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app.py

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+import gradio as gr
+import argparse, torch, gc, os, random, json
+from data import device
+import numpy as np
+from data import MyDataset, load_data, my_collate_fn, device
+import re
+def clean_str(string,use=True):
+    """
+    Tokenization/string cleaning for all datasets except for SST.
+    Original taken from https://github.com/yoonkim/CNN_sentence/blob/master/process_data.py
+    """
+    if not use: return string
+
+    string = re.sub(r"[^A-Za-z0-9(),!?\'\`]", " ", string)
+    string = re.sub(r"\'s", " \'s", string)
+    string = re.sub(r"\'ve", " \'ve", string)
+    string = re.sub(r"n\'t", " n\'t", string)
+    string = re.sub(r"\'re", " \'re", string)
+    string = re.sub(r"\'d", " \'d", string)
+    string = re.sub(r"\'ll", " \'ll", string)
+    string = re.sub(r",", " , ", string)
+    string = re.sub(r"!", " ! ", string)
+    string = re.sub(r"\(", " \( ", string)
+    string = re.sub(r"\)", " \) ", string)
+    string = re.sub(r"\?", " \? ", string)
+    string = re.sub(r"\s{2,}", " ", string)
+    return string.strip().lower()
+
+title_list = np.load("./title_list.npy", allow_pickle=True).tolist()
+data_path = os.path.join('..', 'data')
+device = 'cpu'
+vec_inuse = json.load(open('/Users/sauron/Desktop/Code/Finding-NLP-Papers/finding_papers_gradio/data/papers_embedding_inuse.json'))
+vocab = list(vec_inuse)
+vocab_size = len(vocab) + 2
+word2index = dict()
+index2word = list()
+word2index['<PAD>'] = 0
+word2index['<OOV>'] = 1
+index2word.extend(['<PAD>', '<OOV>'])
+index2word.extend(vocab)
+word2vec = np.zeros((vocab_size, len(list(vec_inuse.values())[0])), dtype=np.float32)
+for wd in vocab:
+    index = len(word2index)
+    word2index[wd] = index
+    word2vec[index, :] = vec_inuse[wd]
+
+def data2index(data_x, word2index):
+    data_x_idx = list()
+    for instance in data_x:
+        def_word_idx = list()
+        def_words = clean_str(instance['question']).strip().split()
+
+        for def_word in def_words:
+            if def_word in word2index and def_word!=instance['answer']:
+                def_word_idx.append(word2index[def_word])
+            else:
+                def_word_idx.append(word2index['<OOV>'])
+        data_x_idx.append({'answer': word2index[instance['answer']], 'question_words': def_word_idx})
+
+    return data_x_idx
+
+
+def greet(paper_str):
+    pred_list = []
+
+    model = torch.load("saved.model",map_location = torch.device('cpu'))
+    model.eval()
+
+
+    test_dataset = MyDataset(data2index(
+        [
+            {
+            'answer':'p-7241',
+            'question': paper_str
+            }
+        ], word2index))
+    test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=1, shuffle=False, collate_fn=my_collate_fn)
+
+    for words_t, definition_words_t in test_dataloader:
+        indices = model('test', x=definition_words_t, w=words_t, mode="b")
+        predicted = indices[:, :10].detach().cpu().numpy().tolist()
+    predicted = [index2word[paper] for paper in predicted[0]]
+
+    del pred_list
+    gc.collect()
+
+    papers_output = []
+    for paper_i in predicted:
+        paper_i = int(paper_i.split('-')[1])
+        papers_output.append(title_list[paper_i])
+    return papers_output
+    
+    
+with gr.Blocks() as demo:
+    name = gr.Textbox(label="Question")
+    output = gr.Textbox(label="Papers")
+    greet_btn = gr.Button("Submit")
+    greet_btn.click(fn=greet, inputs=name, outputs=output)
+
+demo.launch()