Update demo.py

demo.py

@@ -1,170 +1,166 @@
-import os
-import base64
-from openai import OpenAI
-import gradio as gr
-from dotenv import load_dotenv
-import requests
-
-# Load environment variables from .env file
-load_dotenv()
-
-# Set OpenAI API key
-openai_api_key = os.getenv("OPENAI_API_KEY")
-edamam_app_id = os.getenv("EDAMAM_APP_ID")
-edamam_app_key = os.getenv("EDAMAM_API_KEY")
-
-def encode_image(image_path):
-    try:
-        with open(image_path, "rb") as image_file:
-            return base64.b64encode(image_file.read()).decode('utf-8')
-    except Exception as e:
-        raise ValueError(f"Failed to encode image: {e}")
-
-
-def image_to_ingredient_list(image_path):
-    """
-    Transforms an image of food items/dishes into an ingredient list using GPT-4.
-    
-    Args:
-        image_path (str): The file path to the uploaded image.
-    
-    Returns:
-        str: The generated ingredient list or an error message.
-    """
-    try:
-        # Encode the image to base64
-        base64_image = encode_image(image_path)
-        client = OpenAI(api_key=openai_api_key)
-        # Prepare the message with the base64-encoded image
-        messages = [
-            {
-                "role": "user",
-                "content": [
-                    {
-                        "type": "text",
-                        "text": """Please provide an ingredient list with estimated quantities used to make the following food image.
-If the image is a drink, list the ingredients in the drink with estimated quantities based on the image.
-Only estimate the quantities based on the current image not the entire food. For example if the image is a slice of cake, adjust the quantities to fit a single slice and not a whole cake.
-Be confident in your answer and don't list down ingredients or quantities you are unsure of.
-Only output the ingredients list with estimated quantities and separate each item with a newline.
-Example:
-1 cup mango slices
-1/2 cup plain yogurt
-1 tablespoon peanut butter"""
-                    },
-                    {
-                        "type": "image_url",
-                        "image_url": {
-                            "url":  f"data:image/jpeg;base64,{base64_image}"
-                        },
-                    },
-                ],
-            }
-        ]
-        
-        # Call OpenAI's Chat Completion API with image
-        response = client.chat.completions.create(
-            model="gpt-4o-mini", 
-            messages=messages
-        )
-        print(response.choices[0].message.content)
-        # Extract the response text
-        ingredient_list = response.choices[0].message.content
-        
-        return ingredient_list, display_nutrition(ingredient_list)
-    
-    except Exception as e:
-        return f"Error processing image: {str(e)}", ""
-    
-def get_nutritional_info(ingredient_list, app_id=edamam_app_id, app_key=edamam_app_key):
-    """
-    Fetches nutritional information for a list of ingredients using Edamam's Nutrition Analysis API.
-
-    Parameters:
-    - ingredient_list (str): A string where each ingredient is on a new line.
-    - app_id (str): Edamam application ID.
-    - app_key (str): Edamam application key.
-
-    Returns:
-    - dict: Nutritional information returned by the API.
-    """
-    print(edamam_app_id, edamam_app_key)
-    url = 'https://api.edamam.com/api/nutrition-details'
-    headers = {'Content-Type': 'application/json'}
-    data = {
-        'title': 'Recipe',
-        'ingr': ingredient_list.split('\n')
-    }
-    params = {
-        'app_id': app_id,
-        'app_key': app_key
-    }
-    response = requests.post(url, headers=headers, json=data, params=params)
-    # response.raise_for_status()  # Raise an error for bad status codes
-    return response.json()
-
-import requests
-
-def format_nutrition(ingredients):
-    nutrition_data = get_nutritional_info(ingredients)
-
-    # Extract relevant nutritional information
-    calories = nutrition_data.get('calories', 0)
-    total_nutrients = nutrition_data.get('totalNutrients', {})
-    carbs = total_nutrients.get('CHOCDF', {}).get('quantity', 0)
-    protein = total_nutrients.get('PROCNT', {}).get('quantity', 0)
-    fats = total_nutrients.get('FAT', {}).get('quantity', 0)
-
-    return {
-        'Calories': calories,
-        'Carbs': carbs,
-        'Protein': protein,
-        'Fats': fats
-    }
-
-def display_nutrition(ingredients):
-    nutrition_info = format_nutrition(ingredients)
-    return (
-        f"🔥 Calories: {nutrition_info['Calories']} kcal\n"
-        f"🍞 Carbs: {nutrition_info['Carbs']:.2f} g\n"
-        f"🍗 Protein: {nutrition_info['Protein']:.2f} g\n"
-        f"🥑 Fats: {nutrition_info['Fats']:.2f} g"
-    )
-
-    
-# Define Gradio interface
-iface = gr.Interface(
-    fn=image_to_ingredient_list,
-    inputs=gr.Image(type="filepath", label="Upload Food Image"),
-    outputs=[gr.Textbox(label="Ingredient List"), gr.Textbox(label="Nutritional Information")],
-    title="Food Image to Ingredient List",
-    description="Upload an image containing food items, and the app will generate an ingredient list like '1 cup rice, 10 oz chickpeas'.",
-    examples=[
-        ["sample_images/Picture1.jpg"],
-        ["sample_images/Picture2.jpg"],
-        ["sample_images/Picture3.jpg"],
-        ["sample_images/Picture4.jpg"],
-        ["sample_images/Picture5.jpg"],
-        ["sample_images/Picture6.jpg"],
-        ["sample_images/Picture7.jpg"],
-        ["sample_images/Picture8.jpg"],
-        ["sample_images/Picture9.jpg"],
-        ["sample_images/Picture10.jpg"],
-        ["sample_images/Picture11.jpg"],
-        ["sample_images/Picture12.jpg"],
-        ["sample_images/Picture13.jpg"],
-        ["sample_images/Picture14.jpg"],
-        ["sample_images/Picture15.jpg"],
-        ["sample_images/Picture16.jpg"],
-        ["sample_images/Picture17.jpg"],
-        ["sample_images/Picture18.jpg"],
-        ["sample_images/Picture19.jpg"],
-        ["sample_images/Picture20.jpg"]
-
-    ],
-    allow_flagging="never"
-)
-
-# Launch the app
-if __name__ == "__main__":
-    iface.launch()
+import os
+import base64
+from openai import OpenAI
+import gradio as gr
+import requests
+
+# Set OpenAI API key
+openai_api_key = os.getenv("OPENAI_API_KEY")
+edamam_app_id = os.getenv("EDAMAM_APP_ID")
+edamam_app_key = os.getenv("EDAMAM_API_KEY")
+
+def encode_image(image_path):
+    try:
+        with open(image_path, "rb") as image_file:
+            return base64.b64encode(image_file.read()).decode('utf-8')
+    except Exception as e:
+        raise ValueError(f"Failed to encode image: {e}")
+
+
+def image_to_ingredient_list(image_path):
+    """
+    Transforms an image of food items/dishes into an ingredient list using GPT-4.
+    
+    Args:
+        image_path (str): The file path to the uploaded image.
+    
+    Returns:
+        str: The generated ingredient list or an error message.
+    """
+    try:
+        # Encode the image to base64
+        base64_image = encode_image(image_path)
+        client = OpenAI(api_key=openai_api_key)
+        # Prepare the message with the base64-encoded image
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "text",
+                        "text": """Please provide an ingredient list with estimated quantities used to make the following food image.
+If the image is a drink, list the ingredients in the drink with estimated quantities based on the image.
+Only estimate the quantities based on the current image not the entire food. For example if the image is a slice of cake, adjust the quantities to fit a single slice and not a whole cake.
+Be confident in your answer and don't list down ingredients or quantities you are unsure of.
+Only output the ingredients list with estimated quantities and separate each item with a newline.
+Example:
+1 cup mango slices
+1/2 cup plain yogurt
+1 tablespoon peanut butter"""
+                    },
+                    {
+                        "type": "image_url",
+                        "image_url": {
+                            "url":  f"data:image/jpeg;base64,{base64_image}"
+                        },
+                    },
+                ],
+            }
+        ]
+        
+        # Call OpenAI's Chat Completion API with image
+        response = client.chat.completions.create(
+            model="gpt-4o-mini", 
+            messages=messages
+        )
+        print(response.choices[0].message.content)
+        # Extract the response text
+        ingredient_list = response.choices[0].message.content
+        
+        return ingredient_list, display_nutrition(ingredient_list)
+    
+    except Exception as e:
+        return f"Error processing image: {str(e)}", ""
+    
+def get_nutritional_info(ingredient_list, app_id=edamam_app_id, app_key=edamam_app_key):
+    """
+    Fetches nutritional information for a list of ingredients using Edamam's Nutrition Analysis API.
+
+    Parameters:
+    - ingredient_list (str): A string where each ingredient is on a new line.
+    - app_id (str): Edamam application ID.
+    - app_key (str): Edamam application key.
+
+    Returns:
+    - dict: Nutritional information returned by the API.
+    """
+    print(edamam_app_id, edamam_app_key)
+    url = 'https://api.edamam.com/api/nutrition-details'
+    headers = {'Content-Type': 'application/json'}
+    data = {
+        'title': 'Recipe',
+        'ingr': ingredient_list.split('\n')
+    }
+    params = {
+        'app_id': app_id,
+        'app_key': app_key
+    }
+    response = requests.post(url, headers=headers, json=data, params=params)
+    # response.raise_for_status()  # Raise an error for bad status codes
+    return response.json()
+
+import requests
+
+def format_nutrition(ingredients):
+    nutrition_data = get_nutritional_info(ingredients)
+
+    # Extract relevant nutritional information
+    calories = nutrition_data.get('calories', 0)
+    total_nutrients = nutrition_data.get('totalNutrients', {})
+    carbs = total_nutrients.get('CHOCDF', {}).get('quantity', 0)
+    protein = total_nutrients.get('PROCNT', {}).get('quantity', 0)
+    fats = total_nutrients.get('FAT', {}).get('quantity', 0)
+
+    return {
+        'Calories': calories,
+        'Carbs': carbs,
+        'Protein': protein,
+        'Fats': fats
+    }
+
+def display_nutrition(ingredients):
+    nutrition_info = format_nutrition(ingredients)
+    return (
+        f"🔥 Calories: {nutrition_info['Calories']} kcal\n"
+        f"🍞 Carbs: {nutrition_info['Carbs']:.2f} g\n"
+        f"🍗 Protein: {nutrition_info['Protein']:.2f} g\n"
+        f"🥑 Fats: {nutrition_info['Fats']:.2f} g"
+    )
+
+    
+# Define Gradio interface
+iface = gr.Interface(
+    fn=image_to_ingredient_list,
+    inputs=gr.Image(type="filepath", label="Upload Food Image"),
+    outputs=[gr.Textbox(label="Ingredient List"), gr.Textbox(label="Nutritional Information")],
+    title="Food Image to Ingredient List",
+    description="Upload an image containing food items, and the app will generate an ingredient list like '1 cup rice, 10 oz chickpeas'.",
+    examples=[
+        ["sample_images/Picture1.jpg"],
+        ["sample_images/Picture2.jpg"],
+        ["sample_images/Picture3.jpg"],
+        ["sample_images/Picture4.jpg"],
+        ["sample_images/Picture5.jpg"],
+        ["sample_images/Picture6.jpg"],
+        ["sample_images/Picture7.jpg"],
+        ["sample_images/Picture8.jpg"],
+        ["sample_images/Picture9.jpg"],
+        ["sample_images/Picture10.jpg"],
+        ["sample_images/Picture11.jpg"],
+        ["sample_images/Picture12.jpg"],
+        ["sample_images/Picture13.jpg"],
+        ["sample_images/Picture14.jpg"],
+        ["sample_images/Picture15.jpg"],
+        ["sample_images/Picture16.jpg"],
+        ["sample_images/Picture17.jpg"],
+        ["sample_images/Picture18.jpg"],
+        ["sample_images/Picture19.jpg"],
+        ["sample_images/Picture20.jpg"]
+
+    ],
+    allow_flagging="never"
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()