import streamlit as st
import torch
from transformers import AutoProcessor, VisionEncoderDecoderModel
import requests
from PIL import Image
import pandas as pd

st.write("American Dental Association (ADA) claims")
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
processor = AutoProcessor.from_pretrained("Laskari-Naveen/ADA_005")
model = VisionEncoderDecoderModel.from_pretrained("Laskari-Naveen/ADA_005").to(device)


def run_prediction(image, model, processor):
    pixel_values = processor(image, return_tensors="pt").pixel_values
    task_prompt = "<s>"
    decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids
    outputs = model.generate(
        pixel_values.to(device),
        decoder_input_ids=decoder_input_ids.to(device),
        max_length=model.decoder.config.max_position_embeddings,
        early_stopping=True,
        pad_token_id=processor.tokenizer.pad_token_id,
        eos_token_id=processor.tokenizer.eos_token_id,
        use_cache=True,
        num_beams=2,
        bad_words_ids=[[processor.tokenizer.unk_token_id]],
        return_dict_in_generate=True,
    )
    # process output
    prediction = processor.batch_decode(outputs.sequences)[0]
    prediction = processor.token2json(prediction)
    return prediction, outputs

def split_and_expand(row):
    if row['Key'] == "33_Missing_Teeth":
        keys = [row['Key']]
        values = row['Value'].split(';')[0]
    else:
        keys = [row['Key']] * len(row['Value'].split(';'))
        values = row['Value'].split(';')
    return pd.DataFrame({'Key': keys, 'Value': values})


uploaded_file = st.file_uploader("Choose a file")
if uploaded_file is not None:
    content = uploaded_file.read()
    st.image(uploaded_file)
    image = Image.open(uploaded_file).convert("RGB")
    prediction, output = run_prediction(image, model, processor)

    st.dataframe(prediction, width=600)