[0]  # Assuming it returns a 2D array
+
+        # Set threshold for satisfaction
+        satisfaction_probability = probabilities[0]  # Probability for being satisfied (class 0)
+
+        # Apply threshold of 0.87 for satisfaction
+        if satisfaction_probability >= 0.87:
+            return "Satisfied"
+        else:
+            return "Neutral or Dissatisfied"
+
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+import gradio as gr
+inputs = [
+    gr.Slider(minimum=0, maximum=5, label="Online Boarding"),
+    gr.Dropdown(choices=["Personal Travel", "Business travel"], label="Type of Travel"),
+    gr.Slider(minimum=0, maximum=5, label="Inflight Entertainment"),
+    gr.Slider(minimum=0, maximum=5, label="Seat Comfort"),
+    gr.Slider(minimum=0, maximum=5, label="On-board Service"),
+    gr.Dropdown(choices=["Eco Plus", "Business", "Eco"], label="Class"),
+    gr.Slider(minimum=0, maximum=5, label="Leg Room Service"),
+    gr.Slider(minimum=0, maximum=5, label="Cleanliness"),
+    gr.Slider(minimum=31, maximum=4983, label="Flight Distance"),
+    gr.Slider(minimum=0, maximum=5, label="Inflight Wifi Service")
+]
+
+outputs = gr.Textbox(label="Customer Satisfaction Prediction")
+
+gr.Interface(fn=predict_satisfaction, inputs=inputs, outputs=outputs).launch()

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e427b501a03392093d2a1393c4ec0fe6581f2ae7a5051a3a81f71cd7107e3307
+size 119728088