Ayomide
131 lines
4.0 KiB
Python
131 lines
4.0 KiB
Python
from fastapi import FastAPI, HTTPException, UploadFile, File
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from fastapi.middleware.cors import CORSMiddleware
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from pydantic import BaseModel
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from typing import Optional, List
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import json
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from datetime import datetime
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from .vector_store import VectorStore
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from .brand_style import BrandStyle
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from .config import Config
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import openai
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import os
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# Initialize OpenAI
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openai.api_key = Config.OPENAI_API_KEY
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# Initialize
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app = FastAPI(title="Marketing Assistant AI", version="0.1.0")
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vector_store = VectorStore()
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brand_style = BrandStyle()
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# CORS
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_methods=["*"],
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allow_headers=["*"],
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)
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# Models
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class CampaignRequest(BaseModel):
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prompt: str
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content_type: str = "general"
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tone: Optional[str] = None
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class Campaign(BaseModel):
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content: str
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content_type: str
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metadata: dict = {}
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# Routes
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@app.post("/generate")
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async def generate_copy(request: CampaignRequest):
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"""Generate marketing copy based on prompt and brand guidelines"""
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try:
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# Get similar content from vector store
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similar = vector_store.search(request.prompt, request.content_type)
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# Format similar content for context
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similar_content = ""
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if similar:
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similar_content = "\n\nSimilar past campaigns for reference:\n"
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for i, campaign in enumerate(similar[:3], 1):
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similar_content += f"{i}. {campaign.get('content', '')}\n"
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# Generate with OpenAI
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system_prompt = brand_style.get_prompt(request)
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user_prompt = f"Create marketing copy for: {request.prompt}{similar_content}"
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response = openai.ChatCompletion.create(
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model="gpt-3.5-turbo",
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messages=[
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt}
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],
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temperature=0.7,
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max_tokens=500
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)
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generated_copy = response.choices[0].message.content
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# Store the generated copy for future reference
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new_campaign = {
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"content": generated_copy,
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"content_type": request.content_type,
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"metadata": {
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"prompt": request.prompt,
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"tone": request.tone,
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"generated_at": datetime.now().isoformat()
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}
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}
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# Add to vector store for future similarity searches
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vector_store.add_campaign(new_campaign)
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return {"result": generated_copy}
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except Exception as e:
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print(f"Error in generate_copy: {str(e)}") # For debugging
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raise HTTPException(status_code=500, detail=str(e))
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@app.post("/add-campaign")
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async def add_campaign(campaign: Campaign):
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"""Add a new campaign to the vector store"""
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try:
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# Add timestamp to metadata
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campaign_data = campaign.dict()
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campaign_data["metadata"]["added_at"] = datetime.now().isoformat()
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vector_store.add_campaign(campaign_data)
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return {"status": "success"}
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except Exception as e:
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print(f"Error in add_campaign: {str(e)}") # For debugging
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raise HTTPException(status_code=500, detail=str(e))
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@app.get("/search")
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async def search_campaigns(query: str, limit: int = 5):
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"""Search for similar campaigns"""
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try:
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results = vector_store.search(query, k=limit)
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return {"results": results}
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except Exception as e:
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print(f"Error in search_campaigns: {str(e)}") # For debugging
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raise HTTPException(status_code=500, detail=str(e))
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@app.get("/")
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async def root():
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"""Health check endpoint"""
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return {"message": "Marketing Assistant AI is running", "version": "0.1.0"}
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@app.get("/health")
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async def health_check():
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"""Detailed health check"""
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return {
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"status": "healthy",
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"vector_store_size": len(vector_store.campaigns),
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"timestamp": datetime.now().isoformat()
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}
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(app, host="0.0.0.0", port=8000) |