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from transformers import pipeline
from PIL import Image
import gradio as gr
import os
import requests
import json
model_name = "larimei/food-classification-ai"
classifier = pipeline("image-classification", model=model_name)
def predict_image(image):
predictions = classifier(image)
meal_name = predictions[0]['label']
recipe_text = getRecipe(meal_name)
meal_info = f"This is {meal_name.replace('_', ' ')}."
return meal_info, recipe_text
def getRecipe(meal):
app_id = "24bf0913"
app_key = "03c60f26520f9d25b0d0617e50993aaa"
field = ["uri", "label", "image", "ingredientLines", "source", "url"]
url = "https://api.edamam.com/api/recipes/v2"
querystring = {
"type": "public",
"q": meal.replace("_", " "),
"app_id": app_id,
"app_key": app_key,
"field": field
}
response = requests.get(url, params=querystring)
json_object = response.json()
returnString = "It is made out of the following ingredients: \n\n"
for line in json_object["hits"][0]["recipe"]["ingredientLines"]:
returnString += line + "\n"
returnString += "\n You can make " + json_object["hits"][0]["recipe"]["label"] + " yourself by following the steps of this instruction: " + json_object["hits"][0]["recipe"]["url"]
return returnString
title = "Recipifier"
description = "Discover the world of recipes effortlessly with Recipifier, using advanced AI trained on the extensive Food-101 dataset. Simply upload a photo of any dish, and our application identifies it accurately, providing detailed recipes and cooking instructions sourced from a vast collection. Perfect for food enthusiasts and home chefs alike, Recipifier makes exploring new culinary creations intuitive and inspiring. Start transforming everyday ingredients into extraordinary meals today!"
example_list = [["examples/" + example] for example in os.listdir("examples")]
css = """
#component-13 {
display: none;
}
"""
demo = gr.Interface(
fn=predict_image,
inputs=gr.Image(type="pil"),
outputs=[
gr.Textbox(label="Meal", elem_id="meal"),
gr.Textbox(label="Recipe")
],
examples=example_list,
title=title,
description=description,
css=css,
flagging_options=None
)
demo.launch()
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