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()