Update strings.py

strings.py

@@ -1,13 +1,3 @@
-def pygen_func(nl_code_intent):
-    pass # TODO: generate code PL from intent NL + search in corpus
-    # inputs = {'code_nl': code_nl}    
-    # payload = json.dumps(inputs)
-    # prediction = req.request(CT5_METHOD, CT5_URL, data=payload)
-    # prediction = req.request(CT5_METHOD, CT5_URL, json=req_data)
-    # answer = json.loads(prediction.content.decode("utf-8"))
-    # return str(answer)
-    # CT5_URL = "https://api-inference.huggingface.co/models/nielsr/codet5-small-code-summarization-ruby"
-
 dfs_code = r"""
 def dfs(visited, graph, node):  #function for dfs 
     if node not in visited:
@@ -149,3 +139,13 @@ For further details, see the [CodeXGLUE](https://github.com/microsoft/CodeXGLUE)
 """
 
 descr_string = 'The application takes as input the python code for a function, or a class, and generates a documentation string, or code comment, for it using codeT5 fine tuned for code2text generation. Code to text generation, or code summarization, is a CodeXGLUE generation, or sequence to sequence, downstream task. CodeXGLUE stands for General Language Understanding Evaluation benchmark *for code*, which includes diversified code intelligence downstream inference tasks and datasets.'
+
+def pygen_func(nl_code_intent):
+    pass # TODO: generate code PL from intent NL + search in corpus
+    # inputs = {'code_nl': code_nl}    
+    # payload = json.dumps(inputs)
+    # prediction = req.request(CT5_METHOD, CT5_URL, data=payload)
+    # prediction = req.request(CT5_METHOD, CT5_URL, json=req_data)
+    # answer = json.loads(prediction.content.decode("utf-8"))
+    # return str(answer)
+    # CT5_URL = "https://api-inference.huggingface.co/models/nielsr/codet5-small-code-summarization-ruby"