Create app.py

app.py

@@ -0,0 +1,99 @@
+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()
+