version inicial

app.py

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+import gradio as gr
+import pickle
+import pandas as pd
+
+def predict(x1,x2):
+
+    new_data = pd.DataFrame({'Floor space of the shop':[x1],
+                             'Distance to the nearest station':[x2]}
+                            ) 
+         # Load the trained model
+    with open('modelo_regresion.pkl', 'rb') as f:
+        model = pickle.load(f)
+    y_pred = model.predict(new_data) 
+    return y_pred[0]
+
+
+if __name__ == "__main__":
+    demo = gr.Interface(fn=predict, inputs=["number","number"], outputs="number")
+    demo.launch()

modelo_regresion.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7d30dcbf9886112b4c47138e72cd898a3c0360ec764f0474e2b95a81e31abe24
+size 582

panaderias.csv

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+Bakery,Floor space of the shop,Distance to the nearest station,Monthly sales
+Yumenoooka Shop,10,80,469
+Terai Station Shop,8,0,366
+Sone Shop,8,200,371
+Hashimoto Station Shop,5,200,208
+Kikyou Town Shop,7,300,246
+Post Office Shop,8,230,297
+Suidobashi Station Shop,7,40,363
+Rokujo Station Shop,9,0,436
+Wakaba Riverside Shop,6,330,198
+Misato Shop,9,180,364

requirement.txt

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+gradio
+scikit-learn
+pandas