first upload for app.py, req, model-v1

app.py

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+import os
+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")
+
+# hf_token = os.environ["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"]
+# hf_writer = gr.HuggingFaceDatasetSaver(hf_token, "diamond-price-predictor-logs")
+
+model_output = gr.Label(label="Predicted Price (USD)")
+
+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,
+    theme=gr.themes.Soft(),
+    title="Diamond Price Predictor",
+    description="This API allows you to predict the price of a diamond given its attributes",
+    # allow_flagging="auto",
+    # flagging_callback=hf_writer,
+    concurrency_limit=8
+)
+
+demo.queue()
+demo.launch(share=False)

model-v1.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:41dcd60d356e52288c1c14eab8a25ea684958f04a9000770ed71153e49ad38af
+size 4721680

requirements.txt

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+gradio==4.22.0
+pandas==1.4.0
+scikit-learn==1.2.0