Update app.py

app.py

@@ -12,8 +12,6 @@ def get_x(data_set):
 def get_y(data_set):
   return data_set['bf_est']
 
-  
-
 def new_splitter(df):
   # Get the unique values in the 'id' column
   unique_ids = df['id'].unique()
@@ -37,22 +35,55 @@ def new_splitter(df):
   return test, valid
 
 title = "Body Fat Predictor"
-description = "A Body Fat Predictor trained on the subreddit \"guessmybf\"."
-article = "for best preformence upload a front facing photo"
+description = "A Body Fat Predictor trained on the subreddit \"guessmybf\". \
+For best preformence upload a front facing photo. \n The Range button allows \
+you to adjust the range of the predicted body fat percentage. \
+Choosing a higher range results in a less specific prediction but a more ecurate estimate, \
+while a lower range provides a less precise estimate. \n The Examples section \
+provides visual examples of different body fat percentages and how they appear \
+on an individual's body. It is important to note that two individuals can have \
+the same body fat percentage but look different due to differences in body composition \
+(such as muscle mass)."
 
 learner = load_learner("bf_model.pkl")
 
-def predict_bf(img):
-  # pil_img = PILImage.create(img)
-  return round(float(learner.predict(img)[1]),2)
+# def predict_bf(img):
+#   return round(float(learner.predict(img)[1]),2)
+
+def predict_bf(img, range=0):
+  if range not in [0,1,2,3,4]:
+    range = 1
+  prediction = math.floor(float(learner.predict(img)[1]))
+  if range ==0:
+    return f"{str(prediction)}%"
+  else:
+     return  f"{str(round(prediction) - range)}-{str(round(prediction) + range)}%" 
+
+
+examples=[
+        ['/exp/6-8.jpg', '6-8'],  
+        ['/exp/7-9.jpg', '7-9'], 
+        ['/exp/9-11.jpg', '9-11'],
+        ['/exp/9-11v2.jpg', '9-11'],
+        ['/exp/10-12.jpg', '10-12'],
+        ['/exp/10-12v2.jpg', '10-12'],
+        ['/exp/11-13.jpg', '11-13'],
+        ['/exp/13-15.jpg', '13-15'],
+        ['/exp/13-15v2.jpg', '13-15'],
+        ['/exp/15-17.jpg', '15-17'],
+        ['/exp/15-17v2.jpg', '15-17'],
+        ['/exp/15-17v3.jpg', '15-17'],
+        ['/exp/17-19.jpg', '17-19'],
+        ['/exp/19-20.jpg', '19-20'],
+        ['/exp/19-20v2.jpg', '19-20'],
+        ['/exp/21-23.jpg', '21-23'],
+        ['/exp/22-24.jpg', '22-24'],
+        ['/exp/23-25.jpg', '23-25'],
+        ['/exp/24-26.jpg', '24-26']
+        ]
 
-image = gr.Image(shape=(192,192))
-# label = gr.float()
-intf = gr.Interface(fn =predict_bf, inputs = image, outputs = "number", title=title, description=description, article=article)
-intf.launch(inline= False)
 
-# def greet(name):
-#     return "Hello " + name + "!!" + "This is version 2!!!"
-
-# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-# iface.launch()
+image = gr.Image(shape=(192,192))
+intf = gr.Interface(fn =predict_bf, inputs = [image, gr.Radio([0,1,2,3,4])], outputs = gr.outputs.Textbox(), 
+                    title = title, description = description, examples=examples)
+intf.launch(inline= True)