Spaces:
Runtime error
Runtime error
File size: 3,152 Bytes
1277820 d062874 b542780 03c2a65 b542780 7489434 b542780 fcd755f b542780 |
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 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 |
#pip install transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, HfArgumentParser,TrainingArguments,pipeline, logging, TextStreamer, MistralForCausalLM
from peft import LoraConfig, PeftModel, prepare_model_for_kbit_training, get_peft_model,AutoPeftModelForCausalLM
import os,torch, platform, warnings
from datasets import load_dataset
from trl import SFTTrainer
from huggingface_hub import notebook_login
import fire
import streamlit as st
#git clone https://huggingface.co/spaces/J4Lee/RadiantScriptor
st.set_page_config(page_title= "Reports generation from Radiological Image ")
@st.cache(allow_output_mutation=True)
def get_model():
#device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("MariamAde/Mistral_finetuned_base2")
tokenizer = AutoTokenizer.from_pretrained("MariamAde/Mistral_finetuned_base2")
return tokenizer, model
tokenizer, model = get_model()
def generate_report(labels): #,model,tokenizer):
# Tokenize the input labels
inputs = tokenizer(labels, return_tensors="pt") #.to(device)
#model.to(device)
# Generate output using the model
output = model.generate(**inputs)
# Decode the output sentences
sentences = tokenizer.decode(output[0], skip_special_tokens=True)
return sentences
# Streamlit interface
st.title("Radiology Report Generator")
# User input for finding labels
labels = st.text_input("Enter Finding Labels:")
if st.button("Generate Report"):
# Generate the radiology report
report = generate_report(labels) #,model,tokenizer)
# Display the report
st.text_area("Generated Report:", value=report, height=300)
# option 1) Mistral Usage tip
# @st.cache(allow_output_mutation=True)
# def get_model():
# #device = "cuda" # the device to load the model onto
# model = AutoModelForCausalLM.from_pretrained("MariamAde/Mistral_finetuned_v2")
# tokenizer = AutoTokenizer.from_pretrained("MariamAde/Mistral_finetuned_v2")
# return tokenizer, model
# option 2)
# @st.cache(allow_output_mutation=True)
# def get_model():
# tokenizer = LlamaTokenizer.from_pretrained("J4Lee/Medalpaca_finetuned_test")
# model = MistralForCausalLM.from_pretrained("J4Lee/Medalpaca_finetuned_test")
# return tokenizer, model
# option 3)
# @st.cache(allow_output_mutation=True)
# def get_model():
# base_model, new_model = "mistralai/Mistral-7B-v0.1" , "inferenceanalytics/radmistral_7b"
# base_model_reload = AutoModelForCausalLM.from_pretrained(
# base_model, low_cpu_mem_usage=True,
# return_dict=True,torch_dtype=torch.bfloat16,
# device_map= 'auto')
# model = PeftModel.from_pretrained(base_model_reload, new_model)
# model = merged_model.merge_and_unload()
# # Reload tokenizer
# tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)
# tokenizer.pad_token = tokenizer.eos_token
# tokenizer.padding_side = "right"
# return tokenizer, model
# DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
# DEVICE |