File size: 1,282 Bytes
f2c3f89 ac21e0a f2c3f89 e3867c9 ac21e0a f2c3f89 2cd6d6c f2c3f89 e3867c9 f2c3f89 e3867c9 f2c3f89 e192026 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 |
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Check if CUDA is available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if device.type == "cpu":
print("Warning: CUDA is not available. Running on CPU, which may be slow.")
# Load the tokenizer and model directly
model_name = "ruslanmv/ai-medical-model-32bit"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = model = AutoModelForCausalLM.from_pretrained(
model_name,
device_map="auto",
load_in_8bit=True
).to(device)
# Function to ask medical questions
def ask_medical_question(question):
prompt = f"<|start_header_id|>system<|end_header_id|> You are a Medical AI chatbot assistant. <|eot_id|><|start_header_id|>User: <|end_header_id|>This is the question: {question}<|eot_id|>"
inputs = tokenizer(prompt, return_tensors="pt").to(device)
outputs = model.generate(
**inputs,
max_new_tokens=100,
temperature=0.7,
do_sample=True,
top_p=0.9,
top_k=30,
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
# Set up Gradio interface
iface = gr.Interface(fn=ask_medical_question, inputs="text", outputs="text")
iface.launch()
|