change app dtemo

app.py

@@ -1,22 +1,52 @@
 import gradio as gr
 import spaces
+import os
+import time
 import torch
+from config import Config
+from transformers import BertConfig, BertTokenizer, BertForSequenceClassification
 
-zero = torch.Tensor([0]).cuda()
-print(zero.device) # <-- 'cpu' 🤔
 
-testgpu= torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-print(testgpu) 
 
-@spaces.GPU
-def greet(n):
-    print(zero.device) # <-- 'cuda:0' 🤗
-    return f"Hello {zero + n} Tensor"
-
-#demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+def set_seed(seed):
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    torch.backends.cudnn.deterministic = True
 
-demo = gr.Interface(fn=greet, inputs=gr.Number(), outputs=gr.Text())
+@spaces.GPU
+def greet(inputStr):
+    set_seed(1)
+    config = Config("./data_12345")
+    
+    tokenizer = BertTokenizer.from_pretrained("bert-base-chinese")
+    bert_config = BertConfig.from_pretrained("bert-base-chinese", num_labels=config.num_labels)
+    model = BertForSequenceClassification.from_pretrained("bert-base-chinese",
+        config=bert_config
+    )
+    model.to(config.device)
+
+
+    model.load_state_dict(torch.load(config.saved_model))
+    model.eval()
+    inputs = tokenizer(
+        inputStr, 
+        max_length=config.max_seq_len,
+        truncation="longest_first",
+        return_tensors="pt")
+    inputs = inputs.to(config.device)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs[0]
+        label = torch.max(logits.data, 1)[1].tolist()
+        print("Classification result:" + config.label_list[label[0]])
+        return config.label_list[label[0]]
+
+    
+
+demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+#demo = gr.Interface(fn=greet, inputs=gr.Number(), outputs=gr.Text())
 demo.launch()