app.py

import gradio as gr
import base64
import requests
import io
from PIL import Image
import json
import os
from together import Together
import tempfile
import uuid

def encode_image_to_base64(image_path):
    """Convert image to base64 encoding"""
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode('utf-8')

def save_uploaded_image(image):
    """Save uploaded image to a temporary file and return the path"""
    if image is None:
        return None
    
    with tempfile.NamedTemporaryFile(delete=False, suffix='.jpg') as temp_file:
        if isinstance(image, dict) and "path" in image:  # Gradio returns image as dict
            # Copy the uploaded image to our temporary file
            with open(image["path"], "rb") as img_file:
                temp_file.write(img_file.read())
        elif isinstance(image, Image.Image):
            # If it's a PIL Image, save it
            image.save(temp_file.name, format="JPEG")
        else:
            # Try to handle other formats
            try:
                Image.open(image).save(temp_file.name, format="JPEG")
            except Exception:
                return None
    
    return temp_file.name

def analyze_single_image(client, img_path):
    """Analyze a single image to identify ingredients"""
    system_prompt = """You are a culinary expert AI assistant that specializes in identifying ingredients in images.
    Your task is to analyze the provided image and list all the food ingredients you can identify.
    Be specific and detailed about what you see. Only list ingredients, don't suggest recipes yet."""
    
    user_prompt = "Please identify all the food ingredients visible in this image. List each ingredient on a new line."
    
    # Create message with the image
    content = [
        {"type": "text", "text": user_prompt},
        {
            "type": "image_url",
            "image_url": {
                "url": f"file://{img_path}"
            }
        }
    ]
    
    try:
        response = client.chat.completions.create(
            model="meta-llama/Llama-Vision-Free",
            messages=[
                {
                    "role": "system",
                    "content": system_prompt
                },
                {
                    "role": "user",
                    "content": content
                }
            ],
            max_tokens=500,
            temperature=0.2
        )
        
        return response.choices[0].message.content
    except Exception as e:
        return f"Error analyzing image: {str(e)}"

def get_recipe_suggestions(api_key, images, num_recipes=3, dietary_restrictions="None", cuisine_preference="Any"):
    """
    Get recipe suggestions based on the uploaded images of ingredients
    """
    if not api_key:
        return "Please provide your Together API key."
    
    if not images or len(images) == 0 or all(img is None for img in images):
        return "Please upload at least one image of ingredients."
    
    # Filter out None values
    valid_images = [img for img in images if img is not None]
    
    if len(valid_images) == 0:
        return "No valid images were uploaded. Please try again."
    
    # Save all uploaded images
    image_paths = []
    for img in valid_images:
        img_path = save_uploaded_image(img)
        if img_path:
            image_paths.append(img_path)
    
    if not image_paths:
        return "Failed to process the uploaded images."
    
    try:
        # Initialize Together client with the provided API key
        client = Together(api_key=api_key)
        
        # First, analyze each image separately to identify ingredients
        all_ingredients = []
        for img_path in image_paths:
            ingredients_text = analyze_single_image(client, img_path)
            all_ingredients.append(ingredients_text)
        
        # Combine all ingredients into one list
        combined_ingredients = "\n\n".join([f"Image {i+1} ingredients:\n{ingredients}" 
                                          for i, ingredients in enumerate(all_ingredients)])
        
        # Now generate recipes based on all identified ingredients
        system_prompt = """You are a culinary expert AI assistant that specializes in creating recipes based on available ingredients.
        You will be provided with lists of ingredients identified from multiple images. Your task is to suggest creative, 
        detailed recipes that use as many of the identified ingredients as possible.
        
        For each recipe suggestion, include:
        1. Recipe name
        2. Brief description of the dish
        3. Complete ingredients list (including estimated quantities and any additional staple ingredients that might be needed)
        4. Step-by-step cooking instructions
        5. Approximate cooking time
        6. Difficulty level (Easy, Medium, Advanced)
        7. Nutritional highlights
        
        Consider any dietary restrictions and cuisine preferences mentioned by the user."""
        
        user_prompt = f"""Based on the following ingredients identified from multiple images, suggest {num_recipes} creative and delicious recipes.
        
        {combined_ingredients}
        
        Dietary restrictions to consider: {dietary_restrictions}
        Cuisine preference: {cuisine_preference}
        
        Please be creative with your recipe suggestions and try to use ingredients from multiple images if possible."""
        
        # Generate recipe suggestions based on all identified ingredients
        response = client.chat.completions.create(
            model="meta-llama/Llama-Vision-Free",
            messages=[
                {
                    "role": "system",
                    "content": system_prompt
                },
                {
                    "role": "user",
                    "content": user_prompt
                }
            ],
            max_tokens=2048,
            temperature=0.7
        )
        
        # Clean up the temporary files
        for img_path in image_paths:
            try:
                os.unlink(img_path)
            except:
                pass
        
        # Add information about the ingredients identified
        result = "## 📋 Ingredients Identified\n\n"
        result += combined_ingredients
        result += "\n\n---\n\n"
        result += "## 🍽️ Recipe Suggestions\n\n"
        result += response.choices[0].message.content
        
        return result
        
    except Exception as e:
        # Clean up the temporary files in case of error
        for img_path in image_paths:
            try:
                os.unlink(img_path)
            except:
                pass
        return f"Error: {str(e)}"

# Custom CSS for a more appealing interface
custom_css = """
:root {
    --primary-color: #FF6B6B;
    --secondary-color: #4ECDC4;
    --accent-color: #FFD166;
    --background-color: #f8f9fa;
    --text-color: #212529;
    --card-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
    --border-radius: 10px;
    --font-family: 'Poppins', sans-serif;
}

body {
    font-family: var(--font-family);
    background-color: var(--background-color);
    color: var(--text-color);
}

.container {
    max-width: 1200px;
    margin: 0 auto;
    padding: 20px;
}

.app-header {
    text-align: center;
    margin-bottom: 30px;
    padding: 30px 0;
    background: linear-gradient(135deg, var(--primary-color) 0%, var(--secondary-color) 100%);
    border-radius: var(--border-radius);
    color: white;
    box-shadow: var(--card-shadow);
}

.app-title {
    font-size: 3em;
    margin-bottom: 10px;
    font-weight: bold;
}

.app-subtitle {
    font-size: 1.2em;
    opacity: 0.9;
    max-width: 700px;
    margin: 0 auto;
}

.input-section, .output-section {
    background-color: white;
    border-radius: var(--border-radius);
    padding: 25px;
    box-shadow: var(--card-shadow);
    margin-bottom: 20px;
}

.input-section h3, .output-section h3 {
    color: var(--primary-color);
    margin-top: 0;
    font-size: 1.5em;
    border-bottom: 2px solid var(--secondary-color);
    padding-bottom: 10px;
    margin-bottom: 20px;
}

.image-upload-container {
    border: 2px dashed var(--secondary-color);
    border-radius: var(--border-radius);
    padding: 20px;
    text-align: center;
    margin-bottom: 20px;
    transition: all 0.3s ease;
}

.image-upload-container:hover {
    border-color: var(--primary-color);
    background-color: rgba(255, 107, 107, 0.05);
}

button.primary-button {
    background: linear-gradient(135deg, var(--primary-color) 0%, #FF8E8E 100%);
    color: white;
    border: none;
    padding: 12px 25px;
    border-radius: 30px;
    font-size: 1.1em;
    cursor: pointer;
    transition: all 0.3s ease;
    box-shadow: 0 2px 5px rgba(255, 107, 107, 0.3);
    font-weight: bold;
    display: block;
    width: 100%;
    margin-top: 20px;
}

button.primary-button:hover {
    transform: translateY(-2px);
    box-shadow: 0 4px 8px rgba(255, 107, 107, 0.4);
    background: linear-gradient(135deg, #FF8E8E 0%, var(--primary-color) 100%);
}

.gradio-slider.svelte-17l1npl {
    margin-bottom: 20px;
}

.tab-nav {
    background-color: var(--secondary-color);
    border-radius: var(--border-radius) var(--border-radius) 0 0;
}

.tab-nav button {
    color: white;
    font-weight: bold;
}

.recipe-card {
    border-left: 5px solid var(--accent-color);
    padding: 15px;
    background-color: #f9f9f9;
    margin-bottom: 15px;
    border-radius: 0 var(--border-radius) var(--border-radius) 0;
}

.recipe-title {
    color: var(--primary-color);
    font-size: 1.3em;
    margin-bottom: 5px;
}

.footer {
    text-align: center;
    margin-top: 40px;
    color: #6c757d;
    font-size: 0.9em;
}

.icon {
    color: var(--primary-color);
    margin-right: 5px;
}

.input-group {
    margin-bottom: 20px;
}

.input-group label {
    display: block;
    margin-bottom: 8px;
    font-weight: 600;
    color: var(--text-color);
}

.gallery-item {
    border-radius: var(--border-radius);
    overflow: hidden;
    box-shadow: var(--card-shadow);
    transition: transform 0.3s ease;
}

.gallery-item:hover {
    transform: scale(1.02);
}

.loading-spinner {
    text-align: center;
    padding: 20px;
}

.recipe-output {
    max-height: 800px;
    overflow-y: auto;
    padding-right: 10px;
}

.recipe-output h2 {
    color: var(--primary-color);
    border-bottom: 1px solid var(--secondary-color);
    padding-bottom: 5px;
}

.recipe-output h3 {
    color: var(--secondary-color);
}

/* Responsive styles */
@media (max-width: 768px) {
    .app-title {
        font-size: 2em;
    }
    
    .input-section, .output-section {
        padding: 15px;
    }
}

/* Custom styling for the API key input */
input[type="password"] {
    border: 2px solid #e9ecef;
    border-radius: var(--border-radius);
    padding: 10px 15px;
    font-size: 1em;
    width: 100%;
    transition: border-color 0.3s ease;
}

input[type="password"]:focus {
    border-color: var(--secondary-color);
    outline: none;
}

/* Custom dropdown styling */
select {
    appearance: none;
    background: white url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='24' height='24' viewBox='0 0 24 24' fill='none' stroke='%23FF6B6B' stroke-width='2' stroke-linecap='round' stroke-linejoin='round'%3E%3Cpolyline points='6 9 12 15 18 9'%3E%3C/polyline%3E%3C/svg%3E") no-repeat right 10px center;
    border: 2px solid #e9ecef;
    border-radius: var(--border-radius);
    padding: 10px 40px 10px 15px;
    font-size: 1em;
    width: 100%;
    transition: border-color 0.3s ease;
}

select:focus {
    border-color: var(--secondary-color);
    outline: none;
}

/* Remove Gradio branding */
.gradio-container {
    max-width: 100% !important;
}

.footer-logo, .footer-links {
    display: none !important;
}
"""

# Custom HTML header
html_header = """
<div class="app-header">
    <div class="app-title">🍲 Visual Recipe Assistant</div>
    <div class="app-subtitle">Upload images of ingredients you have on hand and get personalized recipe suggestions powered by AI</div>
</div>
"""

# Custom HTML footer
html_footer = """
<div class="footer">
    <p>🧪 Powered by Meta's Llama-Vision-Free Model & Together AI</p>
    <p>📸 Upload multiple ingredient images for more creative recipe combinations</p>
</div>
"""

# Create the Gradio interface with improved design
with gr.Blocks(css=custom_css) as app:
    gr.HTML(html_header)
    
    with gr.Row():
        with gr.Column(scale=1):
            with gr.Group(elem_classes="input-section"):
                gr.HTML("<h3>🔑 API Configuration</h3>")
                api_key_input = gr.Textbox(
                    label="Together API Key", 
                    placeholder="Enter your Together API key here...",
                    type="password",
                    elem_classes="input-group"
                )
                
                gr.HTML("<h3>📷 Upload Ingredients</h3>")
                image_input = gr.Gallery(
                    label="", 
                    elem_id="ingredient-gallery",
                    elem_classes="gallery-container",
                    columns=3,
                    rows=2,
                    height="auto",
                    object_fit="contain"
                )
                
                # Use File component to handle multiple image uploads
                file_upload = gr.File(
                    label="Upload images of ingredients",
                    file_types=["image"],
                    file_count="multiple",
                    elem_classes="image-upload-container"
                )
                
                gr.HTML("<h3>⚙️ Recipe Preferences</h3>")
                with gr.Row():
                    num_recipes = gr.Slider(
                        minimum=1, 
                        maximum=5, 
                        value=3, 
                        step=1, 
                        label="Number of Recipe Suggestions",
                        elem_classes="input-group"
                    )
                
                with gr.Row():
                    with gr.Column():
                        dietary_restrictions = gr.Dropdown(
                            choices=["None", "Vegetarian", "Vegan", "Gluten-Free", "Dairy-Free", "Low-Carb", "Keto", "Paleo"],
                            value="None",
                            label="Dietary Restrictions",
                            elem_classes="input-group"
                        )
                        
                    with gr.Column():
                        cuisine_preference = gr.Dropdown(
                            choices=["Any", "Italian", "Asian", "Mexican", "Mediterranean", "Indian", "American", "French", "Middle Eastern"],
                            value="Any",
                            label="Cuisine Preference",
                            elem_classes="input-group"
                        )
                
                submit_button = gr.Button("Get Recipe Suggestions", elem_classes="primary-button")
        
        with gr.Column(scale=1):
            with gr.Group(elem_classes="output-section"):
                gr.HTML("<h3>🍽️ Your Personalized Recipes</h3>")
                output = gr.Markdown(elem_classes="recipe-output")
    
    gr.HTML(html_footer)
    
    # Handle file uploads to display in gallery
    def update_gallery(files):
        if not files:
            return None
        return [file.name for file in files]
    
    file_upload.change(fn=update_gallery, inputs=file_upload, outputs=image_input)
    
    # Handle recipe generation
    def process_recipe_request(api_key, files, num_recipes, dietary_restrictions, cuisine_preference):
        if not files:
            return "Please upload at least one image of ingredients."
        
        # Get actual image files from the uploaded files
        images = [file.name for file in files]
        return get_recipe_suggestions(api_key, images, num_recipes, dietary_restrictions, cuisine_preference)
    
    # Set up the submission action
    submit_button.click(
        fn=process_recipe_request,
        inputs=[api_key_input, file_upload, num_recipes, dietary_restrictions, cuisine_preference],
        outputs=output
    )

# Launch the app
if __name__ == "__main__":
    app.launch()