Create app.py

app.py

@@ -0,0 +1,58 @@
+import joblib
+
+import gradio as gr
+import pandas as pd
+
+price_predictor = joblib.load('model-v1.joblib')
+
+carat_input = gr.Number(label="Carat")
+
+shape_input = gr.Dropdown(
+    ['Round', 'Princess', 'Emerald', 'Asscher', 'Cushion', 'Radiant', 'Oval', 
+     'Pear', 'Marquise'], 
+     label="Shape"
+)
+
+cut_input = gr.Dropdown(
+    ['Ideal', 'Premium', 'Very Good', 'Good', 'Fair'], 
+    label="Cut"
+)
+
+color_input = gr.Dropdown(
+    ['D', 'E', 'F', 'G', 'H', 'I', 'J'], 
+    label="Color"
+)
+
+clarity_input = gr.Dropdown(
+    ['IF', 'VVS1', 'VVS2', 'VS1', 'VS2', 'SI1', 'SI2', 'I1'], 
+    label="Clarity"
+)
+report_input = gr.Dropdown(['GIA', 'IGI', 'HRD', 'AGS'], label="Report")
+type_input = gr.Dropdown(['Natural', 'Lab Grown'], label="Type")
+
+model_output = gr.Label(label="Predicted Price")
+
+def predict_price(carat, shape, cut, color, clarity, report, type):
+    sample = {
+        'carat': carat,
+        'shape': shape,
+        'cut': cut,
+        'color': color,
+        'clarity': clarity,
+        'report': report,
+        'type': type,
+    }
+    data_point = pd.DataFrame([sample])
+    prediction = price_predictor.predict(data_point).tolist()
+    return prediction[0]
+
+demo = gr.Interface(fn=predict_price,
+                    inputs=[carat_input, shape_input, cut_input, color_input, 
+                            clarity_input, report_input, type_input],
+                    outputs=model_output,
+                    title="Diamond Price Predictor",
+                    description="This API allows you to predict the price of a diamond given its attributes",
+                    flagging_options=["Incorrect", "Correct"])
+
+demo.queue(concurrency_count=8)
+demo.launch(share=False)