Create model_deployer.py

model_deployer.py

@@ -0,0 +1,188 @@
+"""
+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...")