import json
import requests
import gradio as gr
import typesense
from urllib.parse import quote
import os



def getWordVec(content):
    content=quote(content,'utf-8')
    #print(content)
    xurl=os.getenv("emburl")
    print(xurl)
    url=xurl +content
    #print(url)
    response = requests.get(url)
    jsonar=json.loads(response.text).get("embed")
    #print(len(jsonar))
    return jsonar

typesenseserver=os.getenv("typesenseserver")
typesenseport=os.getenv("typesenseport")
typesensekey=os.getenv("typesensekey")
typesensecolname=os.getenv("typesensecolname")

confignode={}
confignode['host']=typesenseserver
confignode['port']=typesenseport
confignode['protocol']='http'
nodes=[]
nodes.append(confignode)
nodeconfig={}
nodeconfig["nodes"]=nodes
nodeconfig["api_key"]=typesensekey
print(nodeconfig)
client = typesense.Client(nodeconfig)
def queryimgage(text):
    html="<table border='1'>\
    <tr>\
    <th>img</th>\
    <th>score</th>\
  </tr>"
    info=getWordVec(text)
    search_requests = {
  'searches': [
    {
      'collection': typesensecolname,
      'q' : '*',
      'per_page': 20,
      'exclude_fields' : 'my_vector',
      'vector_query': 'my_vector:(['+",".join(str(x) for x in info )+'], k:1000)'
    }
  ]
}
    common_search_params =  {}
    res=client.multi_search.perform(search_requests, common_search_params)
    result=res['results'][0]['hits']
    for resultinfo in result:
         documents=resultinfo['document']
         score=(2-resultinfo['vector_distance'])/2
         html=html +"<tr>\
        <td><img src='"+documents['imageurl']+"' width=640 height=600></td>\
        <td>"+ str(score)+"</td>"
    html=html+"</table>"
    return  html

def getNumtip():
    num=client.collections[typesensecolname].retrieve()["num_documents"]
    numtip="图片数:" + str(num)
    return numtip


demo = gr.Interface(
    fn=queryimgage,
    inputs=gr.Textbox(placeholder="请输入文本"),
    outputs=[ "html"],
    article=getNumtip()
)

demo.launch()