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(
    "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;
}
"""
with gr.Blocks(title="SpecX", css=css, theme=gr.themes.Soft()) as demo:
  with gr.Row():
    textmd = gr.Markdown('''
    <div style="margin: 50px 0;"></div>  

    <h1 style="width:100%; text-align: center;">SpecX: Find the Right Specialist For Your Symptoms!</h1>

    ''')
  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()