from fastapi import FastAPI, Form
from fastapi import Depends, FastAPI, HTTPException, status, Request, Form, Cookie, Response, Header
from fastapi.responses import HTMLResponse, RedirectResponse, JSONResponse
from fastapi.templating import Jinja2Templates
from fastapi.staticfiles import StaticFiles
from pydantic import BaseModel

import iCulture_semantic_search
import iCulture_wordcloud
import json
import tqdm
import uvicorn

app = FastAPI()
app.mount("/static", StaticFiles(directory="static"), name="static")
templates = Jinja2Templates(directory="templates")

# input model


class Query(BaseModel):
    query: str
    top_k: int
    similarity: float
    start_date: str
    end_date: str


# output model


class Semantic_search(BaseModel):
    semantic_search: str


class Tag_list(BaseModel):
    tag_list: str


class Wordcloud(BaseModel):
    wordcloud: str


@app.get("/", response_class=HTMLResponse)
async def root(request: Request, response: Response):
    return templates.TemplateResponse("index.html", {"request": request, "response": response})


@app.post("/semantic_search", response_model=Semantic_search)
async def semantic_search(query: Query):
    print('-'*50,'\n')
    print('【Request】')
    print(query,'\n')
    print('-'*50)
    query = query.dict()
    return Semantic_search(
        semantic_search=json.dumps(
            iCulture_semantic_search.search_event(query['query'], query['top_k'], query['similarity'], query['start_date'], query['end_date']))
    )


@app.post("/tag_list", response_model=Tag_list)
async def tag_list(query: Query):
    query = query.dict()
    return Tag_list(
        tag_list=json.dumps(
            iCulture_semantic_search.search_event_for_tag_list(
                query['query'], query['top_k'], query['similarity'], query['start_date'], query['end_date']
            ))
    )


@app.post("/wordcloud", response_model=Wordcloud)
async def wordcloud(query: Query):
    query = query.dict()
    return Wordcloud(
        wordcloud=json.dumps(iCulture_wordcloud.to_wordcloud(
            query['query'], query['top_k'], query['similarity'], query['start_date'], query['end_date']))
    )

@app.post("/add_search")
async def add_search(query: Query):
	query = query.dict()
	keywords = query['query'].split()
	### return these three 
	ret_keywords = []
	ret_labels = []
	ret_names = []
	#print("###############keywords:", keywords)
	'''
	with open("static/data.json", "r") as f:
		return_keywords = json.load(f)
		for keyword in keywords:
			if ret := return_keywords.get(keyword):
				return_keywords = ret
			else:
				ret = {}
				break
		ret_keywords = list(ret.keys())
		print("##########return", ret_keywords)
	'''
	### write here and ret is list of recommend keywords
	ret_keywords = ["abc", "def", "ghi", "jkl", "nmo", "pqr"]
	ret_labels = ["1", "2", "3", "4", "5", "6"]
	ret_names = ["Tomoya", "Jared", "Doris", "Wizer", "Nina", "Morrison"]
	###	
	return {"add_keywords":ret_keywords, "add_labels":ret_labels, "add_names":ret_names}

# if __name__ == "__main__":
#     print('123')
#     uvicorn.run("setup:app", host="0.0.0.0", port=12345, reload=True)