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| import gradio as gr | |
| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig | |
| from peft import PeftModel | |
| print("🚀 Loading AI/ML Tutor with Vicuna...") | |
| # Configure 4-bit quantization to reduce memory usage | |
| bnb_config = BitsAndBytesConfig( | |
| load_in_4bit=True, | |
| bnb_4bit_use_double_quant=True, | |
| bnb_4bit_quant_type="nf4", | |
| bnb_4bit_compute_dtype=torch.float16 | |
| ) | |
| base_model = "TheBloke/vicuna-7B-1.1-HF" | |
| adapter_path = "shahzaib41202/ai-ml-tutor-v1" | |
| try: | |
| # Load tokenizer | |
| tokenizer = AutoTokenizer.from_pretrained(adapter_path) | |
| tokenizer.pad_token = tokenizer.eos_token | |
| # Load base model with 4-bit quantization | |
| model = AutoModelForCausalLM.from_pretrained( | |
| base_model, | |
| quantization_config=bnb_config, | |
| device_map="auto", | |
| trust_remote_code=True | |
| ) | |
| # Load your fine-tuned adapter | |
| model = PeftModel.from_pretrained(model, adapter_path) | |
| print("✅ Vicuna AI Tutor Loaded with 4-bit quantization!") | |
| except Exception as e: | |
| print(f"❌ Failed to load model: {e}") | |
| model = None | |
| tokenizer = None | |
| def ask_ai_tutor(question): | |
| if model is None or tokenizer is None: | |
| return "AI Tutor is currently unavailable. Please try again later." | |
| try: | |
| prompt = f"Question: {question}\nAnswer (from AI ML Tutor created by Shahzaib Shaikh):" | |
| # Tokenize | |
| inputs = tokenizer( | |
| prompt, | |
| return_tensors="pt", | |
| truncation=True, | |
| max_length=256 # Reduced for memory | |
| ) | |
| # Generate with minimal memory usage | |
| with torch.no_grad(): | |
| outputs = model.generate( | |
| **inputs, | |
| max_new_tokens=150, # Shorter responses to save memory | |
| temperature=0.7, | |
| do_sample=True, | |
| top_p=0.9, | |
| repetition_penalty=1.1 | |
| ) | |
| response = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| if "Answer (from AI ML Tutor created by Shahzaib Shaikh):" in response: | |
| answer = response.split("Answer (from AI ML Tutor created by Shahzaib Shaikh):")[-1].strip() | |
| else: | |
| answer = response | |
| return answer | |
| except Exception as e: | |
| return f"❌ Generation error: {str(e)}" | |
| # Create interface | |
| with gr.Blocks(theme=gr.themes.Soft()) as demo: | |
| gr.Markdown("# 🤖 AI/ML Tutor by Shahzaib Shaikh") | |
| gr.Markdown("### Powered by Vicuna-7B (4-bit quantized)") | |
| with gr.Row(): | |
| with gr.Column(): | |
| question = gr.Textbox( | |
| label="Your Question", | |
| placeholder="Ask about Machine Learning, NLP, Random Forest, Decision Trees...", | |
| lines=3 | |
| ) | |
| ask_btn = gr.Button("🚀 Ask AI Tutor", variant="primary") | |
| answer = gr.Textbox( | |
| label="AI Tutor Response", | |
| lines=6, | |
| interactive=False, | |
| show_copy_button=True | |
| ) | |
| clear_btn = gr.Button("🗑️ Clear") | |
| with gr.Column(): | |
| gr.Markdown("### 💡 Quick Examples") | |
| gr.Examples( | |
| examples=[ | |
| "What is Machine Learning?", | |
| "What is NLP?", | |
| "What is Random Forest?", | |
| "What is Decision Tree?" | |
| ], | |
| inputs=question, | |
| label="Click any example" | |
| ) | |
| gr.Markdown("### ℹ️ Note") | |
| gr.Markdown("Using Vicuna-7B with 4-bit quantization for memory efficiency") | |
| def get_answer(q): | |
| if not q.strip(): | |
| return "Please enter a question." | |
| return ask_ai_tutor(q) | |
| def clear_all(): | |
| return "", "" | |
| ask_btn.click(get_answer, question, answer) | |
| question.submit(get_answer, question, answer) | |
| clear_btn.click(clear_all, outputs=[question, answer]) | |
| if __name__ == "__main__": | |
| demo.launch() |