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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModel
# Load Med42
med42_model_name = "m42-health/med42-70b"
med42_tokenizer = AutoTokenizer.from_pretrained(med42_model_name)
med42_model = AutoModelForCausalLM.from_pretrained(med42_model_name)
# Load ClinicalBERT
clinicalbert_model_name = "medicalai/ClinicalBERT"
clinicalbert_tokenizer = AutoTokenizer.from_pretrained(clinicalbert_model_name)
clinicalbert_model = AutoModel.from_pretrained(clinicalbert_model_name)
# Define functions
def med42_qa(question):
inputs = med42_tokenizer(question, return_tensors="pt")
outputs = med42_model.generate(**inputs, max_length=200)
return med42_tokenizer.decode(outputs[0], skip_special_tokens=True)
def analyze_ehr(text):
inputs = clinicalbert_tokenizer(text, return_tensors="pt")
embeddings = clinicalbert_model(**inputs).last_hidden_state
return f"ClinicalBERT generated embeddings with shape: {embeddings.shape}"
# Combine Gradio Interface
def combined_tool(input_text):
qa_result = med42_qa(input_text)
ehr_result = analyze_ehr(input_text)
return f"Med42 Answer:\n{qa_result}\n\nClinicalBERT Analysis:\n{ehr_result}"
# Build Gradio UI
interface = gr.Interface(
fn=combined_tool,
inputs="text",
outputs="text",
title="Healthcare AI Tool",
description="Use Med42 for medical Q&A and ClinicalBERT for EHR analysis."
)
if __name__ == "__main__":
interface.launch()
import os
from transformers import AutoTokenizer, AutoModelForCausalLM
# Load authentication token from environment variables
hf_token = os.getenv("HF_AUTH_TOKEN")
# Load the Med42 model with the token
med42_model_name = "m42-health/med42-70b"
med42_tokenizer = AutoTokenizer.from_pretrained(med42_model_name, use_auth_token=hf_token)
med42_model = AutoModelForCausalLM.from_pretrained(med42_model_name, use_auth_token=hf_token)
import os
from transformers import AutoTokenizer, AutoModelForCausalLM
# Load the token from the environment
hf_token = os.getenv("HF_AUTH_TOKEN")
# Load Med42 model and tokenizer
med42_model_name = "m42-health/med42-70b"
med42_tokenizer = AutoTokenizer.from_pretrained(med42_model_name, use_auth_token=hf_token)
med42_model = AutoModelForCausalLM.from_pretrained(med42_model_name, use_auth_token=hf_token)
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