Upload 3 files

app.py

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+import gradio as gr
+import pickle
+
+# Load the KNN model from the pickle file
+with open('ols_model_results.pkl', 'rb') as f:
+    lr_model = pickle.load(f)
+
+# Define the attributes without the intercept
+attributes = [
+    ("CRIM", (0, 100)),
+    ("ZN", (0, 100)),
+    ("INDUS", (0, 30)),
+    ("CHAS", (0, 1)),
+    ("NOX", (0, 1)),
+    ("RM", (3, 9)),
+    ("DIS", (0, 12)),
+    ("RAD", (1, 24)),
+    ("TAX", (100, 700)),
+    ("PTRATIO", (13, 23)),
+    ("LSTAT", (2, 38))
+]
+
+# Create a list of Input objects for each attribute
+attribute_inputs = [
+    gr.Slider(minimum=min_val, maximum=max_val, label=name, default=min_val)
+    for name, (min_val, max_val) in attributes
+]
+
+# Prediction function
+def predict(*args):
+    # Always prepend 1.0 to represent the intercept
+    input_data = [1.0] + [float(arg) for arg in args]
+    predicted_value = lr_model.predict([input_data])[0]
+    return f"Predicted value of the house: ${predicted_value * 1000:.2f}"
+
+# The rest of the code to display UI remains the same.
+
+# Create the interface
+iface = gr.Interface(fn=predict, inputs=attribute_inputs, outputs="text", live=True)
+
+
+# Launch the interface
+
+iface.launch(server_name="0.0.0.0", server_port=7860)

ols_model_results.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:320708a8aec5e3e41f2aebec138e250dd5e7eabc2ddaa1cf711985349cc770c2
+size 158478

requirements.txt

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+gradio 
+scikit-learn==1.2.2
+numpy
+statsmodels==0.14.0