File size: 1,727 Bytes
65d5ebe 0ebe852 5968a97 0ebe852 65d5ebe 5968a97 0ebe852 |
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 42 43 44 45 46 47 48 49 50 51 52 53 54 |
import os
import subprocess
def install(package):
subprocess.check_call([os.sys.executable, "-m", "pip", "install", package])
install("transformers")
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import spaces
# Dictionary to store loaded models and tokenizers
loaded_models = {}
def load_model(model_name):
"""Load the model and tokenizer if not already loaded."""
if model_name not in loaded_models:
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name, torch_dtype=torch.float16, device_map="auto"
)
loaded_models[model_name] = (tokenizer, model)
return loaded_models[model_name]
@spaces.GPU
def generate_text(model_name, prompt):
"""Generate text using the selected model."""
tokenizer, model = load_model(model_name)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
# List of models to choose from
model_choices = [
"deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
"meta-llama/Llama-3.2-3B-Instruct",
"google/gemma-7b"
]
# Gradio interface setup
with gr.Blocks() as demo:
gr.Markdown("## Clinical Text Analysis with Multiple Models")
model_selector = gr.Dropdown(choices=model_choices, label="Select Model")
input_text = gr.Textbox(label="Input Clinical Text")
output_text = gr.Textbox(label="Generated Output")
analyze_button = gr.Button("Analyze")
analyze_button.click(fn=generate_text, inputs=[model_selector, input_text], outputs=output_text)
demo.launch()
|