First implementtion

Author: m-messer

Dockerfile

@@ -32,6 +32,7 @@ ENV FUNCTION_COMMAND="python"
 ENV FUNCTION_ARGS="-m,evaluation_function.main"
 
 # The transport to use for the RPC server
-ENV FUNCTION_RPC_TRANSPORT="ipc"
+#ENV FUNCTION_RPC_TRANSPORT="ipc"
+ENV FUNCTION_INTERFACE="file"
 
 ENV LOG_LEVEL="debug"

README.md

@@ -1,51 +1,6 @@
-# Python Evaluation Function
+# Wolfram Middle-Layer Evaluation Function
 
-This repository contains the boilerplate code needed to create a containerized evaluation function written in Python.
-
-## Quickstart
-
-This chapter helps you to quickly set up a new Python evaluation function using this template repository.
-
-> [!NOTE]
-> After setting up the evaluation function, delete this chapter from the `README.md` file, and add your own documentation.
-
-#### 1. Create a new repository
-
-- In GitHub, choose `Use this template` > `Create a new repository` in the repository toolbar.
-
-- Choose the owner, and pick a name for the new repository.
-
-  > [!IMPORTANT]
-  > If you want to deploy the evaluation function to Lambda Feedback, make sure to choose the Lambda Feedback organization as the owner.
-
-- Set the visibility to `Public` or `Private`.
-
-  > [!IMPORTANT]
-  > If you want to use GitHub [deployment protection rules](https://docs.github.com/en/actions/deployment/targeting-different-environments/using-environments-for-deployment#deployment-protection-rules), make sure to set the visibility to `Public`.
-
-- Click on `Create repository`.
-
-#### 2. Clone the new repository
-
-Clone the new repository to your local machine using the following command:
-
-```bash
-git clone <repository-url>
-```
-
-#### 3. Configure the evaluation function
-
-When deploying to Lambda Feedback, set the evaluation function name in the `config.json` file. Read the [Deploy to Lambda Feedback](#deploy-to-lambda-feedback) section for more information.
-
-#### 4. Develop the evaluation function
-
-You're ready to start developing your evaluation function. Head over to the [Development](#development) section to learn more.
-
-#### 5. Update the README
-
-In the `README.md` file, change the title and description so it fits the purpose of your evaluation function.
-
-Also, don't forget to delete the Quickstart chapter from the `README.md` file after you've completed these steps.
+This repository forwards requests to the Wolfram Cloud API.
 
 ## Usage
 

config.json

@@ -1,3 +1,3 @@
 {
-  "EvaluationFunctionName": ""
+  "EvaluationFunctionName": "wolfram-middle-layer"
 }

evaluation_function/dev.py

@@ -2,23 +2,24 @@
 
 from lf_toolkit.shared.params import Params
 
-from .evaluation import evaluation_function
+from evaluation_function.evaluation import evaluation_function
+from evaluation_function.preview import preview_function
+
 
 def dev():
     """Run the evaluation function from the command line for development purposes.
 
     Usage: python -m evaluation_function.dev <answer> <response>
     """
-    if len(sys.argv) < 3:
-        print("Usage: python -m evaluation_function.dev <answer> <response>")
-        return
 
-    answer = sys.argv[1]
-    response = sys.argv[2]
+    answer = "x+y"
+    response = "x+y"
+    params = {"type": "structure"}
 
-    result = evaluation_function(answer, response, Params())
+    # result = evaluation_function(answer, response, params)
+    result = preview_function(response, {})
 
-    print(result.to_dict())
+    print(result)
 
 if __name__ == "__main__":
     dev()

evaluation_function/evaluation.py

@@ -1,5 +1,8 @@
+import json
 from typing import Any
 from lf_toolkit.evaluation import Result, Params
+import os
+import requests
 
 def evaluation_function(
     response: Any,
@@ -29,6 +32,33 @@ def evaluation_function(
     to output the evaluation response.
     """
 
-    return Result(
-        is_correct=response == answer
-    )
+    url = os.getenv("EVALUATE_API")
+
+    eval_type = params["type"]
+    data_payload = {
+        "type": eval_type,
+        "response": response,
+        "answer": answer,
+        "params": json.dumps(params)
+    }
+
+    response = requests.post(url, data=data_payload)
+    response_json = response.json()
+
+    if "Success" in response_json:
+        if not response_json["Success"]:
+            result = Result(is_correct=False)
+            result.add_feedback(tag="error", feedback="Wolfram Error")
+            return result
+
+    if "error" in response_json:
+        if response_json["error"] is not None:
+            result = Result(is_correct=False)
+            result.add_feedback(tag="error", feedback=response_json["error"])
+            return result
+
+
+    result = Result(is_correct=response_json["is_correct"])
+    result.add_feedback(tag="feedback", feedback=response_json["feedback"])
+
+    return result

evaluation_function/preview.py

@@ -1,4 +1,7 @@
+import os
 from typing import Any
+
+import requests
 from lf_toolkit.preview import Result, Params, Preview
 
 def preview_function(response: Any, params: Params) -> Result:
@@ -21,10 +24,31 @@ def preview_function(response: Any, params: Params) -> Result:
     The way you wish to structure you code (all in this function, or
     split into many) is entirely up to you.
     """
-
     try:
-        return Result(preview=Preview(sympy=response))
-    except FeedbackException as e:
-        return Result(preview=Preview(feedback=str(e)))
+        url = os.getenv("PREVIEW_API")
+
+        data_payload = {
+            "response": response
+        }
+
+        wolfram_response = requests.post(url, data=data_payload)
+        response_json = wolfram_response.json()
+
+        print(response_json)
+
+        if "Success" in response_json:
+            if not response_json["Success"]:
+                result = Result(preview=Preview(feedback=response_json["error"]))
+                return result
+
+        if "error" in response_json:
+            if response_json["error"] is not None:
+                result = Result(preview=Preview(feedback=response_json["error"]))
+                return result
+
+
+        result = Result(preview=Preview(latex=response_json["latexString"],sympy=response_json["sympyString"]))
+
+        return result
     except Exception as e:
         return Result(preview=Preview(feedback=str(e)))