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app.py

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+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()

pipe.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2617d6ad558609bccb3be20c5759d3aa9ba43df0b0aa681ba715117e508560ad
+size 11561