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import streamlit as st | |
import requests | |
# Function to call the Hugging Face model | |
def query_huggingface_model(prompt): | |
API_TOKEN = "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" # Replace with your Hugging Face API token | |
API_URL = "https://api-inference.huggingface.co/models/MariamAde/Mistral_finetuned_Base2" # Replace with your model's API URL | |
headers = {"Authorization": f"Bearer {API_TOKEN}"} | |
response = requests.post(API_URL, headers=headers, json={"inputs": prompt}) | |
if response.status_code == 200: | |
return response.json() | |
else: | |
return {"error": response.text} | |
# Streamlit interface | |
def main(): | |
st.title("My Fine-tuned Model Demo") | |
# User input | |
user_input = st.text_area("Enter your text here", "") | |
# Button to make the prediction | |
if st.button("Predict"): | |
with st.spinner("Predicting..."): | |
response = query_huggingface_model(user_input) | |
if "error" in response: | |
st.error(response["error"]) | |
else: | |
st.success("Prediction Success") | |
st.write(response) # Modify this based on how your model's response is structured | |
if __name__ == "__main__": | |
main() | |
# #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 AutoModelForSequenceClassification | |
# 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 |