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Browse files- app.py +69 -0
- requirements.txt +8 -0
app.py
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import gradio as gr
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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import torch
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model_name = "ruslanmv/Medical-Llama3-8B"
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device_map = 'auto'
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# Check if GPU is available
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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print(f"Using device: {device}")
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if device.type == "cuda":
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bnb_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_compute_dtype=torch.float16,
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)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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quantization_config=bnb_config,
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trust_remote_code=True,
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use_cache=False,
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device_map=device_map
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)
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else:
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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trust_remote_code=True,
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use_cache=False
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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tokenizer.pad_token = tokenizer.eos_token
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def askme(symptoms, question):
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sys_message = '''
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You are an AI Medical Assistant trained on a vast dataset of health information. Please be thorough and
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provide an informative answer. If you don't know the answer to a specific medical inquiry, advise seeking professional help.
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'''
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content = symptoms + " " + question
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messages = [{"role": "system", "content": sys_message}, {"role": "user", "content": content}]
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(prompt, return_tensors="pt").to(device)
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outputs = model.generate(**inputs, max_new_tokens=200, use_cache=True)
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response_text = tokenizer.batch_decode(outputs)[0].strip()
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answer = response_text.split('<|im_start|>assistant')[-1].strip()
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return answer
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# Example usage
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symptoms = '''
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I'm a 35-year-old male and for the past few months, I've been experiencing fatigue,
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increased sensitivity to cold, and dry, itchy skin.
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'''
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question = '''
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Could these symptoms be related to hypothyroidism?
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If so, what steps should I take to get a proper diagnosis and discuss treatment options?
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'''
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examples = [
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[symptoms, question]
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]
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iface = gr.Interface(
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fn=askme,
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inputs=["text", "text"],
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outputs="text",
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examples=examples,
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title="Medical AI Chatbot",
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description="Ask me a medical question!"
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)
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iface.launch()
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requirements.txt
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torch==2.2.1
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torchvision
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torchaudio
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xformers
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bitsandbytes
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accelerate
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gradio
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transformers
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