import os # Clone the repository os.system("git clone https://github.com/TimDettmers/bitsandbytes.git") os.system("cd bitsandbytes/ && pip install -r requirements-dev.txt && cmake -DCOMPUTE_BACKEND=cuda -S . && make && pip install .") # Check if GPU is available import torch device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"Using device: {device}") if device.type == "cuda": bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.float16, ) model = AutoModelForCausalLM.from_pretrained( model_name, quantization_config=bnb_config, trust_remote_code=True, use_cache=False, device_map=device_map ) else: model = AutoModelForCausalLM.from_pretrained(model_name) # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) tokenizer.pad_token = tokenizer.eos_token def askme(symptoms, question): sys_message = ''' You are an AI Medical Assistant trained on a vast dataset of health information. Please be thorough and provide an informative answer. If you don't know the answer to a specific medical inquiry, advise seeking professional help. ''' content = symptoms + " " + question messages = [{"role": "system", "content": sys_message}, {"role": "user", "content": content}] prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = tokenizer(prompt, return_tensors="pt").to(device) outputs = model.generate(**inputs, max_new_tokens=200, use_cache=True) response_text = tokenizer.batch_decode(outputs)[0].strip() answer = response_text.split('<|im_start|>assistant')[-1].strip() return answer # Example usage symptoms = ''' I'm a 35-year-old male and for the past few months, I've been experiencing fatigue, increased sensitivity to cold, and dry, itchy skin. ''' question = ''' Could these symptoms be related to hypothyroidism? If so, what steps should I take to get a proper diagnosis and discuss treatment options? ''' examples = [ [symptoms, question] ] iface = gr.Interface( fn=askme, inputs=["text", "text"], outputs="text", examples=examples, title="Medical AI Chatbot", description="Ask me a medical question!" ) iface.launch()