Update app.py

app.py

@@ -6,6 +6,41 @@ model_name = "ejschwartz/hext5"  # Replace with your desired model
 model = T5ForConditionalGeneration.from_pretrained(model)
 tokenizer = AutoTokenizer.from_pretrained(model)
 
+examples = [
+    """void __fastcall __noreturn <func>(int a1, int a2, char a3, __int64 a4, __int64 a5)
+{
+  __int64 v5; // rdi
+  int v6; // ebx
+  const char *v9; // rsi
+  char *v10; // r12
+  char *v11; // r13
+  char *v12; // rax
+  char v13[42]; // [rsp+Eh] [rbp-2Ah] BYREF
+
+  v5 = (unsigned int)(a1 - 1);
+  v6 = status;
+  if ( (unsigned int)v5 <= 3 )
+  {
+    v9 = (&off_413A60)[v5];
+    if ( a2 < 0 )
+    {
+      v13[0] = a3;
+      v11 = v13;
+      v10 = &asc_412691[-a2];
+      v13[1] = 0;
+    }
+    else
+    {
+      v10 = "--";
+      v11 = *(char **)(a4 + 32LL * a2);
+    }
+    v12 = dcgettext(0LL, v9, 5);
+    error(v6, 0, v12, v10, v11, a5);
+    abort();
+  }
+  abort();
+}"""
+]
 
 # predict summary
 def predict_summary(tokenizer,code):
@@ -17,7 +52,7 @@ def predict_summary(tokenizer,code):
 
 # predict identifier (func name)
 def predict_identifier(tokenizer,code):
-    gloabl model
+    global model
     '''
     code should be like: "unsigned __int8 *__cdecl <func>(int *<var_0>,...){ return <func_1>(1);}"
     '''
@@ -31,7 +66,8 @@ iface = gr.Interface(
     inputs="text", 
     outputs="text",
     title="Predict identifiers",
-    description="Enter a prompt and see the model generate text."
+    description="Enter a prompt and see the model generate text.",
+    examples=examples
 )
 
 # Launch the interface