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| import gradio as gr | |
| import torch | |
| from minicons import cwe | |
| import pandas as pd | |
| import numpy as np | |
| from model import FeatureNormPredictor | |
| def predict (Word, Sentence, LM, Layer, Norm): | |
| if Word not in Sentence: return "invalid input: word not in sentence" | |
| model_name = LM + str(Layer) + '_to_' + Norm | |
| lm = cwe.CWE('bert-base-uncased') | |
| if Layer not in range (lm.layers): return "invalid input: layer not in lm" | |
| labels = "These are some fake features".split(" ") | |
| vals = np.random.randint(-10,10,(5)) | |
| return model_name+" \n"+"\n".join([labels[i]+" "+str(vals[i]) for i in range(len(labels)) if vals[i]>0]) | |
| # model = FeatureNormPredictor.load_from_checkpoint( | |
| # checkpoint_path=model_name+'.ckpt', | |
| # map_location=None | |
| # ) | |
| # model.eval() | |
| # with open (model_name+'.txt', "r") as file: | |
| # labels = [line.rstrip() for line in file.readlines()] | |
| # data = (Word, Sentence) | |
| # embs = lm.extract_representation(data, layer=8) | |
| # avg = embs.sum(0)/len(data) | |
| # pred = torch.nn.functional.relu(model(avg)) | |
| # pred = pred.squeeze(0) | |
| # pred_list = pred.detach().numpy().tolist() | |
| # output = [labels[i]+'\t'+str(pred_list[i]) for i in range(len(labels)) if pred_list[i] > 0.0] | |
| # return "All Positive Predicted Values:\n"+"\n".join(output) | |
| demo = gr.Interface( | |
| fn=predict, | |
| inputs=[ | |
| "text", | |
| "text", | |
| gr.Radio(["bert", "albert"]), | |
| "number", | |
| gr.Radio(["binder", "mcrae", "buchanan"]), | |
| ], | |
| outputs=["text"], | |
| ) | |
| demo.launch() |