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"""
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Flask App with Gunicorn for Deep Modal Files
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Economics Chat Application using Qwen2 model
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"""
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from flask import Flask, request, jsonify, render_template_string
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import os
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import logging
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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app = Flask(__name__)
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model = None
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tokenizer = None
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HTML_TEMPLATE = """
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<!DOCTYPE html>
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<html>
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<head>
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<title>AEGIS Economics AI</title>
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<meta charset="utf-8">
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<meta name="viewport" content="width=device-width, initial-scale=1">
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<style>
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body { font-family: Arial, sans-serif; margin: 0; padding: 20px; background: #f5f5f5; }
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.container { max-width: 800px; margin: 0 auto; background: white; padding: 20px; border-radius: 10px; box-shadow: 0 2px 10px rgba(0,0,0,0.1); }
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.header { text-align: center; margin-bottom: 30px; }
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.chat-container { border: 1px solid #ddd; border-radius: 5px; height: 400px; overflow-y: auto; padding: 10px; margin-bottom: 20px; background: #fafafa; }
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.message { margin: 10px 0; padding: 10px; border-radius: 5px; }
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.user-message { background: #007bff; color: white; margin-left: 20%; }
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.ai-message { background: #e9ecef; color: #333; margin-right: 20%; }
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.input-group { display: flex; gap: 10px; }
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.input-field { flex: 1; padding: 10px; border: 1px solid #ddd; border-radius: 5px; }
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.send-btn { padding: 10px 20px; background: #007bff; color: white; border: none; border-radius: 5px; cursor: pointer; }
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.send-btn:hover { background: #0056b3; }
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.loading { text-align: center; color: #666; font-style: italic; }
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</style>
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</head>
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<body>
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<div class="container">
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<div class="header">
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<h1>🏛️ AEGIS Economics AI</h1>
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<p>Advanced Economic Analysis & Policy Insights</p>
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</div>
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<div id="chat-container" class="chat-container">
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<div class="message ai-message">
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Hello! I'm AEGIS Economics AI. Ask me about economic policies, market analysis, or financial strategies.
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<div id="model-status" style="font-size: 0.8em; color: #666; margin-top: 5px;">
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Checking model status...
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</div>
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</div>
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</div>
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<div class="input-group">
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<input type="text" id="user-input" class="input-field" placeholder="Ask about economics, policy, markets..." onkeypress="handleKeyPress(event)">
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<button onclick="sendMessage()" class="send-btn">Send</button>
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</div>
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</div>
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<script>
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// Check model status on page load
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async function checkModelStatus() {
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try {
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const response = await fetch('/health');
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const data = await response.json();
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const statusDiv = document.getElementById('model-status');
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if (data.model_loaded) {
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statusDiv.textContent = '✅ Model loaded and ready!';
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statusDiv.style.color = '#28a745';
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} else {
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statusDiv.textContent = '⏳ Model loading... Please wait.';
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statusDiv.style.color = '#ffc107';
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// Try to load model
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setTimeout(tryLoadModel, 2000);
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}
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} catch (error) {
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const statusDiv = document.getElementById('model-status');
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statusDiv.textContent = '❌ Connection error';
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statusDiv.style.color = '#dc3545';
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}
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}
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async function tryLoadModel() {
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try {
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const response = await fetch('/load_model', { method: 'POST' });
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const data = await response.json();
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if (data.success) {
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const statusDiv = document.getElementById('model-status');
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statusDiv.textContent = '✅ Model loaded successfully!';
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statusDiv.style.color = '#28a745';
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} else {
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setTimeout(checkModelStatus, 5000); // Check again in 5 seconds
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}
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} catch (error) {
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setTimeout(checkModelStatus, 5000);
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}
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}
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// Call on page load
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window.onload = checkModelStatus;
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function handleKeyPress(event) {
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if (event.key === 'Enter') {
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sendMessage();
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}
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}
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function addMessage(content, isUser) {
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const chatContainer = document.getElementById('chat-container');
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const messageDiv = document.createElement('div');
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messageDiv.className = `message ${isUser ? 'user-message' : 'ai-message'}`;
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messageDiv.textContent = content;
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chatContainer.appendChild(messageDiv);
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chatContainer.scrollTop = chatContainer.scrollHeight;
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}
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function showLoading() {
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const chatContainer = document.getElementById('chat-container');
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const loadingDiv = document.createElement('div');
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loadingDiv.className = 'loading';
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loadingDiv.id = 'loading';
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loadingDiv.textContent = 'AI is thinking...';
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chatContainer.appendChild(loadingDiv);
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chatContainer.scrollTop = chatContainer.scrollHeight;
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}
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function hideLoading() {
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const loading = document.getElementById('loading');
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if (loading) {
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loading.remove();
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}
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}
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async function sendMessage() {
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const input = document.getElementById('user-input');
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const message = input.value.trim();
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if (!message) return;
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addMessage(message, true);
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input.value = '';
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showLoading();
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try {
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const response = await fetch('/chat', {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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},
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body: JSON.stringify({ message: message })
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});
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const data = await response.json();
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hideLoading();
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if (data.response) {
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addMessage(data.response, false);
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} else {
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addMessage('Sorry, I encountered an error. Please try again.', false);
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}
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} catch (error) {
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hideLoading();
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addMessage('Connection error. Please try again.', false);
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}
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}
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</script>
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</body>
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</html>
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"""
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def load_model():
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"""Load the Qwen2 model and tokenizer from HF repository"""
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global model, tokenizer
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try:
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logger.info("Loading model and tokenizer from Hugging Face...")
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model_repo = "Gaston895/Aegisecon1"
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logger.info(f"Loading tokenizer from {model_repo}...")
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tokenizer = AutoTokenizer.from_pretrained(
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model_repo,
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trust_remote_code=True,
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use_auth_token=False
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)
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logger.info(f"Loading model from {model_repo}...")
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model = AutoModelForCausalLM.from_pretrained(
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model_repo,
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torch_dtype=torch.float16,
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device_map="cpu",
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trust_remote_code=True,
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use_auth_token=False,
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low_cpu_mem_usage=True
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)
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logger.info("Model loaded successfully from HF repository!")
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return True
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except Exception as e:
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logger.error(f"Error loading model from HF: {str(e)}")
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try:
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logger.info("Trying alternative loading method...")
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tokenizer = AutoTokenizer.from_pretrained(
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"Qwen/Qwen2-1.5B",
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trust_remote_code=True
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)
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model = AutoModelForCausalLM.from_pretrained(
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"Qwen/Qwen2-1.5B",
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torch_dtype=torch.float16,
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device_map="cpu",
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trust_remote_code=True,
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low_cpu_mem_usage=True
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)
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logger.info("Fallback model loaded successfully!")
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return True
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except Exception as e2:
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logger.error(f"Fallback loading also failed: {str(e2)}")
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return False
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def generate_response(prompt):
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"""Generate response using the loaded model"""
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try:
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if model is None or tokenizer is None:
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return "Model is still loading, please wait a moment and try again..."
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system_prompt = """You are AEGIS Economics AI, an expert economic analyst and policy advisor.
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Provide clear, accurate, and insightful responses about economics, finance, markets, and policy.
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Focus on practical analysis and actionable insights."""
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full_prompt = f"{system_prompt}\n\nUser: {prompt}\nAssistant:"
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inputs = tokenizer(full_prompt, return_tensors="pt", truncation=True, max_length=1024)
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with torch.no_grad():
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outputs = model.generate(
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inputs.input_ids,
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max_new_tokens=256,
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temperature=0.7,
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do_sample=True,
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pad_token_id=tokenizer.eos_token_id,
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repetition_penalty=1.1,
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no_repeat_ngram_size=3
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)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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if "Assistant:" in response:
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response = response.split("Assistant:")[-1].strip()
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return response
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except Exception as e:
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logger.error(f"Error generating response: {str(e)}")
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return "I apologize, but I'm having trouble processing your request right now. Please try again in a moment."
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@app.route('/')
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def home():
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"""Serve the main chat interface"""
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return render_template_string(HTML_TEMPLATE)
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@app.route('/chat', methods=['POST'])
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def chat():
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"""Handle chat messages"""
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try:
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data = request.get_json()
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user_message = data.get('message', '')
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if not user_message:
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return jsonify({'error': 'No message provided'}), 400
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ai_response = generate_response(user_message)
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return jsonify({'response': ai_response})
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except Exception as e:
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logger.error(f"Error in chat endpoint: {str(e)}")
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return jsonify({'error': 'Internal server error'}), 500
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@app.route('/health')
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def health():
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"""Health check endpoint"""
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return jsonify({
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'status': 'healthy',
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'model_loaded': model is not None,
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'tokenizer_loaded': tokenizer is not None,
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'model_info': 'Gaston895/Aegisecon1' if model is not None else 'Not loaded'
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})
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@app.route('/load_model', methods=['POST'])
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def load_model_endpoint():
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"""Endpoint to trigger model loading"""
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try:
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success = load_model()
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return jsonify({
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'success': success,
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'model_loaded': model is not None,
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'tokenizer_loaded': tokenizer is not None
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})
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except Exception as e:
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return jsonify({'error': str(e)}), 500
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if __name__ == '__main__':
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logger.info("Starting AEGIS Economics AI...")
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logger.info("Attempting to load model...")
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model_loaded = load_model()
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if model_loaded:
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logger.info("Model loaded successfully, starting server...")
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else:
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logger.warning("Model failed to load, starting server anyway. Model can be loaded via /load_model endpoint.")
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app.run(host='0.0.0.0', port=7860, debug=False) |
