| import spaces |
| import gradio as gr |
| import torch |
| from transformers import AutoTokenizer, AutoModelForCausalLM |
| from peft import PeftModel |
|
|
| base_model_id = "unsloth/llama-3-8b-Instruct-bnb-4bit" |
| adapter_model_id = "mutij8/academic-humanizer-llama3" |
|
|
| |
| tokenizer = None |
| model = None |
|
|
| def load_model_once(): |
| global tokenizer, model |
| if model is not None: |
| return tokenizer, model |
|
|
| print("⏳ ZeroGPU allocated! Loading model into VRAM...") |
| tokenizer = AutoTokenizer.from_pretrained(adapter_model_id) |
| |
| base_model = AutoModelForCausalLM.from_pretrained( |
| base_model_id, |
| torch_dtype=torch.float16, |
| device_map="cuda" |
| ) |
| model = PeftModel.from_pretrained(base_model, adapter_model_id) |
| print("✅ Model loaded successfully on GPU!") |
| return tokenizer, model |
|
|
| @spaces.GPU |
| def humanize_paragraph(paragraph): |
| paragraph = paragraph.strip() |
| if not paragraph: |
| return "Please enter a valid academic paragraph." |
|
|
| tok, mod = load_model_once() |
| word_count = len(paragraph.split()) |
|
|
| sft_instruction = f'''You are a human researcher hastily writing a draft for an Essay. |
| - Tone: Write with natural human imperfection, varied sentence lengths (mixing very short and long sentences), and stylistic asymmetry. |
| - Rule 1: Output MUST be between {int(word_count*0.85)} and {int(word_count*1.15)} words. |
| - Rule 2: Force clause inversion. Actively rewrite the paragraph structures by shifting the order of clauses (e.g., start with the consequence, context, or ending arguments rather than the original opening clauses). |
| - Rule 3: Use highly varied sentence structures and alternative vocabulary choices to avoid contiguous 3-to-5 word matches with the source, preventing plagiarism detection. |
| - Rule 4: Keep all inline citations (e.g. [1] or (Smith, 2023)) 100% intact. |
| - Rule 5: Maintain the precise narrative tense of the original text (do not change past tense to present or present tense to past). |
| - Rule 6: Output ONLY the rewritten text.''' |
|
|
| messages = [ |
| {"role": "system", "content": sft_instruction}, |
| {"role": "user", "content": paragraph} |
| ] |
|
|
| prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
| inputs = tok([prompt], return_tensors="pt").to("cuda") |
|
|
| outputs = mod.generate( |
| **inputs, |
| max_new_tokens=int(word_count * 1.5) + 30, |
| do_sample=True, |
| temperature=0.85, |
| top_p=0.95, |
| top_k=50 |
| ) |
|
|
| input_len = inputs["input_ids"].shape[1] |
| generated_text = tok.decode(outputs[0][input_len:], skip_special_tokens=True).strip() |
| return generated_text |
|
|
| interface = gr.Interface( |
| fn=humanize_paragraph, |
| inputs=gr.Textbox(lines=10, placeholder="Enter AI-style academic paragraph here..."), |
| outputs=gr.Textbox(lines=10, label="Humanized Paragraph (Low Plagiarism & Low AI Score)"), |
| title="Turnitin-Calibrated Essay Humanizer", |
| description="Input a paragraph to rewrite it using humanized structures, clause inversion, and exact tense retention." |
| ) |
|
|
| if __name__ == "__main__": |
| interface.launch() |
