marketing-assistant-ai/backend/main.py

"""
Main FastAPI application for the Marketing Assistant AI.
Provides API endpoints for generating and managing marketing content.
"""

import os
import json
from typing import Dict, List, Any, Optional
from datetime import datetime
from pathlib import Path
from fastapi import FastAPI, HTTPException, Depends, Query, Body, status
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse
from loguru import logger
from pydantic import BaseModel, Field

import config
from copywriter import copywriter
from vector_store import vector_store
from brand_style import brand_style_manager
from embeddings import embeddings_manager

# Initialize logging
logger.add(config.LOG_FILE, level=config.LOG_LEVEL, rotation="10 MB", retention="1 month")

# Create FastAPI app
app = FastAPI(
    title="Marketing Assistant AI",
    description="AI-powered tool for marketing copywriting with Adriana James' brand voice",
    version="1.0.0"
)

# Add CORS middleware
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # In production, specify your frontend domain
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Define request and response models
class GenerateCopyRequest(BaseModel):
    prompt: str = Field(..., description="The main instruction for generating content")
    content_type: Optional[str] = Field(None, description="Type of content to generate")
    length: Optional[str] = Field(None, description="Desired length of the content")
    include_cta: Optional[bool] = Field(False, description="Whether to include a call to action")
    reference_similar_content: Optional[bool] = Field(True, description="Whether to reference similar content")
    max_tokens: Optional[int] = Field(1000, description="Maximum tokens for the generated response")

class TrainingDataRequest(BaseModel):
    content_type: str = Field(..., description="Type of content")
    content: str = Field(..., description="The marketing content")
    metadata: Optional[Dict[str, Any]] = Field({}, description="Additional metadata about the content")

class BrandStyleUpdateRequest(BaseModel):
    tone: Optional[List[str]] = Field(None, description="Brand tone options")
    voice_characteristics: Optional[List[str]] = Field(None, description="Voice characteristics")
    taboo_words: Optional[List[str]] = Field(None, description="Words to avoid")
    preferred_terms: Optional[Dict[str, str]] = Field(None, description="Preferred terminology")

class ContentImprovementRequest(BaseModel):
    content: str = Field(..., description="Original generated content")
    feedback: str = Field(..., description="User feedback for improvement")

# API Routes
@app.get("/")
async def root():
    """Root endpoint with API information."""
    return {
        "name": "Marketing Assistant AI",
        "version": "1.0.0",
        "description": f"AI-powered marketing copywriter for {config.BRAND_NAME}"
    }

@app.post("/generate-copy")
async def generate_copy(request: GenerateCopyRequest):
    """Generate marketing copy based on the provided prompt and parameters."""
    try:
        # Validate content type if provided
        if request.content_type and request.content_type not in config.CONTENT_TYPES:
            return JSONResponse(
                status_code=status.HTTP_400_BAD_REQUEST,
                content={
                    "status": "error",
                    "message": f"Invalid content_type. Must be one of: {', '.join(config.CONTENT_TYPES)}"
                }
            )
        
        # Generate copy
        result = await copywriter.generate_copy(
            prompt=request.prompt,
            content_type=request.content_type,
            length=request.length,
            include_cta=request.include_cta,
            reference_similar_content=request.reference_similar_content,
            max_tokens=request.max_tokens
        )
        
        # Add timestamp
        result["metadata"]["generated_at"] = datetime.now().isoformat()
        
        # Store the generated content in the vector store for future reference
        if result["content"]:
            metadata = {
                "content_type": request.content_type,
                "prompt": request.prompt,
                "generated": True
            }
            await vector_store.add_documents([result["content"]], [metadata])
        
        # Store the user query for future training
        query_path = Path(config.DATA_DIR) / "user_queries" / f"{datetime.now().strftime('%Y%m%d%H%M%S')}.json"
        with open(query_path, 'w') as f:
            json.dump({
                "prompt": request.prompt,
                "parameters": {
                    "content_type": request.content_type,
                    "length": request.length,
                    "include_cta": request.include_cta
                },
                "timestamp": datetime.now().isoformat()
            }, f, indent=2)
        
        return {
            "status": "success",
            "content": result["content"],
            "suggestions": result.get("suggestions", []),
            "metadata": result["metadata"]
        }
        
    except Exception as e:
        logger.error(f"Error generating copy: {str(e)}")
        raise HTTPException(
            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
            detail=f"Failed to generate copy: {str(e)}"
        )

@app.get("/brand-style")
async def get_brand_style():
    """Get the current brand style guidelines."""
    try:
        style = brand_style_manager.get_style_guidelines()
        return style
    except Exception as e:
        logger.error(f"Error getting brand style: {str(e)}")
        raise HTTPException(
            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
            detail=f"Failed to get brand style: {str(e)}"
        )

@app.put("/brand-style")
async def update_brand_style(request: BrandStyleUpdateRequest):
    """Update the brand style guidelines."""
    try:
        update_data = request.dict(exclude_unset=True)
        updated_style = brand_style_manager.update_style_guidelines(update_data)
        
        return {
            "status": "success",
            "message": "Brand style updated successfully",
            "style": updated_style
        }
    except Exception as e:
        logger.error(f"Error updating brand style: {str(e)}")
        raise HTTPException(
            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
            detail=f"Failed to update brand style: {str(e)}"
        )

@app.post("/training-data")
async def add_training_data(request: TrainingDataRequest):
    """Add new marketing content for AI training."""
    try:
        # Validate content type
        if request.content_type not in config.CONTENT_TYPES:
            return JSONResponse(
                status_code=status.HTTP_400_BAD_REQUEST,
                content={
                    "status": "error",
                    "message": f"Invalid content_type. Must be one of: {', '.join(config.CONTENT_TYPES)}"
                }
            )
        
        # Add metadata
        metadata = request.metadata.copy()
        metadata["content_type"] = request.content_type
        metadata["added_at"] = datetime.now().isoformat()
        metadata["training_data"] = True
        
        # Add to vector store
        doc_ids = await vector_store.add_documents([request.content], [metadata])
        
        # Save to past campaigns
        campaign_path = Path(config.DATA_DIR) / "past_campaigns" / f"{datetime.now().strftime('%Y%m%d%H%M%S')}.json"
        with open(campaign_path, 'w') as f:
            json.dump({
                "content": request.content,
                "content_type": request.content_type,
                "metadata": metadata,
                "document_id": doc_ids[0] if doc_ids else None,
                "timestamp": datetime.now().isoformat()
            }, f, indent=2)
        
        return {
            "status": "success",
            "message": "Training data added successfully",
            "data_id": doc_ids[0] if doc_ids else None
        }
    except Exception as e:
        logger.error(f"Error adding training data: {str(e)}")
        raise HTTPException(
            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
            detail=f"Failed to add training data: {str(e)}"
        )

@app.get("/training-data")
async def list_training_data(
    content_type: Optional[str] = Query(None, description="Filter by content type"),
    page: int = Query(1, ge=1, description="Page number"),
    limit: int = Query(10, ge=1, le=100, description="Items per page")
):
    """Retrieve a list of available training data."""
    try:
        # Build filters
        filters = {}
        if content_type:
            if content_type not in config.CONTENT_TYPES:
                return JSONResponse(
                    status_code=status.HTTP_400_BAD_REQUEST,
                    content={
                        "status": "error",
                        "message": f"Invalid content_type. Must be one of: {', '.join(config.CONTENT_TYPES)}"
                    }
                )
            filters["content_type"] = content_type
        
        filters["training_data"] = True
        
        # Fetch all matching documents first (not efficient for large datasets but works for demo)
        all_docs = []
        for i in range(len(vector_store.metadata)):
            doc = await vector_store.get_document(i)
            if doc and all(doc["metadata"].get(k) == v for k, v in filters.items()):
                all_docs.append(doc)
        
        # Sort by timestamp (newest first)
        all_docs.sort(key=lambda x: x["metadata"].get("added_at", ""), reverse=True)
        
        # Paginate
        total = len(all_docs)
        pages = (total + limit - 1) // limit if total > 0 else 1
        start = (page - 1) * limit
        end = start + limit
        paginated_docs = all_docs[start:end]
        
        # Format the response
        items = []
        for doc in paginated_docs:
            # Get a preview of the text (first 100 characters)
            preview = doc["text"][:100] + "..." if len(doc["text"]) > 100 else doc["text"]
            
            items.append({
                "id": doc["document_id"],
                "content_type": doc["metadata"].get("content_type", "unknown"),
                "preview": preview,
                "added_at": doc["metadata"].get("added_at", "")
            })
        
        return {
            "items": items,
            "pagination": {
                "total": total,
                "page": page,
                "limit": limit,
                "pages": pages
            }
        }
    except Exception as e:
        logger.error(f"Error listing training data: {str(e)}")
        raise HTTPException(
            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
            detail=f"Failed to list training data: {str(e)}"
        )

@app.get("/training-data/{document_id}")
async def get_training_data(document_id: int):
    """Retrieve a specific training document by ID."""
    try:
        doc = await vector_store.get_document(document_id)
        if not doc:
            raise HTTPException(
                status_code=status.HTTP_404_NOT_FOUND,
                detail=f"Document with ID {document_id} not found"
            )
        
        return {
            "id": doc["document_id"],
            "content": doc["text"],
            "metadata": doc["metadata"]
        }
    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"Error retrieving training data: {str(e)}")
        raise HTTPException(
            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
            detail=f"Failed to retrieve training data: {str(e)}"
        )

@app.delete("/training-data/{document_id}")
async def delete_training_data(document_id: int):
    """Delete a specific training document by ID."""
    try:
        success = await vector_store.delete_document(document_id)
        if not success:
            raise HTTPException(
                status_code=status.HTTP_404_NOT_FOUND,
                detail=f"Document with ID {document_id} not found or could not be deleted"
            )
        
        return {
            "status": "success",
            "message": f"Document with ID {document_id} successfully deleted"
        }
    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"Error deleting training data: {str(e)}")
        raise HTTPException(
            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
            detail=f"Failed to delete training data: {str(e)}"
        )

@app.post("/improve-content")
async def improve_content(request: ContentImprovementRequest):
    """Improve content based on user feedback."""
    try:
        improved_content = await copywriter.improve_copy(
            content=request.content,
            feedback=request.feedback
        )
        
        return {
            "status": "success",
            "original_content": request.content,
            "improved_content": improved_content,
            "feedback": request.feedback
        }
    except Exception as e:
        logger.error(f"Error improving content: {str(e)}")
        raise HTTPException(
            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
            detail=f"Failed to improve content: {str(e)}"
        )

@app.post("/analyze-content")
async def analyze_content(content: str = Body(..., embed=True)):
    """Analyze marketing content for performance prediction."""
    try:
        analysis = await copywriter.analyze_content_performance(content)
        
        return {
            "status": "success",
            "analysis": analysis
        }
    except Exception as e:
        logger.error(f"Error analyzing content: {str(e)}")
        raise HTTPException(
            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
            detail=f"Failed to analyze content: {str(e)}"
        )

# Run the application
if __name__ == "__main__":
    import uvicorn
    uvicorn.run(
        "main:app",
        host=config.API_HOST,
        port=config.API_PORT,
        reload=True
    )