app.py

from transformers import T5ForConditionalGeneration, AutoTokenizer
import gradio as gr

# Load the model and tokenizer
model_name = "ejschwartz/hext5"  # Replace with your desired model
model = T5ForConditionalGeneration.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

max_input_length = 4096

examples = [
   "void __fastcall __noreturn <func>(int a1, int a2, char a3, __int64 a4, __int64 a5)\n{\n  __int64 v5; // rdi\n  int v6; // ebx\n  const char *v9; // rsi\n  char *v10; // r12\n  char *v11; // r13\n  char *v12; // rax\n  char v13[42]; // [rsp+Eh] [rbp-2Ah] BYREF\n\n  v5 = (unsigned int)(a1 - 1);\n  v6 = status;\n  if ( (unsigned int)v5 <= 3 )\n  {\n    v9 = (&off_413A60)[v5];\n    if ( a2 < 0 )\n    {\n      v13[0] = a3;\n      v11 = v13;\n      v10 = &asc_412691[-a2];\n      v13[1] = 0;\n    }\n    else\n    {\n      v10 = \"--\";\n      v11 = *(char **)(a4 + 32LL * a2);\n    }\n    v12 = dcgettext(0LL, v9, 5);\n    error(v6, 0, v12, v10, v11, a5);\n    abort();\n  }\n  abort();\n}\n"
]

# predict summary
def predict_summary(code):
    global model
    global tokenizer
    input = tokenizer('summarize: '+code, return_tensors='pt', max_length=max_input_length, truncation=True)
    output = model.generate(**input, max_new_tokens=256)[0]
    return tokenizer.decode(output, skip_special_tokens=True)

# predict identifier (func name)
def predict_identifier(code):
    global model
    global tokenizer
    '''
    code should be like: "unsigned __int8 *__cdecl <func>(int *<var_0>,...){ return <func_1>(1);}"
    '''
    input = tokenizer('identifier_predict: '+code, return_tensors='pt', max_length=max_input_length, truncation=True)
    output = model.generate(**input, max_new_tokens=10000)[0]
    return tokenizer.decode(output)

# Create the Gradio interface for predicting identifiers
identifier_iface = gr.Interface(
    fn=predict_identifier, 
    inputs="text", 
    outputs="text",
    title="Predict Identifiers",
    description="Enter a code snippet and see the model generate function identifiers.",
    examples=examples
)

# Create the Gradio interface for predicting summaries
summary_iface = gr.Interface(
    fn=predict_summary, 
    inputs="text", 
    outputs="text",
    title="Predict Summary",
    description="Enter a code snippet and see the model generate a summary.",
    examples=examples
)

# Combine the interfaces into a single tabbed interface
combined_iface = gr.TabbedInterface(
    interface_list=[identifier_iface, summary_iface],
    tab_names=["Predict Identifiers", "Predict Summary"]
)

# Launch the combined interface
combined_iface.launch()