backend/builder_api/service.py

import os
from typing import Dict, Any, List

from backend.builder.engine import AutonomousCodeBuilder
from backend.models.gateway import model_gateway, DEFAULT_MODEL
from backend.tools.knowledge_loader import build_prompt_with_knowledge



class AIBuilderService:
    def __init__(self, workspace_root: str = "."):
        self.builder = AutonomousCodeBuilder(workspace_root=workspace_root)

    def generate_project(
        self,
        prompt: str,
        template: str = "fastapi-react",
    ) -> Dict[str, Any]:
        """
        Generate a production project scaffold and write it to disk.
        (Kept template-based: multi-file scaffolding is a structural
        operation, not a single free-text generation.)
        """
        files: Dict[str, str] = {
            "README.md": f"""# Generated Project

Prompt:
{prompt}
""",
            ".gitignore": """__pycache__/
*.pyc
.env
node_modules/
dist/
build/
""",
        }

        if template == "fastapi-react":
            files.update(
                {
                    "backend/main.py": """from fastapi import FastAPI

app = FastAPI(title="Generated API")

@app.get("/")
async def root():
    return {"status": "ok"}
""",
                    "backend/requirements.txt": """fastapi
uvicorn
""",
                    "frontend/package.json": """{
    "name": "generated-app",
    "private": true,
    "version": "1.0.0"
}
""",
                    "frontend/src/main.tsx": """export default function App() {
    return <h1>Generated Project</h1>;
}
""",
                }
            )

        created_files = self.builder.generate_project(files)
        return {
            "status": "success",
            "prompt": prompt,
            "template": template,
            "generated_files": created_files,
            "file_count": len(created_files),
        }

    async def generate_component(
        self,
        name: str,
        description: str,
        framework: str = "react",
    ) -> Dict[str, Any]:
        """Generate a real UI component via LLM."""
        filename = (
            f"components/{name.lower()}.tsx"
            if framework == "react"
            else f"components/{name.lower()}.py"
        )

        prompt = (
            f"Write a single {framework} component named {name}. "
            f"Description: {description}. "
            f"Output ONLY the code, no explanation, no markdown fences."
        )

        result = await model_gateway.generate(DEFAULT_MODEL, prompt)

        return {
            "status": "success",
            "component_name": name,
            "framework": framework,
            "filepath": filename,
            "code": result["text"],
            "provider": result["provider"],
            "model": result["model"],
        }

    async def generate_api(
        self,
        endpoint_path: str,
        method: str,
        description: str,
    ) -> Dict[str, Any]:
        """Generate a real FastAPI endpoint via LLM."""
        base_prompt = f"Write a production FastAPI route for {method.upper()} {endpoint_path}. Description: {description}. Assume app = FastAPI() exists. Include all imports."
        prompt = build_prompt_with_knowledge(base_prompt, f"fastapi {description}")

        result = await model_gateway.generate(DEFAULT_MODEL, prompt)

        return {
            "status": "success",
            "endpoint": endpoint_path,
            "method": method.upper(),
            "code": result["text"],
            "provider": result["provider"],
            "model": result["model"],
        }

    async def generate_schema(
        self,
        table_name: str,
        fields: List[Dict[str, str]],
    ) -> Dict[str, Any]:
        """Generate a real Pydantic model via LLM."""
        model_name = "".join(word.capitalize() for word in table_name.split("_"))
        field_desc = ", ".join(f"{f['name']}: {f['type']}" for f in fields)

        base_prompt = f"Write a production Pydantic BaseModel named {model_name}Base with fields: {field_desc}. Include SQLAlchemy model too."
        prompt = build_prompt_with_knowledge(base_prompt, "fastapi pydantic sqlalchemy")

        result = await model_gateway.generate(DEFAULT_MODEL, prompt)

        return {
            "status": "success",
            "table_name": table_name,
            "pydantic_model": result["text"],
            "provider": result["provider"],
            "model": result["model"],
        }

    def generate_pipeline(
        self,
        target: str = "docker",
    ) -> Dict[str, Any]:
        """Kept template-based: deployment configs need to be exact/reliable,
        not creatively generated."""
        if target == "docker":
            content = """FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
CMD ["uvicorn", "backend.main:app", "--host", "0.0.0.0", "--port", "8000"]
"""
            filename = "Dockerfile"
        else:
            content = """#!/usr/bin/env bash
echo "Building package..."
"""
            filename = "deploy.sh"

        return {
            "status": "success",
            "target": target,
            "filename": filename,
            "content": content,
        }