Update app.py

app.py

@@ -4,5 +4,40 @@ import numpy as np
 import re
 import torch
 
-tokenizer = BertTokenizer.from_pretrained(r'D:\EmergencyManagementSystem\venv\Dissertation\text\transformers_bert\models\checkpoint-2000')
-model = BertForSequenceClassification.from_pretrained(r'D:\EmergencyManagementSystem\venv\Dissertation\text\transformers_bert\models\checkpoint-2000', num_labels = 6)
+tokenizer = BertTokenizer.from_pretrained(r'morror_art_test/model')
+model = BertForSequenceClassification.from_pretrained(r'morror_art_test/model', num_labels = 6)
+
+def preprocess(temp):
+    temp = re.sub(u"\n\n", "\n", temp)
+    temp = re.sub(u"(^\n)|(\n$)", "", temp)
+    temp = re.sub('[^\u4e00-\u9fa5，。？！\n]+', '', temp)
+    temp = re.sub(u"\n", "，", temp)
+    for _ in range(int(len(temp) / 2)):
+        temp = re.sub(u"，，|！！|？？|。。", "，", temp)
+        temp = re.sub(u"，！|！，", "！", temp)
+        temp = re.sub(u"，？|？，", "？", temp)
+        temp = re.sub(u"，。|。，", "。", temp)
+    # time.sleep(1)
+    temp = temp.strip('，')
+
+    return temp
+
+def classify_text(inp):
+    inp = preprocess(inp)
+    print(inp)
+    with torch.no_grad():
+        logits = model(**inputs).logits
+    print(logits)
+    logits = torch.nn.Softmax(dim=0)(logits)
+    print(logits)
+    return {labels[i]: float(logits[i].item()) for i in range(len(labels))}
+
+gr.Interface(
+    classify_text,
+    # gr.inputs.Image(),
+    gr.inputs.Textbox(),
+    outputs = 'label'
+    # inputs='image',
+    # outputs='label',
+    # examples=[["images/cheetah1.jpg"], ["images/lion.jpg"]],
+).launch(debug=True)