Upload app.py

app.py

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+import os
+import sys
+sys.path.insert(0, os.path.abspath('./'))
+
+import torch
+from tqdm.auto import tqdm
+from torch.utils.data import DataLoader, random_split
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+from event_detection_dataset import *
+from event_detection_model import *
+
+import gradio as gr
+#print(f"Gradio version: {gr.__version__}")
+
+
+def predict(data):
+    data=[data]
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    #print(f"Device {device}")    
+    
+    
+    """Load Tokenizer"""  
+    tokenizer = AutoTokenizer.from_pretrained('distilbert-base-cased', use_fast=True)
+    
+    
+    """Tokenized Inputs"""
+    tokenized_inputs = tokenizer(
+        data,                          
+        add_special_tokens=True,
+        max_length=512,
+        padding='max_length',
+        return_token_type_ids=True,
+        truncation=True,
+        is_split_into_words=True
+    )
+    
+    
+    """Load Model"""
+    model_path = "./"
+    #print("model_path:", model_path)
+
+    #print("================ load model ===========================")
+    model = DistillBERTClass('distilbert-base-cased')
+    
+    #print("================  model init ===========================")
+    pretrained_model=torch.load(model_path + "/event_domain_final.pt",map_location=torch.device('cpu'))
+    model.load_state_dict(pretrained_model['model_state_dict'])
+    model.to(device)
+
+    
+    """Make Prediction"""
+    model.eval()
+    
+    ids = torch.tensor(tokenized_inputs['input_ids']).to(device)
+    mask = torch.tensor(tokenized_inputs['attention_mask']).to(device)
+
+    with torch.no_grad():
+        outputs = model(ids, mask)
+
+    max_val, max_idx = torch.max(outputs.data, dim=1)
+    
+    #print("=============== inference result =================")
+    #print(f"predicted class {max_idx}")
+    id2tags={0: "Acquisition",1: "I-Positive Clinical Trial & FDA Approval",2: "Dividend Cut",3: "Dividend Increase",4: "Guidance Increase",5: "New Contract",6: "Dividend",7: "Reverse Stock Split",8: "Special Dividend ",9: "Stock Repurchase",10: "Stock Split",11: "Others"}
+    return id2tags[max_idx.item()]
+
+
+title="Financial Event Detection"
+description="Predict Finacial Events."
+article="modified the model in the following paper: Zhou, Z., Ma, L., & Liu, H. (2021)."
+example_list=[["Investors who receive dividends can choose to take them as cash or as additional shares."]]
+
+# Create the Gradio demo
+demo = gr.Interface(fn=predict, # mapping function from input to output
+                    inputs="text", # what are the inputs?
+                    outputs="text", # our fn has two outputs, therefore we have two outputs
+                    examples=example_list, 
+                    title=title,
+                    description=description,
+                    article=article)
+
+# Launch the demo!
+demo.launch(debug=False, share=True)