Update app.py

app.py

@@ -2,23 +2,47 @@ import pandas as pd
 import pickle
 import numpy as np
 from sklearn import tree
+
+import gradio as gr
+
+
+def multiline(textData):
+        print("inp", textData)
+        col=["HighBP","HighChol","CholCheck","BMI","Smoker","Stroke","HeartDiseaseorAttack","PhysActivity","Fruits"
+        ,"Veggies","HvyAlcoholConsump","AnyHealthcare","NoDocbcCost","GenHlth","MentHlth","PhysHlth","DiffWalk"
+        ,"Sex","Age","Education","Income"]
+        empty_array = []
+        for line in textData.split("\n"):
+          abc = list(map(float, line.split(",")));
+          print(abc)
+          empty_array = np.append(empty_array, np.array([abc]), axis=0)      
+        print(empty_array)
+        ddf = pd.DataFrame(empty_array, columns=col)
+        print(ddf)
+
+        print(loaded_model.predict(ddf))
+        return ddf
+
+
+
+
+
+
+
+
+
 # Load the Random Forest CLassifier model
 filename = 'model.pkl'
 loaded_model = pickle.load(open(filename, 'rb'))
 print(loaded_model)
 
-input = [0.0,0.0,1.0,26.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,3.0,0.0,15.0,0.0,0.0,7.0,5.0,7.0]
-manualInput =[
- [1.0,1.0,1.0,37.0,1.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,5.0,0.0,0.0,1.0,1.0,10.0,6.0,5.0]
- #,[1.0,1.0,1.0,28.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,4.0,0.0,0.0,0.0,1.0,12.0,2.0,4.0]
- #,[1.0,1.0,1.0,27.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,1.0,0.0,4.0,20.0,20.0,1.0,0.0,8.0,4.0,7.0]
-]
-print(input, manualInput)
-col=["HighBP","HighChol","CholCheck","BMI","Smoker","Stroke","HeartDiseaseorAttack","PhysActivity","Fruits"
-     ,"Veggies","HvyAlcoholConsump","AnyHealthcare","NoDocbcCost","GenHlth","MentHlth","PhysHlth","DiffWalk"
-     ,"Sex","Age","Education","Income"]
-ddf = pd.DataFrame(manualInput, columns=col)
-print(ddf)
-
-result = loaded_model.predict(ddf)
-print(result)
+
+def predict2(content):
+        multiple_records = multiline(content)
+   
+        result = classifier.predict(multiple_records)
+        print(result)
+
+
+iface = gr.Interface(fn=predict2, inputs="text", outputs="text")
+iface.launch()