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"""
PyPilot Model Deployer - Production deployment and serving
"""
import torch
from transformers import pipeline
import flask
from flask import Flask, request, jsonify
import fastapi
from fastapi import FastAPI, HTTPException
import uvicorn
import threading
import time
from datetime import datetime
class PyPilotDeployer:
def __init__(self, model_path=None):
self.model_path = model_path
self.model = None
self.tokenizer = None
self.is_loaded = False
def load_model_for_inference(self, quantize=True):
"""Load model optimized for inference"""
print("🔄 Loading model for inference...")
if quantize:
# Apply quantization for faster inference
self.model = torch.quantization.quantize_dynamic(
self.model, {torch.nn.Linear}, dtype=torch.qint8
)
print("✅ Model quantized for faster inference")
self.model.eval()
self.is_loaded = True
print("✅ Model ready for inference!")
def create_flask_api(self, host='0.0.0.0', port=5000):
"""Create Flask REST API for model serving"""
app = Flask(__name__)
@app.route('/health', methods=['GET'])
def health_check():
return jsonify({'status': 'healthy', 'timestamp': datetime.now().isoformat()})
@app.route('/complete', methods=['POST'])
def code_completion():
data = request.get_json()
code_prompt = data.get('code', '')
max_length = data.get('max_length', 100)
if not self.is_loaded:
return jsonify({'error': 'Model not loaded'}), 500
try:
completion = self.generate_completion(code_prompt, max_length)
return jsonify({
'completion': completion,
'timestamp': datetime.now().isoformat()
})
except Exception as e:
return jsonify({'error': str(e)}), 500
@app.route('/analyze', methods=['POST'])
def code_analysis():
data = request.get_json()
code = data.get('code', '')
from code_analyzer import PyPilotCodeAnalyzer
analyzer = PyPilotCodeAnalyzer()
analysis = analyzer.comprehensive_analysis(code)
return jsonify(analysis)
print(f"🌐 Starting Flask API on {host}:{port}")
return app, host, port
def create_fastapi_service(self):
"""Create FastAPI service for high-performance serving"""
app = FastAPI(title="PyPilot API", version="1.0.0")
@app.get("/")
async def root():
return {"message": "PyPilot Code Assistant API"}
@app.post("/v1/completions")
async def create_completion(request: dict):
code = request.get("code", "")
max_tokens = request.get("max_tokens", 100)
if not code:
raise HTTPException(status_code=400, detail="Code prompt required")
completion = self.generate_completion(code, max_tokens)
return {
"completion": completion,
"model": "PyPilot",
"created": datetime.now().isoformat()
}
@app.post("/v1/analysis")
async def analyze_code(request: dict):
code = request.get("code", "")
from code_analyzer import PyPilotCodeAnalyzer
analyzer = PyPilotCodeAnalyzer()
analysis = analyzer.comprehensive_analysis(code)
return analysis
return app
def generate_completion(self, prompt, max_length=100):
"""Generate code completion"""
# This would use the actual model for inference
# For now, return a mock completion
mock_completions = [
f"# Generated completion for your code\nprint('Hello from PyPilot!')",
f"# TODO: Implement this functionality\nreturn result",
f"# PyPilot suggestion\nif __name__ == '__main__':\n main()"
]
import random
return random.choice(mock_completions)
def start_serving(self, api_type='flask', **kwargs):
"""Start the model serving API"""
if api_type == 'flask':
app, host, port = self.create_flask_api(**kwargs)
app.run(host=host, port=port, debug=False)
elif api_type == 'fastapi':
app = self.create_fastapi_service()
uvicorn.run(app, host=kwargs.get('host', '0.0.0.0'),
port=kwargs.get('port', 8000))
def create_gradio_interface(self):
"""Create Gradio web interface for easy testing"""
try:
import gradio as gr
def gradio_complete(code):
return self.generate_completion(code)
def gradio_analyze(code):
from code_analyzer import PyPilotCodeAnalyzer
analyzer = PyPilotCodeAnalyzer()
return analyzer.comprehensive_analysis(code)
interface = gr.Interface(
fn=gradio_complete,
inputs=gr.Textbox(lines=10, placeholder="Enter your code here..."),
outputs="text",
title="PyPilot Code Assistant",
description="AI-powered code completion and analysis"
)
return interface
except ImportError:
print("Gradio not installed. Install with: pip install gradio")
return None
if __name__ == "__main__":
deployer = PyPilotDeployer()
# Start a simple Flask server
print("🚀 Starting PyPilot deployment...")
app, host, port = deployer.create_flask_api(port=5001)
# Run in background thread
def run_flask():
app.run(host=host, port=port, debug=False, use_reloader=False)
flask_thread = threading.Thread(target=run_flask)
flask_thread.daemon = True
flask_thread.start()
print(f"✅ PyPilot API running on http://{host}:{port}")
print("📚 Endpoints:")
print(" GET /health - Health check")
print(" POST /complete - Code completion")
print(" POST /analyze - Code analysis")
# Keep running
try:
while True:
time.sleep(1)
except KeyboardInterrupt:
print("\n🛑 Shutting down PyPilot...") |