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import gradio as gr
import requests
import io
import os
from PIL import Image
from googleapiclient.discovery import build
# API and Model Setup
HF_header = os.getenv('header')
headers = {"Authorization": HF_header}
YOUTUBE_DATA_API = os.getenv('YOUTUBE_API') # replace this with your own YouTube Data API key
youtube = build('youtube', 'v3', developerKey=YOUTUBE_DATA_API)
mealdb_base_url = os.getenv('mealdb_base_api')
# Function to convert image to base64
def image_to_base64(image):
buffered = io.BytesIO()
image.save(buffered, format="JPEG")
return base64.b64encode(buffered.getvalue()).decode('utf-8')
# Function to perform inference on the image using Hugging Face model
def perform_inference(image):
buffered = io.BytesIO()
image.save(buffered, format="JPEG")
data = buffered.getvalue()
model_url = "https://api-inference.huggingface.co/models/juliensimon/autotrain-food101-1471154053"
response = requests.post(model_url, headers=headers, data=data)
result = response.json()
return result[0]['label']
# Function to search YouTube videos based on a query
def search_youtube_videos(query):
search_params = {
'q': query + " recipe",
'part': 'snippet',
'maxResults': 5,
'type': 'video',
}
search_response = youtube.search().list(**search_params).execute()
video_ids = [item['id']['videoId'] for item in search_response['items']]
return [f"https://www.youtube.com/embed/{video_id}" for video_id in video_ids]
# Function to get recipe details from TheMealDB
def get_recipe_details(query):
response = requests.get(f"{mealdb_base_url}search.php?s={query}")
data = response.json()
meals = data.get('meals', [])
if meals:
meal_id = meals[0]['idMeal']
details_response = requests.get(f"{mealdb_base_url}lookup.php?i={meal_id}")
details_data = details_response.json()
recipe = details_data['meals'][0]
ingredients = "\n".join([f"{recipe[f'strIngredient{i}']}: {recipe[f'strMeasure{i}']}" for i in range(1, 21) if recipe[f'strIngredient{i}']])
return f"{recipe['strMeal']} -\n\nSteps:\n{recipe['strInstructions']}\n\nIngredients:\n{ingredients}"
return "Recipe details not found."
# Gradio interface function that handles image uploads and processes the data
def gradio_interface(image):
dish_name = perform_inference(image)
youtube_links = search_youtube_videos(dish_name)
recipe_details = get_recipe_details(dish_name)
# Generate HTML content for embedding videos
youtube_html = generate_embed_html(youtube_links)
return dish_name, youtube_html, recipe_details
iface = gr.Interface(
fn=gradio_interface,
inputs=gr.Image(type="pil", label="Upload an Image"),
outputs=[
gr.Textbox(label="Predicted Dish"),
gr.HTML(label="YouTube Recipe Videos"),
gr.Textbox(label="Recipe Details")
],
title="Dish Prediction, Recipe Videos, and Recipe Details",
description="Upload an image of food, and the app will predict the dish, provide YouTube links for recipes, and fetch detailed recipe instructions."
)
if __name__ == "__main__":
iface.launch()
def gradio_interface(image):
dish_name = perform_inference(image)
return dish_name, ""
def show_recipe(dish_name):
if dish_name:
return get_recipe_details(dish_name)
return "No dish predicted. Please upload an image and predict the dish first.", ""
def show_videos(dish_name):
if dish_name:
video_links = search_youtube_videos(dish_name)
return "", generate_embed_html(video_links)
return "", "No dish predicted. Please upload an image and predict the dish first."
iface = gr.Blocks()
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