from fastapi import FastAPI, HTTPException, UploadFile, File
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from typing import Optional, List
import json
from datetime import datetime
from .vector_store import VectorStore
from .brand_style import BrandStyle
from .config import Config
import openai
import os

# Initialize OpenAI
openai.api_key = Config.OPENAI_API_KEY

# Initialize
app = FastAPI(title="Marketing Assistant AI", version="0.1.0")
vector_store = VectorStore()
brand_style = BrandStyle()

# CORS
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_methods=["*"],
    allow_headers=["*"],
)

# Models
class CampaignRequest(BaseModel):
    prompt: str
    content_type: str = "general"
    tone: Optional[str] = None

class Campaign(BaseModel):
    content: str
    content_type: str
    metadata: dict = {}

# Routes
@app.post("/generate")
async def generate_copy(request: CampaignRequest):
    """Generate marketing copy based on prompt and brand guidelines"""
    try:
        # Get similar content from vector store
        similar = vector_store.search(request.prompt, request.content_type)
        
        # Format similar content for context
        similar_content = ""
        if similar:
            similar_content = "\n\nSimilar past campaigns for reference:\n"
            for i, campaign in enumerate(similar[:3], 1):
                similar_content += f"{i}. {campaign.get('content', '')}\n"
        
        # Generate with OpenAI
        system_prompt = brand_style.get_prompt(request)
        user_prompt = f"Create marketing copy for: {request.prompt}{similar_content}"
        
        response = openai.ChatCompletion.create(
            model="gpt-3.5-turbo",
            messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": user_prompt}
            ],
            temperature=0.7,
            max_tokens=500
        )
        
        generated_copy = response.choices[0].message.content
        
        # Store the generated copy for future reference
        new_campaign = {
            "content": generated_copy,
            "content_type": request.content_type,
            "metadata": {
                "prompt": request.prompt,
                "tone": request.tone,
                "generated_at": datetime.now().isoformat()
            }
        }
        
        # Add to vector store for future similarity searches
        vector_store.add_campaign(new_campaign)
        
        return {"result": generated_copy}
    
    except Exception as e:
        print(f"Error in generate_copy: {str(e)}")  # For debugging
        raise HTTPException(status_code=500, detail=str(e))

@app.post("/add-campaign")
async def add_campaign(campaign: Campaign):
    """Add a new campaign to the vector store"""
    try:
        # Add timestamp to metadata
        campaign_data = campaign.dict()
        campaign_data["metadata"]["added_at"] = datetime.now().isoformat()
        
        vector_store.add_campaign(campaign_data)
        return {"status": "success"}
    except Exception as e:
        print(f"Error in add_campaign: {str(e)}")  # For debugging
        raise HTTPException(status_code=500, detail=str(e))

@app.get("/search")
async def search_campaigns(query: str, limit: int = 5):
    """Search for similar campaigns"""
    try:
        results = vector_store.search(query, k=limit)
        return {"results": results}
    except Exception as e:
        print(f"Error in search_campaigns: {str(e)}")  # For debugging
        raise HTTPException(status_code=500, detail=str(e))

@app.get("/")
async def root():
    """Health check endpoint"""
    return {"message": "Marketing Assistant AI is running", "version": "0.1.0"}

@app.get("/health")
async def health_check():
    """Detailed health check"""
    return {
        "status": "healthy",
        "vector_store_size": len(vector_store.campaigns),
        "timestamp": datetime.now().isoformat()
    }

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)