import gradio as gr
#from transformers import AutoModelForSequenceClassification, AutoTokenizer
#from transformers import BertTokenizer, BertLMHeadModel

# Load pre-trained model and tokenizer
#tokenizer = BertTokenizer.from_pretrained('clinicalBERT')
#model = BertLMHeadModel.from_pretrained('clinicalBERT')

from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("medicalai/ClinicalBERT")
model = AutoModel.from_pretrained("medicalai/ClinicalBERT")


# Define a function to generate text using the model
def generate_text(input_text):
    input_ids = tokenizer.encode(input_text, return_tensors='pt')
    output = model.generate(input_ids)
    return tokenizer.decode(output[0], skip_special_tokens=True)

interface = gr.Interface(fn=generate_text, inputs="text", outputs="text")

interface.launch()