add code t5 structure

app.py

@@ -1,61 +1,22 @@
+import json
 import gradio as gr
+import requests as req
 
-from transformers import pipeline
+code_nl = "function for db connection"
 
-question_answerer = pipeline("question-answering")
+CT5_URL = "https://api-inference.huggingface.co/models/deepset/roberta-base-squad2"
+CT5_METHOD = 'POST'
 
-context = r"""
-Extractive Question Answering is the task of extracting an answer from a text given a question. An example of a
-question answering dataset is the SQuAD dataset, which is entirely based on that task. If you would like to fine-tune
-a model on a SQuAD task, you may leverage the examples/pytorch/question-answering/run_squad.py script.
-"""
-
-result = question_answerer(question="What is extractive question answering?", context=context)
-print(f"Answer: '{result['answer']}', score: {round(result['score'], 4)}, start: {result['start']}, end: {result['end']}")
-
-#result = question_answerer(question="What is a good example of a question answering dataset?", context=context)
-#print(f"Answer: '{result['answer']}', score: {round(result['score'], 4)}, start: {result['start']}, end: {result['end']}")
-
-def qa_func(paragraph, question):
-    #return model.predict(paragraph, question)["answer"]
-    return result['answer']
-
-iface = gr.Interface(qa_func, 
-    [
-        gr.inputs.Textbox(lines=7, label="Context", default=context), 
-        gr.inputs.Textbox(lines=1, label="Question", default="What is extractive question answering?"), 
-    ], 
-    gr.outputs.Textbox(label="Answer"))
-iface.launch(share=True)
-
-
-"""
-
-import os, sys
-file_folder = os.path.dirname(os.path.abspath(__file__))
-sys.path.insert(0, os.path.join(file_folder, "utils"))
-from bert import QA
-
-model = QA('bert-large-uncased-whole-word-masking-finetuned-squad')
-def qa_func(paragraph, question):
-    return model.predict(paragraph, question)["answer"]
+def pygen_func(code_nl):
+    inputs = {'code_nl': code_nl}
+    payload = json.dumps(inputs)
+    prediction = req.request(CT5_METHOD, CT5_URL, data=payload)
+    answer = json.loads(prediction.content.decode("utf-8"))
+    return 'BOOM!'
 
 iface = gr.Interface(qa_func, 
     [
-        gr.inputs.Textbox(lines=7, label="Context", default="Victoria has a written constitution enacted in 1975, but based on the 1855 colonial constitution, passed by the United Kingdom Parliament as the Victoria Constitution Act 1855, which establishes the Parliament as the state's law-making body for matters coming under state responsibility. The Victorian Constitution can be amended by the Parliament of Victoria, except for certain 'entrenched' provisions that require either an absolute majority in both houses, a three-fifths majority in both houses, or the approval of the Victorian people in a referendum, depending on the provision."), 
-        gr.inputs.Textbox(lines=1, label="Question", default="When did Victoria enact its constitution?"), 
+        gr.inputs.Textbox(lines=7, label="Context", default=code_nl),  
     ], 
-    gr.outputs.Textbox(label="Answer"))
-iface.launch()
-
-"""
-
-"""
-import gradio as gr
-
-def greet(name):
-    return "Hello " + name + "!!"
-
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
-"""
+    gr.outputs.Textbox(label="Completion"))
+iface.launch(share=True)