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import gradio as gr
from unsloth import FastLanguageModel
from transformers import AutoTokenizer
# Function to load model and tokenizer
def load_model(hf_token):
try:
# Initialize the model using ZeroGPU (to run on CPU in Hugging Face environment)
model_name = "shukdevdatta123/sql_injection_classifier_DeepSeek_R1_fine_tuned_model"
model, tokenizer = FastLanguageModel.from_pretrained(
model_name=model_name,
load_in_4bit=True,
token=hf_token,
# use_zero=True, # Ensure ZeroGPU usage
)
return model, tokenizer
except Exception as e:
return None, str(e)
# Function to predict SQL injection
def predict_sql_injection(query, hf_token):
model, tokenizer = load_model(hf_token)
if model is None:
return f"Error loading model: {tokenizer}"
# Prepare the model for inference
inference_model = FastLanguageModel.for_inference(model)
prompt = f"### Instruction:\nClassify the following SQL query as normal (0) or an injection attack (1).\n\n### Query:\n{query}\n\n### Classification:\n"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
# Use the inference model for generation
outputs = inference_model.generate(
input_ids=inputs.input_ids,
attention_mask=inputs.attention_mask,
max_new_tokens=1000,
use_cache=True,
)
prediction = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
return prediction.split("### Classification:\n")[-1].strip()
# Gradio UI
def classify_sql_injection(query, hf_token):
if not hf_token:
return "Please enter your Hugging Face token."
if not query:
return "Please enter a SQL query first."
result = predict_sql_injection(query, hf_token)
return f"Prediction: {result}"
# Gradio interface
iface = gr.Interface(
fn=classify_sql_injection,
inputs=[
gr.Textbox(label="SQL Query", placeholder="Enter SQL query here..."),
gr.Textbox(label="Hugging Face Token", type="password")
],
outputs="text",
live=True,
title="SQL Injection Classifier",
description="Enter an SQL query and your Hugging Face token to classify whether the query is a normal SQL query (0) or a SQL injection attack (1)."
)
# Launch the Gradio app
iface.launch()
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