Update app.py

app.py

@@ -4,19 +4,26 @@ import tensorflow as tf
 from tf_coder.value_search import colab_interface
 from tf_coder.value_search import value_search_settings as settings_module
 
-inputs = st.text_area('A dict mapping input variable names to input tensors.', placeholder='''
-{'rows': [10, 20, 30],
-'cols': [1, 2, 3, 4],}
-    ''')
-output = st.text_area('The corresponding output tensor.', placeholder='''
-[[11, 12, 13, 14],
-[21, 22, 23, 24],
-[31, 32, 33, 34]]
-    ''')
-    
-constants = st.text_input('A list of relevant scalar constants, if any.', placeholder='[]' )
+def get_problem():
+  """Specifies a problem to run TF-Coder on. Edit this function!"""
+  # A dict mapping input variable names to input tensors.
+  inputs = {
+      'rows': [10, 20, 30],
+      'cols': [1, 2, 3, 4],
+  }
 
-description = st.text_input('An English natural language description of the tensor manipulation', placeholder='Add two vectors with broadcasting to get a matrix')
+  # The single desired output tensor.
+  output = [[11, 12, 13, 14],
+            [21, 22, 23, 24],
+            [31, 32, 33, 34]]
+
+  # A list of relevant scalar constants (if any).
+  constants = []
+
+  # An English description of the tensor manipulation.
+  description = 'add two vectors with broadcasting to get a matrix'
+
+  return inputs, output, constants, description
 
 settings = settings_module.from_dict({
       'timeout': 300,
@@ -25,5 +32,5 @@ settings = settings_module.from_dict({
       'require_all_inputs_used': True,
       'require_one_input_used': False,
   })
- 
+inputs, output, constants, description = get_problem()
 st.write(colab_interface.run_value_search_from_colab(inputs, output, constants, description, settings))