import joblib import gradio as gr import pandas as pd pipe = joblib.load('pipe.joblib') def pred(pi, pt, pla, ss, pr, gs): df = pd.DataFrame( { "Pelvic incidence": pi, "Pelvic tilt": pt, "Lumbar lordosis angle": pla, "Sacral slope": ss, "pelvic radius": pr, "grade of spondylolisthesis": gs }, index=[0] ) prediction = pipe.predict(df.values) if (prediction[0]==0): output = 'Normal' elif (prediction[0]==1): output = 'Anormal' return "La predicción es "+output+'.' iface = gr.Interface( pred, [ gr.Slider(-99,99,label="Pelvic incidence", value=0), gr.Slider(-99,99,label="Pelvic tilt", value=0), gr.Slider(-99,99,label="Lumbar lordosis angle", value=0), gr.Slider(-99,99,label="Sacral slope", value=0), gr.Slider(-99,99,label="Pelvic radius", value=0), gr.Slider(-99,99,label="Grade of spondylolisthesis", value=0), ], "text", examples=[ [63.0278175, 22.55258597, 39.60911701, 40.47523153, 98.67291675, -0.254399986], [40.34929637, 10.19474845, 37.96774659, 30.15454792, 128.0099272, 0.458901373], [118.1446548, 38.44950127, 50.83851954, 79.69515353, 81.0245406, 74.04376736], [33.78884314, 3.675109986, 25.5, 30.11373315, 128.3253556, -1.776111234], ], title = 'Orthopaedic column prediction', ) iface.launch()