app.py

# https://stackoverflow.com/questions/50951955/pytesseract-tesseractnotfound-error-tesseract-is-not-installed-or-its-not-i
# https://pysource.com/2020/04/23/text-recognition-ocr-with-tesseract-and-opencv/

import os
import openai
import gradio
import json
from pathlib import Path
from bs4 import BeautifulSoup


openai.api_key = os.getenv("OPENAI_API_KEY")

content_input = "Format the recipe, given the format provided. You must return an HTML:"
html_template = '''
<div itemscope itemtype="https://schema.org/Recipe">
    <span itemprop="name">Mom's World Famous Banana Bread</span>

    <img itemprop="image" src="https://encrypted-tbn0.gstatic.com/banana.jpg" />

    <span itemprop="description">This classic banana bread recipe comes
    from my mom.</span>

    <span itemprop="recipeIngredient">3 or 4 ripe bananas, smashed</span>
    <span itemprop="recipeIngredient">3/4 cup of sugar</span>

    <span itemprop="recipeInstructions">
    1 - Preheat the oven to 350 degrees.
    </span>

    <span itemprop="recipeInstructions">
    2 - Mix in the ingredients in a bowl.
    </span>
</div>
'''
content_input+=html_template

messages = [{"role": "system", "content": content_input}]

# create a static directory to store the static files
static_dir = Path('./static')
static_dir.mkdir(parents=True, exist_ok=True)



def CustomChatGPT(html_explainer, recipe):
    recipe_name_str=""
    ingredients_str=""
    steps_str=""
    import_link_str = ""


    # if image is not None and bool(image):
    #    recipe = pytesseract.image_to_string(Image.open(image))

    if recipe is not None and bool(recipe):
        messages.append({"role": "user", "content": recipe})

        response = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=messages)
        ChatGPT_reply = response["choices"][0]["message"]["content"]

        file_name = "output.html"
        file_path = static_dir / file_name

        soup = BeautifulSoup(ChatGPT_reply, 'html.parser')

        recipe_name = soup.find(attrs={"itemprop": "name"})
        recipe_name_str = recipe_name.text

        ingredients_html = soup.find_all(attrs={"itemprop": "recipeIngredient"})
        ingredients = [item.text.strip() for item in ingredients_html]
        ingredients_str = "\n".join(ingredients)

        steps_html = soup.find_all(attrs={"itemprop": "recipeInstructions"})
        steps = [item.text.strip() for item in steps_html]
        steps_str = "\n".join(steps)

        import_link_str = "<a href='file=static/output.html'>Import</a>"

        with open(file_path, "w") as file:
            file.write(ChatGPT_reply)

    return recipe_name_str, ingredients_str, steps_str, import_link_str

html_explainer = '''
    <h1>This is the Recipe Cleaner:</h1>
    <ol>
    <li>Take a picture of the text of your recipe (from a magazine example)</li>
    <li>Using the text recognition feature of your phone, copy the text</li>
    <li>Paste the text on the field: Recipe Text</li>
    <li>Click on Submit</li>
    <li>If you click import after receiving the results, the recipe can be imported in a compatible format with your favorite recipe app's browser extension.</li>
    </ol>
    '''


gradio_input_html_explainer= gradio.HTML(html_explainer)
gradio_txt_input_recipe_content = gradio.Textbox(label="Recipe Text", lines=2, placeholder="Add here the text of the picture of your recipe...")
gradio_input_image = gradio.Image(shape=(400, 300),type="filepath", label="Recipe Image")


gradio_txt_output_recipe_name = gradio.Textbox(label="Recipe Name", lines=1, placeholder="Recipe Name...")
gradio_txt_output_ingredients = gradio.Textbox(label="Ingredients", lines=2, placeholder="Ingredients...")
gradio_txt_output_steps = gradio.Textbox(label="Preparation Steps", lines=2, placeholder="Steps...")

demo = gradio.Interface(
    fn=CustomChatGPT,
    inputs=[gradio_input_html_explainer, gradio_txt_input_recipe_content],
    outputs=[gradio_txt_output_recipe_name, gradio_txt_output_ingredients,gradio_txt_output_steps,gradio.HTML()],
    title="Recipe Cleaner",
    allow_flagging="never"
)

demo.launch()
# demo.launch(share=True)