from fastai.vision.all import PILImage
from transformers import AutoTokenizer, GPT2LMHeadModel
import streamlit as st

def preprocess_image(file):
    """
    Preprocess image file into an image format to be used by machine learning models
    """
    img = PILImage.create(file)
    img.resize((288,288))
    return img

def get_context(model, img):
    """
    Gets context of given image with given image-to-text model
    """
    # Do Image to Text
    text_from_image = model(img)
    # Extract results
    string_result = ''.join(map(str,text_from_image))
    string_result = string_result[19:]
    return string_result

def precheck(img_to_text_result):
    """
    Returns true if the given image to text results are about dogs or puppies
    """
    result = img_to_text_result.lower()
    return result.find('handwriting') != -1 or result.find('writing') != -1 or result.find('book')

def emotion(model,input):#precheck emotion with text classifier nlp
    output = model(input)
    return output

def handle(model , prompt):
    generate = model(prompt)
    return generate