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demo.yaml

@@ -4,38 +4,47 @@ lite_metadata:
 class_string: gradio.interface.Interface
 kwargs:
   title: gitio app
-  description: 'Let the user draw two figures, circle, rectangle or square and compute
-    the total area '
+  description: An app to recieve n numbers comma separated and spit a Random Function
+    with n variables and show 2 int inputs to evaluate the function derivative and
+    integral
   article: null
   thumbnail: null
   theme: gradio/seafoam
   css: null
   allow_flagging: never
   inputs:
-  - class_string: gradio.components.Dataframe
+  - class_string: gradio.components.Textbox
     kwargs:
-      label: shapes
-      type: array
-  - class_string: gradio.components.Dataframe
+      label: function
+  - class_string: gradio.components.Number
     kwargs:
-      label: dimensions
-      type: array
-  outputs:
+      label: a
+      precision: 0
   - class_string: gradio.components.Number
+    kwargs:
+      label: b
+      precision: 0
+  outputs:
+  - class_string: gradio.components.Dataframe
     kwargs:
       label: output
+      type: array
   fn:
     class_string: gradify.gradify_closure
     kwargs:
       argmaps:
-      - label: shapes
-        postprocessing: 'lambda array: list(map(str, array[0]))'
-      - label: dimensions
-        postprocessing: 'lambda array: list(map(float, array[0]))'
+      - label: function
+        postprocessing: null
+      - label: a
+        postprocessing: null
+      - label: b
+        postprocessing: null
       func_kwargs: {}
-      source: "import math\n\n\ndef calculate_area(shapes, dimensions):\n    total_area\
-        \ = 0\n    for i in range(len(shapes)):\n        if shapes[i] == 'circle':\n\
-        \            total_area += math.pi * dimensions[i] ** 2\n        elif shapes[i]\
-        \ == 'rectangle':\n            total_area += dimensions[i] * dimensions[i\
-        \ + 1]\n        elif shapes[i] == 'square':\n            total_area += dimensions[i]\
-        \ ** 2\n    return total_area"
+      source: "import random\nimport numpy as np\n\n\ndef random_function(n):\n  \
+        \  variables = [f'x{i}' for i in range(n)]\n    function = ''\n    for i in\
+        \ range(n):\n        coefficient = random.randint(-10, 10)\n        function\
+        \ += f'{coefficient}*{variables[i]} + '\n    function = function[:-3]\n  \
+        \  return function\n\n\ndef evaluate_derivative_integral(function, a, b):\n\
+        \    x = np.linspace(a, b, 1000)\n    y = eval(function)\n    derivative =\
+        \ np.gradient(y, x)[0]\n    integral = np.trapz(y, x)\n    return [derivative,\
+        \ integral]\n"