Spaces:
Sleeping
Sleeping
File size: 3,116 Bytes
b435483 0e693fb b435483 0e693fb b435483 0e693fb b435483 0e693fb b435483 0e693fb b435483 29fd933 b435483 0e693fb b435483 0e693fb b435483 0e693fb b435483 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 |
# 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) |