Using Type4Py Rest API

Usage Example

Type4Py's Rest API provides a predict endpoint, which accepts a Python source file via a POST request. A minimal example is given below in Python.

import requests

# Replace `example.py` with your desired Python source file.
with open('example.py') as f:
    r = requests.post("https://type4py.com/api/predict?tc=0", f.read())
    print(r.json())

The response contains type information in JSON format. See here for the description of the response format. For other programming languages, it should be possible to write a similar code like the above example.

Note 1: The sent Python file should have valid syntax.

Note 2: Above example uses type4py.com, our public API. Use localhost:5001 instead of type4py.com if Type4Py's Docker image is running.

JSON Response

A dummy response is shown below to describe the JSON's fields:

{
    "error":"None",
    "response":{
      "classes":[
        {
          "cls_var_ln":{
            "cls_var_name":[[4,4],[4,13]]
          },
          "cls_var_occur":{
            "cls_var_name":[
              [
                "token",
                "var",
                "name",
              ]
            ]
          },
          "funcs":[
            {
              "docstring":{
                "func":"None",
                "long_descr":"None",
                "ret":"None"
              },
              "fn_lc":[[5,4],[8,28]],
              "fn_var_ln":{
                "var_name":[[7,8],[7,16]],
              },
              "fn_var_occur":{
                "var_name":[
                  [
                    "token",
                    "var",
                    "name"
                  ]
                ]
              },
              "name":"__init__",
              "params":{
                "age":"int",
              },
              "params_descr":{
                "age":"comment"
              },
              "params_occur":{
                "age":[
                  [
                    "self",
                    "age",
                    "age"
                  ]
                ],
              },
              "params_p":{
                "age":[
                  [
                    "int",
                    0.9999999991180025
                  ],
                  [
                    "str",
                    2.3463255785983247e-10
                  ],
                ],
              },
              "q_name":"Person.__init__",
              "ret_exprs":[
                
              ],
              "ret_type":"None",
              "variables":{
                "age":""
              },
              "variables_p":{
                "age":[
                  [
                    "int",
                    0.2801499039103035
                  ]
                ]
              }
            },
            {
              "docstring":{
                "func":"None",
                "long_descr":"None",
                "ret":"None"
              },
              "fn_lc":[[10,4],[11,24]],
              "fn_var_ln":{
              },
              "fn_var_occur":{
              },
              "name":"get_name",
              "params":{
              },
              "params_descr":{
              },
              "params_occur":{
              },
              "params_p":{
              },
              "q_name":"Person.get_name",
              "ret_exprs":[
                "return self.name"
              ],
              "ret_type":"",
              "ret_type_p":[
                ["str", 0.7073830464758581]
              ],
              "variables":{
              },
              "variables_p":{
              }
            }
          ],
          "name":"Person",
          "q_name":"Person",
          "variables":{
            "person_id":""
          },
          "variables_p":{
            "person_id":[
              ["str", 0.703074717210447],
            ]
          }
        }
      ],
      "funcs":[
        {
          "docstring":{
            "func":"None",
            "long_descr":"None",
            "ret":"None"
          },
          "fn_lc":[[18,0],[25,18]],
          "fn_var_ln":{
            "leave_hours":[[19,4],[19,15]],
          },
          "fn_var_occur":{
            "leave_hours":[
              [
                "no_hours",
                "leave_hours"
              ]
            ],
          },
          "name":"work",
          "params":{
            "no_hours":""
          },
          "params_descr":{
            "no_hours":""
          },
          "params_occur":{
            "no_hours":[
              [
                "no_hours",
                "leave_hours"
              ]
            ]
          },
          "params_p":{
            "no_hours":[
              ["Type", 0.0999]
            ]
          },
          "q_name":"work",
          "ret_exprs":[
            "return \"Done!\""
          ],
          "ret_type":"",
          "ret_type_p":[
            ["str", 0.287441260068372],
          ],
          "variables":{
            "leave_hours":"",
          },
          "variables_p":{
            "leave_hours":[
              ["int",0.2],
            ]
          }
        }
      ],
      "imports":["os"],
      "mod_var_ln":{
        "A_GLOBAL_VAR":[[1,0],[1,12]]
      },
      "mod_var_occur":{
        "A_GLOBAL_VAR":["token"]
      },
      "no_types_annot":{
        "D":1,
        "I":0,
        "U":14
      },
      "session_id":"a0bvkdCC8utA35r8JrOho07FrDpV9qaLr2lccFzoXB4",
      "set":"None",
      "tc":[false,"None"],
      "type_annot_cove":0.07,
      "typed_seq":"",
      "untyped_seq":"",
      "variables":{
        "A_GLOBAL_VAR":""
      },
      "variables_p":{
        "A_GLOBAL_VAR":[
          ["str",0.41389838221497904],
        ]
      }
    }
  }