Update app.py

app.py

@@ -15,6 +15,8 @@ MEALDB_BASE_URL = os.getenv('MEALDB_BASE_URL')
 headers = {"Authorization": f"Bearer {HF_API_KEY}"}
 youtube = build('youtube', 'v3', developerKey=YOUTUBE_DATA_API)
 
+
+
 # Function to convert image to base64
 def image_to_base64(image):
     buffered = io.BytesIO()
@@ -27,12 +29,9 @@ def perform_inference(image):
     image.save(buffered, format="JPEG")
     data = buffered.getvalue()
     model_url = "https://api-inference.huggingface.co/models/juliensimon/autotrain-food101-1471154053"
-    try:
-        response = requests.post(model_url, headers=headers, data=data)
-        result = response.json()
-        return result[0]['label']
-    except Exception as e:
-        return f"Error performing inference: {str(e)}"
+    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):
@@ -42,49 +41,63 @@ def search_youtube_videos(query):
         'maxResults': 5,
         'type': 'video',
     }
-    try:
-        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]
-    except Exception as e:
-        return [f"Error searching YouTube: {str(e)}"]
+    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):
-    try:
-        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}"
-        else:
-            return "Recipe details not found."
-    except Exception as e:
-        return f"Error fetching recipe details: {str(e)}"
+    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)
-    return dish_name, youtube_links, recipe_details
+    # 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.Textbox(label="YouTube Recipe Videos"),
+        gr.HTML(label="YouTube Recipe Videos"),  # Changed to gr.HTML
         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."
 )
 
+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()
+
+
 if __name__ == "__main__":
     iface.launch()