app.py iteration 2

app.py

@@ -43,13 +43,13 @@ def main(Age,Sex,BMI,No_of_Children,Smoker,Region):
   large=[costpredLin,costpredRand]
   #large.sort(reverse=True)
   if large[0] <= 0 and large[1]<=0:
-    return 'No values found , 404 error'
+    return 'No values found, 404 error'
   if large[0] <= 0 and large[1]>0:
-    return f"The amount to be paid at hospital for the mentioned patient is:- {large[1]}"
+    return f"The amount to be paid at the hospital for the mentioned patient is- {large[1]}"
   if large[0] >0  and large[1] <= 0:
-    return f"The amount to be paid at hospital for the mentioned patient is:- {large[0]}"
+    return f"The amount to be paid at the hospital for the mentioned patient is- {large[0]}"
   if large[0] >=0  and large[1] >=0:
-    return f"The amount to be paid at hospital for the mentioned patient is:- {large[1]}"
+    return f"The amount to be paid at the hospital for the mentioned patient is- {large[1]}"
 
 iface = gr.Interface(fn = main,
                      
@@ -60,28 +60,25 @@ outputs =['text'],
 title="Medical cost prediction ",
 
 description ='''    Description
- 
-Polycystic ovary syndrome (PCOS) is a problem with hormones that happens during the reproductive years. If you have PCOS, you may not have periods very often.
-Or you may have periods that last many days. You may also have too much of a hormone called androgen in your body.
-With PCOS, many small sacs of fluid develop along the outer edge of the ovary. These are called cysts. The small fluid-filled cysts contain immature eggs. 
-These are called follicles. The follicles fail to regularly release eggs.
-The exact cause of PCOS is unknown. Early diagnosis and treatment along with weight loss may lower the risk of long-term complications such as type 2 diabetes and heart disease.
-
-    Output0 - Describes the Prediction made
-
-
-More details about the Inputs taken and how they needed to be taken are given below:
-    * Pregnancies (number of times pregnant)
-    * glucose - two-hour plasma glucose concentration after 75g anhydrous glucose in mg/dl (Can check using a Glucometer at home, or at a clinic)
-    * Blood Pressure (Diastolic Blood Pressure in mmHg)
-    * Skin Thickness (Triceps skin fold thickness in mm)
-    * Insulin Resistance (2 h serum insulin in mu U/ml)( Results could be obtained from a Clinic )
-    * BMI (Body Mass Index in kg/m2), BMI can be calculated using the equation  = Weight/(Height²)
-    * Age (years)
-    * Pedigree Diabetes Function ('function that represents how likely they are to get the disease by extrapolating from their ancestor’s history )
-      Generally, it's between 0 - 2.5, if there wasn't any ancestorial history of diabetes you can choose between 0 and 1, 
-      and if there is any so can select between 1 - 2.5 regarding the seriousness of the family.
+
+          Age: age of the primary beneficiary
+
+          Sex: insurance contractor gender, female = 0, male = 1
+
+          BMI: Body mass index, providing an understanding of the body, weights that are relatively high or low relative to height,
+                objective index of body weight (kg / m ^ 2) using the ratio of height to weight, ideally 18.5 to 24.9
+
+          No_of_Children: Number of children covered by health insurance / Number of dependents
+
+          Smoker: Smoking (Yes(1)/No(0))
+
+          Region: the beneficiary's residential area in the US, northeast =0, northwest =1, southeast = 2, and southwest = 3
+
 ''',
+                     article='''
+                     🩺 Medical Cost Prediction
+A regression model that predicts medical cost with an accuracy above 85% 
+'''
 examples=[[19,0,27.9,0,1,3],[32,1,28.9,0,0,1]])
 
 iface.launch(debug =True)