import gradio as gr import tensorflow as tf from transformers import TFAutoModel, AutoTokenizer import os import numpy as np model_name = 'cardiffnlp/twitter-roberta-base-sentiment-latest' tokenizer = AutoTokenizer.from_pretrained(model_name) model = tf.keras.models.load_model( "/content/SpecX2/model.h5", custom_objects={ 'TFRobertaModel': TFAutoModel.from_pretrained(model_name) } ) labels = [ 'Cardiologist', 'Dermatologist', 'ENT Specialist', 'Gastro-enterologist', 'General-Physicians', 'Neurologist/Gastro-enterologist', 'Ophthalmologist', 'Orthopedist', 'Psychiatrist', 'Respirologist', 'Rheumatologist', 'Rheumatologist/Gastro-enterologist', 'Rheumatologist/Orthopedist', 'Surgeon' ] seq_len = 152 def prep_data(text): tokens = tokenizer( text, max_length=seq_len, truncation=True, padding='max_length', add_special_tokens=True, return_tensors='tf' ) return { 'input_ids': tokens['input_ids'], 'attention_mask': tokens['attention_mask'] } def inference(text): encoded_text = prep_data(text) probs = model.predict_on_batch(encoded_text) probabilities = {i:j for i,j in zip(labels, list(probs.flatten()))} return probabilities css = """ textarea { background-color: #00000000; border: 1px solid #6366f160; color: #000000; } """ with gr.Blocks(title="SpecX", css=css, theme=gr.themes.Soft()) as demo: with gr.Row(): textmd = gr.Markdown('''

SpecX: Find the Right Specialist For Your Symptoms!

''') with gr.Row(): with gr.Column(scale=1, min_width=600): text_box = gr.Textbox(label="Explain your problem in one sentence.") submit_btn = gr.Button("Submit", elem_id="warningk", variant='primary') examples = gr.Examples(examples=[ "When I remember her I feel down", "The area around my heart doesn't feel good.", "I have a split on my thumb that will not heal." ], inputs=text_box) label = gr.Label(num_top_classes=4, label="Recommended Specialist") submit_btn.click(inference, inputs=text_box, outputs=label) demo.launch()