Upload app.py

app.py

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+import pickle
+import pandas as pd
+import gradio as gr
+
+eb = "ebas.pkl"
+with open(eb, mode="rb") as f:
+    model = pickle.load(f)
+
+features = ['rooms', 'area', 'pop', 'pop_dens', 'emp', 'tax_income', 'pop_dens_per_room']
+
+def predict(rooms, area, pop, pop_dens, emp, tax_income, pop_dens_per_room):
+    input_data = pd.DataFrame([[rooms, area, pop, pop_dens, emp, tax_income, pop_dens_per_room]],
+                              columns=features)
+    prediction = model.predict(input_data)[0]
+    return prediction
+
+i = gr.Interface(
+    fn=predict,
+    inputs=["number", "number", "number", "number", "number", "number", "number"],
+    outputs="number",
+    title="Apartment Rent Price Predictor with Pop_dens_per_room feature",
+    description="Enter apartment features to predict the rent price.",
+    examples=[[4.5,130,3664,282.9343629344,783,79838,62.874302874311105],
+              [3.5,102,8796,605.3682037164,2469,94471,172.9623439189714]]
+)
+
+i.launch()