"""
Deployment Agent - Generates deployment code and configuration
"""

import os
import yaml
import torch
from datetime import datetime
from typing import Dict, Optional
from jinja2 import Template

from src.models.schemas import (
    TaskType, DeploymentType, BenchmarkResult, UserRequirements
)


class DeploymentAgent:
    """
    Generates production-ready deployment code for selected models.
    
    Supports FastAPI, Gradio, and Docker deployments with
    appropriate configuration files.
    """
    
    def __init__(self, output_dir: str = "deployments"):
        self.output_dir = output_dir
        self._load_templates()
    
    def _load_templates(self):
        """Load deployment templates"""
        self.templates = {}
        
        # FastAPI template
        fastapi_template = '''"""
FastAPI deployment for {{ model_id }}
Generated by HuggingFace Model Selector
"""

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from transformers import pipeline
import torch
import uvicorn
import time
from typing import Dict, Any

app = FastAPI(
    title="{{ model_id }} API",
    description="Model deployment for {{ task_type }}",
    version="1.0.0"
)

# Load model
print("Loading model {{ model_id }}...")
model = pipeline("{{ task_type }}", model="{{ model_id }}")
print("Model loaded successfully!")

class InferenceRequest(BaseModel):
    text: str
    parameters: Dict[str, Any] = {}

class InferenceResponse(BaseModel):
    result: Any
    inference_time: float

@app.post("/predict", response_model=InferenceResponse)
async def predict(request: InferenceRequest):
    """Run inference on input text"""
    try:
        start_time = time.time()
        result = model(request.text, **request.parameters)
        inference_time = time.time() - start_time
        
        return InferenceResponse(
            result=result,
            inference_time=inference_time
        )
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.get("/health")
async def health():
    """Health check endpoint"""
    return {"status": "healthy", "model": "{{ model_id }}"}

@app.get("/info")
async def info():
    """Model information"""
    return {
        "model_id": "{{ model_id }}",
        "task": "{{ task_type }}",
        "device": "cuda" if torch.cuda.is_available() else "cpu"
    }

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)
'''
        self.templates[DeploymentType.FASTAPI] = Template(fastapi_template)
        
        # Gradio template
        gradio_template = '''"""
Gradio deployment for {{ model_id }}
Generated by HuggingFace Model Selector
"""

import gradio as gr
from transformers import pipeline
import torch

# Load model
print("Loading model {{ model_id }}...")
model = pipeline("{{ task_type }}", model="{{ model_id }}")
print("Model loaded successfully!")

def predict(text):
    """Run inference on input text"""
    try:
        result = model(text)
        return result
    except Exception as e:
        return f"Error: {str(e)}"

# Create interface
interface = gr.Interface(
    fn=predict,
    inputs=gr.Textbox(label="Input Text", lines=3),
    outputs=gr.Textbox(label="Result", lines=5),
    title="{{ model_id }}",
    description="Model deployment for {{ task_type }}",
    examples=[["This is a sample input"]]
)

if __name__ == "__main__":
    interface.launch(server_name="0.0.0.0", server_port=7860)
'''
        self.templates[DeploymentType.GRADIO] = Template(gradio_template)
        
        # Dockerfile template
        docker_template = '''FROM python:3.9-slim

WORKDIR /app

# Install system dependencies
RUN apt-get update && apt-get install -y \\
    gcc \\
    g++ \\
    && rm -rf /var/lib/apt/lists/*

# Copy requirements
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy application code
COPY . .

# Expose port
EXPOSE 8000

# Run the application
CMD ["python", "app.py"]
'''
        self.templates["dockerfile"] = Template(docker_template)
    
    def generate_deployment(self, 
                          model_id: str,
                          task_type: TaskType,
                          deployment_type: DeploymentType,
                          benchmark_results: Optional[BenchmarkResult] = None,
                          requirements: Optional[UserRequirements] = None) -> Dict[str, str]:
        """
        Generate deployment code and configuration.
        
        Returns:
            Dictionary mapping filenames to file contents
        """
        deployment_files = {}
        
        # Create timestamp for unique folder
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        safe_model_id = model_id.replace("/", "_")
        deploy_folder = f"{self.output_dir}/{safe_model_id}_{timestamp}"
        
        # Generate main application file
        if deployment_type == DeploymentType.FASTAPI:
            deployment_files["app.py"] = self.templates[DeploymentType.FASTAPI].render(
                model_id=model_id,
                task_type=task_type.value
            )
        elif deployment_type == DeploymentType.GRADIO:
            deployment_files["app.py"] = self.templates[DeploymentType.GRADIO].render(
                model_id=model_id,
                task_type=task_type.value
            )
        
        # Generate requirements.txt
        deployment_files["requirements.txt"] = self._generate_requirements(
            task_type, deployment_type
        )
        
        # Generate Dockerfile if requested
        if deployment_type == DeploymentType.DOCKER:
            deployment_files["Dockerfile"] = self.templates["dockerfile"].render()
            
            # Generate docker-compose.yml
            deployment_files["docker-compose.yml"] = self._generate_docker_compose(
                model_id, requirements
            )
        
        # Generate configuration file
        deployment_files["config.yaml"] = self._generate_config(
            model_id, task_type, benchmark_results, requirements
        )
        
        # Generate README
        deployment_files["README.md"] = self._generate_readme(
            model_id, task_type, deployment_type, benchmark_results
        )
        
        # Add folder info
        deployment_files["_folder"] = deploy_folder
        
        return deployment_files
    
    def _generate_requirements(self, task_type: TaskType, deployment_type: DeploymentType) -> str:
        """Generate requirements.txt"""
        requirements = "# Generated requirements\n"
        requirements += "transformers>=4.35.0\n"
        requirements += "torch>=2.0.0\n"
        requirements += "huggingface-hub>=0.19.0\n"
        
        if deployment_type == DeploymentType.FASTAPI:
            requirements += "fastapi>=0.104.0\n"
            requirements += "uvicorn>=0.24.0\n"
            requirements += "pydantic>=2.0.0\n"
        elif deployment_type == DeploymentType.GRADIO:
            requirements += "gradio>=4.0.0\n"
        
       # Task-specific requirements - using correct TaskType names
        if task_type in [TaskType.IMAGE_CLASSIFICATION, TaskType.OBJECT_DETECTION]:
            requirements += "pillow>=10.0.0\n"
        elif task_type == TaskType.SPEECH_TO_TEXT:  # Changed from SPEECH_RECOGNITION
            requirements += "librosa>=0.10.0\n"
        elif task_type == TaskType.TEXT_TO_SPEECH:
            requirements += "librosa>=0.10.0\n"
        elif task_type == TaskType.OCR:
            requirements += "pillow>=10.0.0\n"
            requirements += "pytesseract>=0.3.10\n"
        
        return requirements
    
    def _generate_docker_compose(self, model_id: str, requirements: Optional[UserRequirements]) -> str:
        """Generate docker-compose.yml"""
        compose = "version: '3.8'\n\n"
        compose += "services:\n"
        compose += "  model-service:\n"
        compose += "    build: .\n"
        compose += "    ports:\n"
        compose += "      - \"8000:8000\"\n"
        compose += "    environment:\n"
        compose += f"      - MODEL_ID={model_id}\n"
        compose += "    restart: unless-stopped\n"
        
        # Add GPU support if needed
        if requirements and any("gpu" in c.value for c in requirements.hardware_constraints):
            compose += "    runtime: nvidia\n"
            compose += "    environment:\n"
            compose += "      - NVIDIA_VISIBLE_DEVICES=all\n"
        
        return compose
    
    def _generate_config(self, model_id: str, task_type: TaskType,
                        benchmark_results: Optional[BenchmarkResult],
                        requirements: Optional[UserRequirements]) -> str:
        """Generate YAML configuration"""
        config = {
            "model": {
                "id": model_id,
                "task": task_type.value,
            },
            "deployment": {
                "batch_size": 1,
                "max_length": 512,
                "device": "cuda" if torch.cuda.is_available() else "cpu"
            }
        }
        
        if benchmark_results and not benchmark_results.error:
            config["performance"] = {
                "latency_ms": benchmark_results.latency_ms,
                "memory_mb": benchmark_results.memory_usage_mb,
                "throughput_sps": benchmark_results.throughput
            }
        
        if requirements:
            config["requirements"] = {}
            
            # Add hardware constraints
            if requirements.hardware_constraints:
                config["requirements"]["hardware_constraints"] = [c.value for c in requirements.hardware_constraints]
            
            # Add max model size
            if requirements.max_model_size_gb:
                config["requirements"]["max_model_size_gb"] = requirements.max_model_size_gb
            
            # Add task-specific requirements based on task type
            if requirements.task_type == TaskType.TRANSLATION and requirements.translation_reqs:
                req = requirements.translation_reqs
                config["requirements"]["source_language"] = req.source_language.value
                config["requirements"]["target_language"] = req.target_language.value
                if req.domain:
                    config["requirements"]["domain"] = req.domain
            
            elif requirements.task_type == TaskType.TEXT_TO_SPEECH and requirements.tts_reqs:
                req = requirements.tts_reqs
                config["requirements"]["language"] = req.language.value
                config["requirements"]["voice_type"] = req.voice_type.value
            
            elif requirements.task_type == TaskType.SPEECH_TO_TEXT and requirements.stt_reqs:
                req = requirements.stt_reqs
                config["requirements"]["language"] = req.language.value
                if req.domain:
                    config["requirements"]["domain"] = req.domain
            
            elif requirements.llm_reqs:
                req = requirements.llm_reqs
                config["requirements"]["model_size"] = req.model_size.value
                config["requirements"]["context_length"] = req.context_length
            
            elif requirements.ocr_reqs:
                req = requirements.ocr_reqs
                config["requirements"]["languages"] = [lang.value for lang in req.languages]
                config["requirements"]["handwritten"] = req.handwritten
    
        return yaml.dump(config, default_flow_style=False)
    
    def _generate_readme(self, model_id: str, task_type: TaskType,
                        deployment_type: DeploymentType,
                        benchmark_results: Optional[BenchmarkResult]) -> str:
        """Generate README.md"""
        readme = f"# {model_id} Deployment\n\n"
        readme += "This deployment was automatically generated by the HuggingFace Model Selector.\n\n"
        readme += "## Model Information\n\n"
        readme += f"- **Model ID**: {model_id}\n"
        readme += f"- **Task**: {task_type.value}\n"
        readme += f"- **Deployment Type**: {deployment_type.value}\n\n"
        
        if benchmark_results and not benchmark_results.error:
            readme += "## Performance Metrics\n\n"
            readme += f"- **Average Latency**: {benchmark_results.latency_ms:.2f} ms\n"
            readme += f"- **Memory Usage**: {benchmark_results.memory_usage_mb:.2f} MB\n"
            readme += f"- **Throughput**: {benchmark_results.throughput_samples_per_second:.2f} samples/second\n\n"
        
        readme += "## Quick Start\n\n"
        readme += "### 1. Install dependencies\n"
        readme += "```bash\n"
        readme += "pip install -r requirements.txt\n"
        readme += "```\n\n"
        
        readme += "### 2. Run the application\n"
        readme += "```bash\n"
        readme += "python app.py\n"
        readme += "```\n\n"
        
        readme += "### 3. Test the API\n\n"
        
        if deployment_type == DeploymentType.FASTAPI:
            readme += "```bash\n"
            readme += "# Health check\n"
            readme += "curl http://localhost:8000/health\n\n"
            readme += "# Run inference\n"
            readme += 'curl -X POST http://localhost:8000/predict \\\n'
            readme += '  -H "Content-Type: application/json" \\\n'
            readme += '  -d \'{"text": "Your input text here"}\'\n'
            readme += "```\n"
        elif deployment_type == DeploymentType.GRADIO:
            readme += "Open http://localhost:7860 in your browser to use the Gradio interface.\n"
        
        if deployment_type == DeploymentType.DOCKER:
            readme += "\n## Docker Deployment\n\n"
            readme += "```bash\n"
            readme += "# Build the image\n"
            readme += "docker build -t model-service .\n\n"
            readme += "# Run the container\n"
            readme += "docker run -p 8000:8000 model-service\n\n"
            readme += "# Or use docker-compose\n"
            readme += "docker-compose up\n"
            readme += "```\n"
        
        return readme
    
    def save_deployment_files(self, deployment_files: Dict[str, str]) -> str:
        """Save generated files to disk"""
        folder = deployment_files.pop("_folder")
        os.makedirs(folder, exist_ok=True)
        
        for filename, content in deployment_files.items():
            filepath = os.path.join(folder, filename)
            with open(filepath, "w", encoding="utf-8") as f:
                f.write(content)
            print(f"     Created {filepath}")
        
        return folder