app.py

# app.py
import os
from flask import Flask, render_template, request, jsonify, send_from_directory
from graphviz import Digraph
import random

app = Flask(__name__)

# CONFIGURATION
UPLOAD_FOLDER = 'static/uploads'
DIAGRAM_FOLDER = 'static/diagrams'
MODEL_FOLDER = 'static/models'
os.makedirs(UPLOAD_FOLDER, exist_ok=True)
os.makedirs(DIAGRAM_FOLDER, exist_ok=True)

# --- FEATURE 1: AI CODE VISUALIZATION ENGINE ---
@app.route('/generate_code_diagram', methods=['POST'])
def generate_code_diagram():
    """
    Takes code/logic text and creates a visual flowchart image.
    In a real app, you would use an LLM to parse complex code.
    Here, we simulate the visualization of logic flow.
    """
    data = request.json
    code_text = data.get('code', '')
    
    # Create a visual graph (The "Code Image")
    dot = Digraph(comment='Code Flow', format='png')
    dot.attr(rankdir='TB', size='8,5')
    
    # Logic to turn text into nodes (Simulated AI parsing)
    dot.node('A', 'Start: User Input')
    dot.node('B', 'AI Analysis')
    dot.node('C', 'Generate 3D Asset')
    dot.node('D', 'AR Deployment')
    
    dot.edge('A', 'B', label='Upload Image')
    dot.edge('B', 'C', label='Identify Food')
    dot.edge('C', 'D', label='Render GLB')
    
    # Save the diagram
    filename = f"flow_{random.randint(1000,9999)}"
    filepath = os.path.join(DIAGRAM_FOLDER, filename)
    dot.render(filepath)
    
    return jsonify({'diagram_url': f"/{filepath}.png"})

# --- FEATURE 2: AI FOOD ANALYSIS & 3D SELECTOR ---
@app.route('/analyze_food', methods=['POST'])
def analyze_food():
    """
    1. Receives food image.
    2. 'AI' identifies it (Simulated for this demo).
    3. Returns the correct 3D model file for the table.
    """
    if 'image' not in request.files:
        return jsonify({'error': 'No image uploaded'}), 400
    
    file = request.files['image']
    # Save file logic here...
    
    # MOCK AI RECOGNITION LOGIC
    # In a real app, use TensorFlow/YOLO here to detect "Pizza" or "Burger"
    # For demo, we randomly detect one to show the switching capability.
    detected_food = random.choice(['burger', 'pizza']) 
    
    response_data = {
        'food_detected': detected_food,
        'confidence': '98%',
        'model_url': f"/static/models/{detected_food}.glb", # Returns the 3D file path
        'calories': '450 kcal'
    }
    return jsonify(response_data)

# --- FEATURE 3: AI GUIDE CHAT ---
@app.route('/chat_guide', methods=['POST'])
def chat_guide():
    user_msg = request.json.get('message', '').lower()
    
    if "price" in user_msg:
        reply = "This dish costs $12.99 based on the portion size shown."
    elif "spicy" in user_msg:
        reply = "This dish is rated 2/5 on the spice scale."
    else:
        reply = "I am your MenuVision Assistant. Upload a photo to see it in 3D on your table!"
        
    return jsonify({'reply': reply})

@app.route('/')
def index():
    return render_template('index.html')

if __name__ == '__main__':
    # Run on 0.0.0.0 so you can access it from your phone
    app.run(host='0.0.0.0', port=5000, debug=True)