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ML_Model_Size_Frames.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:73013558f49ded38bc66d1b0076f14391c64c047baa5622d2cf6e640b563efcc
+size 14353

app.py

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+import pandas as pd
+import pycaret
+from pycaret.classification import *
+import gradio as gr
+
+model = load_model('ML_Model_Size_Frames')
+load_config('/ML_Model_Size_Frames_config')
+
+def ml_classication(Height,Working_Distance,IPD_Infinity,IPD_WD,IPD_300mm):
+    columns = ['Frame_Size_Refractives','Height','Working_Distance','IPD_Infinity','IPD_WD','IPD_300mm']
+    df = pd.DataFrame(columns=columns)
+    df = df.append({'Height':Height, 
+                    'Working_Distance': Working_Distance, 'IPD_Infinity': IPD_Infinity,
+                    'IPD_WD':IPD_WD, 'IPD_300mm': IPD_300mm}, ignore_index=True)
+    new_prediction = predict_model(model, data=df)
+    prediction = new_prediction['Label'].iloc[0]
+    score = new_prediction['Score'].iloc[0]
+    # 
+    return prediction, score
+
+
+inputs = [
+    gr.Slider(120, 220, 175, label="Height [cm]"),
+    gr.Slider(400, 600, 300, label="Working Distance [mm]"),
+    gr.Slider(45, 100, 60, label='IPD Infinity [mm]'),
+    gr.Slider(45, 100, 60, label="IPD Working Distance [mm]"),
+    gr.Slider(45, 100, 60, label="IPD 300 mm [mm]"),
+]
+outputs = [
+    gr.Text(label='Frame Size'),
+    gr.Text(label = 'Score'),
+]
+
+demo = gr.Interface(
+    fn=ml_classication,
+    inputs=inputs,
+    outputs=outputs,
+    examples=[
+        [163, 490, 62.0, 58.0, 56.0],
+        [180, 510, 63.5, 59.5, 57.5],
+    ],
+    cache_examples=True,
+)
+
+if __name__ == "__main__":
+    demo.launch()
+

requirements.txt

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+gradio==3.8.1
+pandas==1.4.4
+pycaret==2.2.2