import openai
import json
import os
# os.system('pip install gradio==2.8.12')
import gradio as gr

openai.api_key = os.environ.get('OPENAI')

def callAPI(input_prompt, engine_type="text-davinciplus-001", temp=0.2):
    try:
        output = openai.Completion.create(
          engine=engine_type,
          prompt=input_prompt,
          max_tokens=250,
          temperature=temp,
          n=1,
          stop="\n---"
        )
        output = json.dumps(output)
        output = json.loads(output)['choices'][0]
        for ind in output:
            if (ind == 'text'):
                output = output[ind]
                return output
            else:
                print('Error: failed to find text in output')
                raise RuntimeError
    except:
        print('Error: failed to make successful OpenAI API call')
        print(output)
        raise RuntimeError

def makeValues(text_array):
    print(text_array)
    try:
        output = {}
        for text in text_array:
            if text == '':
                pass
            else:
                try:
                    split_text = text.split(": ", 1)
                    if split_text[1] == "none" or split_text[1] == "None" or split_text[1] == '':
                        output[str(split_text[0])] = ""
                    else:
                        output[str(split_text[0])] = split_text[1].strip()
                except:
                    split_text = text.split(":", 1)
                    if split_text[1] == "none" or split_text[1] == "None" or split_text[1] == '':
                        output[str(split_text[0])] = ""
                    else:
                        output[str(split_text[0])] = split_text[1].strip()
        return output

    except:
        print("error converting array values to dict")
        print(output)
        print(text)
        raise RuntimeError

def company(model, input=None):
    if not input:
        return "Error: Please supply a company's name"
    else:
        company_text="Fill in the company facts table truthfully.  If unsure list none.\n---\nFord\nstock_symbol: F\nstock_market: NYSE\nceo: Jim Farley\nheadquarters: Dearborn, MI\nindustry: automotive, manufacturing, electric vehicle\nfounded_date: 1903\nfounders: Henry Ford\nlegal_name: Ford Motor Company\ncompany_website: https://ford.com\nsummary: Ford Motor Company is an American multinational automobile manufacturer headquartered in Dearborn, Michigan, United States.\ncompetitors: Chevrolet, Toyota, Honda\nwikipedia_url: https://en.wikipedia.org/wiki/Ford_Motor_Company\ncrunchbase_url: https://www.crunchbase.com/organization/ford\ninstagram_url: https://www.instagram.com/ford\ntwitter_url: https://twitter.com/Ford\nyoutube_url: https://www.youtube.com/user/ford\n---\n"
        loaded_prompt=company_text+input
        engine_used=model
        result=callAPI(loaded_prompt, engine_used, 0.2)
        result = result.split('\n')
        formatted_result=makeValues(result)
        try:
            final={}
            final['triples']={}
            final['metadata']={}
            final['subject']=input
            final['type']='company'
            final['triples']['stock_symbol'] = formatted_result['stock_symbol']
            final['triples']['stock_market'] = formatted_result['stock_market']
            final['triples']['ceo'] = formatted_result['ceo']
            final['triples']['industry'] = formatted_result['industry'].split(',')
            final['triples']['headquarters'] = formatted_result['headquarters']
            final['triples']['founded_date'] = formatted_result['founded_date']
            final['triples']['founders'] = formatted_result['founders'].split(',')
            final['triples']['legal_name'] = formatted_result['legal_name']
            final['triples']['company_website'] = formatted_result['company_website']
            final['triples']['wikipedia_url'] = formatted_result['wikipedia_url']
            final['triples']['summary'] = formatted_result['summary']
            final['metadata']['producer']='MIDAS v0.01'
            final['metadata']['nlp_model']=engine_used
            print(final)
            #json_result = json.dumps(final)
        except:
            print('Error: could not form JSON from key:values')
            print(formatted_result)
            return '{"Error": "could not form JSON from key:values"}'
        return final

def person(model, input=None):
    if not input:
        return "Error: Please supply a company's name"
    else:
        person_text="Fill in the persons fact table truthfully.  If unsure list none.\n---\nJim Farley\ncommon_name: Jim Farley\nborn: 1956\nlegal_name: Jim Farley\nsummary: Jim Farley is the CEO of Ford Motor Company.\nwikipedia_url: https://en.wikipedia.org/wiki/Jim_Farley\ninstagram_url:\ntwitter_url:\nyoutube_url:\n---\n"
        loaded_prompt=person_text+input
        engine_used=model
        result=callAPI(loaded_prompt, engine_used, 0)
        result = result.split('\n')
        formatted_result=makeValues(result)
        try:
            final={}
            final['triples']={}
            final['metadata']={}
            final['subject']=input
            final['type']='person'
            final['triples']['common_name'] = formatted_result['common_name']
            final['triples']['born'] = formatted_result['born']
            final['triples']['legal_name'] = formatted_result['legal_name']
            final['triples']['instagram_url'] = formatted_result['instagram_url']
            final['triples']['twitter_url'] = formatted_result['twitter_url']
            final['triples']['wikipedia_url'] = formatted_result['wikipedia_url']
            final['triples']['summary'] = formatted_result['summary']
            final['metadata']['producer']='MIDAS v0.01'
            final['metadata']['nlp_model']=engine_used
            print(final)
            #json_result = json.dumps(final)
        except:
            print('Error: could not form JSON from key:values')
            print(formatted_result)
            return '{"Error": "could not form JSON from key:values"}'
        return final
        
model_options = {
    "Curie Instruct (OpenAI)": "text-curie-001",
    "GPT-J": "gpt-j",
}
    
def start(types=None, text=None):
    model = "Curie Instruct (OpenAI)"
    if not text or not types or not model:
        return "Error: Please supply an input"
    else:
        if model_options[model] == 'gpt-j':
            return "GPT-J coming soon"
        else:
            if types == "Company":
                return company(model_options[model], text)
            if types == "Person":
                return person(model_options[model], text)

custom_css = '''
@import url('https://use.typekit.net/kmj7hxn.css');
    
.gradio-bg {
    font-family: 'Roc Grotesk', sans-serif;
}
.gradio-bg .gradio-page {
  display: flex;
  width: 100vw;
  min-height: 50vh;
  flex-direction: column;
  justify-content: center;
  align-items: center;
  margin: 0px;
  max-width: 100vw;
  background: transparent;
}
.gradio-bg .gradio-page .content {
  padding: 0px;
  margin: 0px;
}
.gradio-interface {
  width: 100vw;
  max-width: 1400px;
}
.gradio-interface .panel:nth-child(2) .component:nth-child(3) { 
    display:none
}
.gradio-interface[theme=default] .panel-header {
    letter-spacing: 0.1em;
    font-weight: 500;
    margin-left: 5px;
    margin-bottom: 4px;
}
.gradio-bg .panel-buttons {
    justify-content: flex-end;
    background: #D8CBFE;
    margin: 0;
    border: none;
    padding: 15px;
    padding-top: 0px;
    border-radius: 0px 0px 15px 15px;
}
.panel-button:nth-child(1){
    display:none;
}
.gradio-bg .panel-button {
  flex: 0 0 0;
  min-width: 150px;
}
.gradio-bg .gradio-interface .panel-button.submit { 
  background: #7131FA;
  border-radius: 50px;
  color: #FFFFFF;
  font-size: 18px;
  min-width: 150px;
  letter-spacing: 0.06em;
  line-height: 100%;
  flex: 0 0 0;
  transition: all ease-in-out 240ms;
}
.gradio-bg .gradio-interface .panel-button.submit:hover { 
  background-color: #9f72ff;
  box-shadow: 1px 1px 15px rgba(0, 0, 0, 0.25);
}
.input_text:focus {
  border-color: #FA7880;
}
.input-text[theme=default] input, 
.input-text[theme=default] textarea {
  line-height: 110%;
  color: #7131FA;
  border-radius: 5px;
  padding: 15px;
  border: none;
  background: #FFFFFF;
}
.input-text[theme=default] {
    font-weight: 500;
    font-size: 28px;
    padding: 17px;
    border-radius: 8px;
}
.input-text textarea:focus-visible { 
  outline: none;
}
.input-dropdown[theme=default] .selector {
    font-weight: 500;
    background: rgba(255,255,255,0.5);
    padding: 7px 12px 7px 12px;
}
.input-dropdown[theme=default] .dropdown-item {
    font-weight: 500;
}
.input-dropdown[theme=default] .dropdown-item:hover {
    background: #FFFFFF;
    color: #7131FA;
    font-weight: 500;
}
.gradio-bg .gradio-interface .input-radio .radio-item.selected {
    background-color: #7131FA;
}
.gradio-bg .gradio-interface .input-radio .selected .radio-circle {
    border-color: #4365c4;
}
.gradio-bg .gradio-interface .output-json {
    background: #333;
    padding: 25px;
    border-radius: 10px;
    font-size: 16px;
    color: #eee;
}
.text-green-500 {
    color: #cabdff;
}
.panel:nth-child(1) {
  margin-left: 50px;
  margin-right: 10px;
  margin-bottom: 80px;
  max-width: 575px;
}
.panel {
  background: transparent;
}
.gradio-bg .gradio-interface[theme=default] .component-set {
    background: #D8CBFE;
    border: none;
    box-shadow: none;
    border-radius: 15px 15px 0px 0px;
    padding: 20px;
    padding-bottom: 12px;
}
.panel:nth-child(2) .gradio-interface[theme=default] .panel-header {
  display: none;
}
.labels {
    height: 20px;
    width: auto;
}
@media (max-width: 1000px){
    .panel:nth-child(1) {
        margin-left: 0px;
        margin-right: 0px;
    }
    .gradio-bg .gradio-interface .output-json {
        height: auto;
    }
}
.footer {
    display: none !important;
}
'''

iface = gr.Interface(
    fn=start, 
    inputs=[
        gr.inputs.Dropdown(["Company", "Person"], label="Entity Type"),
        gr.inputs.Textbox(lines=1, label="Subject")
    ],
    outputs=[
        gr.outputs.JSON(label="JSON Triples")
    ],
    css=custom_css,
    theme="default",
    allow_flagging='never',
    allow_screenshot=False,
)
    
iface.launch(enable_queue=True)