1st upload for app.py,requirements.txt and model3.joblib

app.py

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+import os
+import joblib
+
+import gradio as gr
+import pandas as pd
+
+
+price_predictor=joblib.load("model-v3.joblib")
+
+carat_input=gr.Number(label="Carat")
+shape_input=gr.Dropdown(['Round', 'Emerald', 'Marquise', 'Princess', 'Pear', 'Heart',
+                         'Oval', 'Cushion', 'Asscher', 'Radiant'],label='Shape')
+
+cut_input=gr.Dropdown(['Very Good', 'Ideal', 'Super Ideal', 'Good', 'Fair'],label='Cut')
+
+color_input=gr.Dropdown(['J', 'I', 'E', 'F', 'G', 'H', 'D'],label='Color')
+
+clarity_input=gr.Dropdown(['SI2', 'SI1', 'VS2', 'VVS1', 'VS1', 'VVS2', 'IF', 'FL'],label='Clarity')
+
+report_input=gr.Dropdown(['GIA', 'HRD', 'IGI', 'GCAL'],label='Report')
+
+type_input=gr.Dropdown(['natural', 'lab'],label='Type')
+
+
+
+
+model_output=gr.Label(label="Predicted Price (USD)")
+
+def predict_price(carat,shape,cut,color,clarity,report,type):
+    sample={'caret':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="Predictor of Diamond Valuations",
+    description="This application enables you to estimate the value of diamonds based on their characteristics",
+    #allow_flagging="auto",
+    #flagging_callback=hf_writer,
+    concurrency_limit=8
+)

model-v3.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2aaced60665688b76a1a99009f72c30fe47a413069eb7a495ebe95c6661b0f08
+size 5323520

requirements.txt

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+gradio==4.29.0
+pandas>=1.4.0,<1.6.0
+scikit-learn==1.2.2
+joblib==1.2.0
+
+
+