| """ |
| 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: |
| |
| 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""" |
| |
| |
| 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() |
| |
| |
| print("🚀 Starting PyPilot deployment...") |
| app, host, port = deployer.create_flask_api(port=5001) |
| |
| |
| 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") |
| |
| |
| try: |
| while True: |
| time.sleep(1) |
| except KeyboardInterrupt: |
| print("\n🛑 Shutting down PyPilot...") |
