Create app.py

app.py

@@ -0,0 +1,24 @@
+import gradio as gr
+import numpy as np
+def predict_diabetes(preg,glu,bp,st,ins,bmi,dpf,age):
+    input_array=np.array([[preg,glu,bp,st,ins,bmi,dpf,age]])
+    pred=LR.predict(input_array)
+    output=classify(pred[0])
+    if output=='negative':
+      return [(0,output)]
+    else:
+      return [(1,output)]
+preg = gr.inputs.Slider(minimum=0, maximum=17, default=2, label="Pregnancy")
+glu = gr.inputs.Slider(minimum=0, maximum=199, default=2, label="glucose")
+bp = gr.inputs.Slider(minimum=0, maximum=122, default=2, label="blood prussure")
+st = gr.inputs.Slider(minimum=0, maximum=99, default=2, label="skin thickness")
+ins = gr.inputs.Slider(minimum=0, maximum=846, default=2, label="insulin")
+bmi = gr.inputs.Slider(minimum=0, maximum=67.1, default=2, label="bmi")
+dpf = gr.inputs.Slider(minimum=0, maximum=2.5, default=2, label="diabetes pedigree function")
+age = gr.inputs.Slider(minimum=20, maximum=100, default=2, label="age")
+
+
+op=gr.outputs.HighlightedText(color_map={ "negative": "green",
+        "positive": "red",})
+
+gr.Interface(predict_diabetes, inputs=[preg,glu,bp,st,ins,bmi,dpf,age], outputs=op,live=True).launch(share=True)