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Specify the ingredients and identify the glass

app.py

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+import gradio as gr
+import pickle
+import pandas as pd 
+
+def predict(RI, Na, Mg, Al, Si, K, Ca, Ba, Fe):
+    tst = pd.DataFrame([[RI, Na, Mg, Al, Si, K, Ca, Ba, Fe]],
+          columns=['RI', 'Na', 'Mg', 'Al', 'Si', 'K', 'Ca', 'Ba', 'Fe'])    
+    filehandler = open("stack_gls.pkl", "rb")
+    bm_loaded = pickle.load(filehandler)
+    print(tst)
+    return bm_loaded.predict(tst)[0] 
+      
+
+with gr.Blocks() as demo:
+    with gr.Row():
+      RI = gr.Number(label='RI')
+      Na = gr.Number(label='Na')
+      Mg = gr.Number(label='Mg')
+    with gr.Row():
+      Al = gr.Number(label='Al')
+      Si = gr.Number(label='Si')
+      K = gr.Number(label='K')
+    with gr.Row():
+      Ca = gr.Number(label='Ca')
+      Ba = gr.Number(label='Ba')
+      Fe = gr.Number(label='Fe')
+    with gr.Row(): 
+      Type = gr.Text(label='Type') 
+    with gr.Row():  
+      button = gr.Button(value="Which Glass?")
+      button.click(predict,
+            inputs=[RI, Na, Mg, Al, Si, K, Ca, Ba, Fe],
+            outputs=[Type])
+
+
+demo.launch()

requirements.txt

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+scikit-learn==1.7.2
+pandas==2.3.3
+gradio==5.48.0

stack_gls.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b88bb714307bb74df3e204e6cab127bc85402ccd62c7e77b68ee5e4aea664bbf
+size 265585