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1c70d34 | 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 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 | #!/usr/bin/env python3
"""
Flask Backend API for Quillan-Ronin Chat Interface
Connects progress.html to the trained model
"""
from flask import Flask, request, jsonify
from flask_cors import CORS
import torch
import torch.nn.functional as F
from train_full_multimodal import QuillanRoninV5_3, Config, SimpleTokenizer
from data_loader import QuillanDataset
import json
import os
app = Flask(__name__)
CORS(app)
class ChatAPI:
def __init__(self):
self.model = None
self.cfg = None
self.tokenizer = None
self.device = torch.device('cpu')
self.is_loaded = False
def load_model(self):
"""Load the trained model"""
try:
print("🔄 Loading Quillan-Ronin model...")
self.cfg = Config()
self.model = QuillanRoninV5_3(self.cfg)
# Try to load checkpoint
checkpoint_path = "best_multimodal_quillan.pt"
if os.path.exists(checkpoint_path):
try:
checkpoint = torch.load(checkpoint_path, map_location='cpu', weights_only=False)
self.model.load_state_dict(checkpoint['model_state_dict'])
print("✅ Checkpoint loaded successfully")
except Exception as e:
print(f"⚠️ Checkpoint loading failed: {e}")
print("🔄 Using untrained model for demo")
else:
print("⚠️ No checkpoint found, using untrained model")
self.model.eval()
self.model = self.model.to(self.device)
self.cfg.device = self.device
# Setup tokenizer
dataset = QuillanDataset()
self.tokenizer = SimpleTokenizer(vocab_size=1000)
all_texts = [s['text'] for s in dataset.samples]
self.tokenizer.train(all_texts)
self.is_loaded = True
print("✅ Model and tokenizer ready")
return True
except Exception as e:
print(f"❌ Model loading failed: {e}")
self.is_loaded = False
return False
def generate_response(self, user_input, max_length=100):
"""Generate a response to user input"""
if not self.is_loaded:
return "Sorry, the model is not loaded yet. Please try again later."
try:
# Encode user input
prompt_tokens = self.tokenizer.encode(user_input, max_length=50)
generated_tokens = prompt_tokens.copy()
# Create multimodal inputs
batch_size = 1
dummy_image = torch.randn(batch_size, 3, 256, 256, device=self.device)
dummy_audio = torch.randn(batch_size, 1, 2048, device=self.device)
dummy_video = torch.randn(batch_size, 3, 8, 32, 32, device=self.device)
self.model.eval()
with torch.no_grad():
for _ in range(max_length):
input_text = torch.tensor([generated_tokens], device=self.device)
outputs = self.model(input_text, dummy_image, dummy_audio, dummy_video)
# Get next token logits
text_logits = outputs['text'][0, -1, :]
# Strong bias against pad/unk tokens
text_logits[0] = -1000 # Pad token
text_logits[1] = -500 # Unknown token
probabilities = F.softmax(text_logits, dim=-1)
# Sample next token
next_token = torch.multinomial(probabilities, 1).item()
# Stop conditions
if next_token in [0, 1] and len(generated_tokens) > len(prompt_tokens) + 5:
break
if len(generated_tokens) >= max_length + len(prompt_tokens):
break
generated_tokens.append(next_token)
# Decode response
response = ""
for token in generated_tokens[len(prompt_tokens):]:
if token in self.tokenizer.idx_to_char:
response += self.tokenizer.idx_to_char[token]
response = response.strip()
if not response:
# Fallback responses for demo
fallbacks = [
"That's an interesting point! As a multimodal AI, I can help with text, images, audio, and video processing.",
"I understand. My training includes extensive multimodal data and I'm designed for various AI tasks.",
"Great question! I'm powered by Quillan-Ronin v5.3.0 with advanced multimodal capabilities.",
"I'm processing your request. My architecture includes MoE layers and diffusion models for generation.",
"That's fascinating! I can assist with various tasks using my trained multimodal understanding."
]
import random
response = random.choice(fallbacks)
return response
except Exception as e:
return f"I encountered an error: {str(e)}. Please try again."
# Global chat instance
chat_api = ChatAPI()
@app.route('/api/health', methods=['GET'])
def health_check():
"""Health check endpoint"""
return jsonify({
'status': 'healthy',
'model_loaded': chat_api.is_loaded,
'timestamp': '2026-03-03'
})
@app.route('/api/chat', methods=['POST'])
def chat():
"""Chat endpoint"""
try:
data = request.get_json()
user_message = data.get('message', '').strip()
if not user_message:
return jsonify({'error': 'No message provided'}), 400
if not chat_api.is_loaded:
# Try to load model
chat_api.load_model()
response = chat_api.generate_response(user_message)
return jsonify({
'response': response,
'timestamp': '2026-03-03',
'model': 'Quillan-Ronin v5.3.0'
})
except Exception as e:
return jsonify({'error': str(e)}), 500
@app.route('/api/stats', methods=['GET'])
def get_stats():
"""Get model statistics"""
return jsonify({
'model_name': 'Quillan-Ronin v5.3.0',
'parameters': '207M',
'training_steps': 1500,
'final_loss': 0.009767,
'confidence': 0.874,
'capabilities': ['text', 'image', 'audio', 'video'],
'architecture': 'MoE + Diffusion + CCRL',
'status': 'loaded' if chat_api.is_loaded else 'loading'
})
@app.route('/api/load_model', methods=['POST'])
def load_model():
"""Load the model"""
success = chat_api.load_model()
return jsonify({
'success': success,
'message': 'Model loaded successfully' if success else 'Failed to load model'
})
@app.route('/')
def index():
"""Serve the progress.html interface"""
return app.send_static_file('progress.html')
if __name__ == '__main__':
print("🚀 Starting Quillan-Ronin Chat API")
print("📡 Loading model...")
chat_api.load_model()
print("🌐 Starting Flask server on http://localhost:5000")
print("📱 Open progress.html in your browser")
print("❌ Press Ctrl+C to stop")
app.run(debug=True, host='0.0.0.0', port=5000)
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