Update app.py

app.py

@@ -4,68 +4,68 @@ import torch
 import json
 
 # load all models
-pragformer = transformers.AutoModel.from_pretrained("Pragformer/PragFormer", trust_remote_code=True)
-pragformer_private = transformers.AutoModel.from_pretrained("Pragformer/PragFormer_private", trust_remote_code=True)
-pragformer_reduction = transformers.AutoModel.from_pretrained("Pragformer/PragFormer_reduction", trust_remote_code=True)
+# pragformer = transformers.AutoModel.from_pretrained("Pragformer/PragFormer", trust_remote_code=True)
+# pragformer_private = transformers.AutoModel.from_pretrained("Pragformer/PragFormer_private", trust_remote_code=True)
+# pragformer_reduction = transformers.AutoModel.from_pretrained("Pragformer/PragFormer_reduction", trust_remote_code=True)
 
 
 #Event Listeners
 
-tokenizer = transformers.AutoTokenizer.from_pretrained('NTUYG/DeepSCC-RoBERTa')
+# tokenizer = transformers.AutoTokenizer.from_pretrained('NTUYG/DeepSCC-RoBERTa')
 
-with open('c_data.json', 'r') as f:
-    data = json.load(f)
+# with open('c_data.json', 'r') as f:
+#     data = json.load(f)
 
-def fill_code(code_pth):
-    return data[code_pth]['pragma'], data[code_pth]['code']
+# def fill_code(code_pth):
+#     return data[code_pth]['pragma'], data[code_pth]['code']
   
 
-def predict(code_txt):
-    code = code_txt.lstrip().rstrip()
-    tokenized = tokenizer.batch_encode_plus(
-                [code],
-                max_length = 150,
-                pad_to_max_length = True,
-                truncation = True
-            )
-    pred = pragformer(torch.tensor(tokenized['input_ids']), torch.tensor(tokenized['attention_mask']))
-
-    y_hat = torch.argmax(pred).item()
-    return 'With OpenMP' if y_hat==1 else 'Without OpenMP', torch.nn.Softmax(dim=1)(pred).squeeze()[y_hat].item()
-
-
-def is_private(code_txt):
-    code = code_txt.lstrip().rstrip()
-    tokenized = tokenizer.batch_encode_plus(
-                [code],
-                max_length = 150,
-                pad_to_max_length = True,
-                truncation = True
-            )
-    pred = pragformer_private(torch.tensor(tokenized['input_ids']), torch.tensor(tokenized['attention_mask']))
-
-    y_hat = torch.argmax(pred).item()
-    if y_hat == 0:
-        return gr.update(visible=False)
-    else:
-        return gr.update(value=f"Confidence: {torch.nn.Softmax(dim=1)(pred).squeeze()[y_hat].item()}", visible=True)
-
-
-def is_reduction(code_txt):
-    code = code_txt.lstrip().rstrip()
-    tokenized = tokenizer.batch_encode_plus(
-                [code],
-                max_length = 150,
-                pad_to_max_length = True,
-                truncation = True
-            )
-    pred = pragformer_reduction(torch.tensor(tokenized['input_ids']), torch.tensor(tokenized['attention_mask']))
-
-    y_hat = torch.argmax(pred).item()
-    if y_hat == 0:
-        return gr.update(visible=False)
-    else:
-        return gr.update(value=f"Confidence: {torch.nn.Softmax(dim=1)(pred).squeeze()[y_hat].item()}", visible=True)
+# def predict(code_txt):
+#     code = code_txt.lstrip().rstrip()
+#     tokenized = tokenizer.batch_encode_plus(
+#                 [code],
+#                 max_length = 150,
+#                 pad_to_max_length = True,
+#                 truncation = True
+#             )
+#     pred = pragformer(torch.tensor(tokenized['input_ids']), torch.tensor(tokenized['attention_mask']))
+
+#     y_hat = torch.argmax(pred).item()
+#     return 'With OpenMP' if y_hat==1 else 'Without OpenMP', torch.nn.Softmax(dim=1)(pred).squeeze()[y_hat].item()
+
+
+# def is_private(code_txt):
+#     code = code_txt.lstrip().rstrip()
+#     tokenized = tokenizer.batch_encode_plus(
+#                 [code],
+#                 max_length = 150,
+#                 pad_to_max_length = True,
+#                 truncation = True
+#             )
+#     pred = pragformer_private(torch.tensor(tokenized['input_ids']), torch.tensor(tokenized['attention_mask']))
+
+#     y_hat = torch.argmax(pred).item()
+#     if y_hat == 0:
+#         return gr.update(visible=False)
+#     else:
+#         return gr.update(value=f"Confidence: {torch.nn.Softmax(dim=1)(pred).squeeze()[y_hat].item()}", visible=True)
+
+
+# def is_reduction(code_txt):
+#     code = code_txt.lstrip().rstrip()
+#     tokenized = tokenizer.batch_encode_plus(
+#                 [code],
+#                 max_length = 150,
+#                 pad_to_max_length = True,
+#                 truncation = True
+#             )
+#     pred = pragformer_reduction(torch.tensor(tokenized['input_ids']), torch.tensor(tokenized['attention_mask']))
+
+#     y_hat = torch.argmax(pred).item()
+#     if y_hat == 0:
+#         return gr.update(visible=False)
+#     else:
+#         return gr.update(value=f"Confidence: {torch.nn.Softmax(dim=1)(pred).squeeze()[y_hat].item()}", visible=True)
 
 
 # Define GUI
@@ -118,10 +118,10 @@ with gr.Blocks() as pragformer_gui:
                 private = gr.Textbox(label="Private", visible=False)
                 reduction = gr.Textbox(label="Reduction", visible=False)
 
-    submit_btn.click(fn=predict, inputs=code_in, outputs=[label_out, confidence_out])
-    submit_btn.click(fn=is_private, inputs=code_in, outputs=private)
-    submit_btn.click(fn=is_reduction, inputs=code_in, outputs=reduction)
-    sample_btn.click(fn=fill_code, inputs=drop, outputs=[pragma, code_in])
+    # submit_btn.click(fn=predict, inputs=code_in, outputs=[label_out, confidence_out])
+    # submit_btn.click(fn=is_private, inputs=code_in, outputs=private)
+    # submit_btn.click(fn=is_reduction, inputs=code_in, outputs=reduction)
+    # sample_btn.click(fn=fill_code, inputs=drop, outputs=[pragma, code_in])
 
 
 # pragformer_gui.launch()