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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 using environment variables for security | |
| headers = {"Authorization": os.getenv('HF_API_KEY')} | |
| YOUTUBE_DATA_API = os.getenv('YOUTUBE_API_KEY') | |
| mealdb_base_url = os.getenv('MEALDB_BASE_URL') | |
| youtube = build('youtube', 'v3', developerKey=YOUTUBE_DATA_API) | |
| # Function to convert image to base64 (unused in this script but defined) | |
| def image_to_base64(image): | |
| buffered = io.BytesIO() | |
| image.save(buffered, format="JPEG") | |
| import base64 | |
| return base64.b64encode(buffered.getvalue()).decode('utf-8') | |
| # Function to perform inference on the image using a 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." | |
| # Function to generate HTML content for embedding videos | |
| def generate_embed_html(video_links): | |
| html_content = "<div>" | |
| for video in video_links: | |
| html_content += f'<iframe width="560" height="315" src="{video}" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>' | |
| html_content += "</div>" | |
| return html_content | |
| # 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) | |
| youtube_html = generate_embed_html(youtube_links) | |
| return dish_name, recipe_details, youtube_html | |
| iface = gr.Interface( | |
| fn=gradio_interface, | |
| inputs=gr.Image(type="pil", label="Upload an Image"), | |
| outputs=[ | |
| gr.Textbox(label="Predicted Dish"), | |
| gr.Textbox(label="Recipe Details"), | |
| gr.HTML(label= "YouTube Recipe Videos") # Changed to gr.HTML | |
| ], | |
| title="Dish Prediction, Recipe Details, and Recipe Videos", | |
| 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() | |