import streamlit as st
import requests
import time
from ast import literal_eval
from datetime import datetime

def to_md(text):
    # return text.replace("\n", "<br />")
    return text.replace("\n", "<br />")

@st.cache
def infer(
    prompt, 
    model_name, 
    max_new_tokens=10, 
    temperature=0.1, 
    top_p=1.0,
    top_k=40,
    num_completions=1,
    seed=42,
    stop="\n"
):
    model_name_map = {
        "GPT-JT-6B-v1": "Together-gpt-JT-6B-v1",
    }
    max_new_tokens = int(max_new_tokens)
    num_completions = int(num_completions)
    temperature = float(temperature)
    top_p = float(top_p)
    stop = stop.split(";")
    seed = seed
    
    assert 0 <= max_new_tokens <= 256
    assert 1 <= num_completions <= 5
    assert 0.0 <= temperature <= 10.0
    assert 0.0 <= top_p <= 1.0

    if temperature == 0.0:
        temperature = 0.01
    if prompt=="":
        prompt = " "
    my_post_dict = {
        "model": "Together-gpt-JT-6B-v1",
        "prompt": prompt,
        "top_p": top_p,
        "top_k": top_k,
        "temperature": temperature,
        "max_tokens": max_new_tokens,
        "stop": stop,
    }
    print(f"send: {datetime.now()}")
    response = requests.get("https://staging.together.xyz/api/inference", params=my_post_dict).json()
    generated_text = response['output']['choices'][0]['text']
    print(f"recv: {datetime.now()}")
    
    for stop_word in stop:
        if stop_word != '' and stop_word in generated_text:
            generated_text = generated_text[:generated_text.find(stop_word)]
    
    return generated_text


def set_preset():
    if st.session_state.preset == "Question Answering":
        
        st.session_state.prompt = '''
Please answer the following question:

Question: What is the capital of Canada?
Answer: Ottawa

Question: What is the currency of Switzerland?
Answer: Swiss franc

Question: In which country is Wisconsin located?
Answer:
'''.strip()
        st.session_state.temperature = "0.0"
        st.session_state.top_p = "1.0"
        st.session_state.max_new_tokens = "5"
        st.session_state.stop = r'\n'
        
    elif st.session_state.preset == "Sentiment Analysis":
        
        st.session_state.prompt = '''
Label the tweets as either "positive", "negative", "mixed", or "neutral":

Tweet: I can say that there isn't anything I would change.
Label: positive

Tweet: I'm not sure about this.
Label: neutral

Tweet: I liked some parts but I didn't like other parts.
Label: mixed

Tweet: I think the background image could have been better.
Label: negative

Tweet I really like it.
Label:
'''.strip()
        st.session_state.temperature = "0.0"
        st.session_state.top_p = "1.0"
        st.session_state.max_new_tokens = "2"
        st.session_state.stop = r'\n'
        
    elif st.session_state.preset == "Topic Classification":
        
        st.session_state.prompt = '''
Given a news article, classify its topic.
Possible labels: 1. World 2. Sports 3. Business 4. Sci/Tech

Article: A nearby star thought to harbor comets and asteroids now appears to be home to planets, too.
Label: Sci/Tech

Article: Soaring crude prices plus worries about the economy and the outlook for earnings are expected to hang over the stock market next week during the depth of the summer doldrums.
Label: Business

Article: Murtagh a stickler for success Northeastern field hockey coach Cheryl Murtagh doesn't want the glare of the spotlight that shines on her to detract from a team that has been the America East champion for the past three years and has been to the NCAA tournament 13 times.
Label:
'''.strip()
        st.session_state.temperature = "0.0"
        st.session_state.top_p = "1.0"
        st.session_state.max_new_tokens = "5"
        st.session_state.stop = r'\n'
        
    elif st.session_state.preset == "Paraphrasing":
        
        st.session_state.prompt = '''
Paraphrase the given sentence into a different sentence.

Input: Can you recommed some upscale restaurants in New York?
Output: What upscale restaurants do you recommend in New York?

Input: What are the famous places we should not miss in Paris?
Output: Recommend some of the best places to visit in Paris?

Input: Could you recommed some hotels that have cheap price in Zurich?
Output:
'''.strip()
        st.session_state.temperature = "0.8"
        st.session_state.top_p = "1.0"
        st.session_state.max_new_tokens = "20"
        st.session_state.stop = r'\n'
        
    elif st.session_state.preset == "Text Summarization":
        
        st.session_state.prompt = '''
Given a review from Amazon's food products, the task is to generate a short summary of the given review in the input.

Input: I have bought several of the Vitality canned dog food products and have found them all to be of good quality. The product looks more like a stew than a processed meat and it smells better. My Labrador is finicky and she appreciates this product better than most.
Output: Good Quality Dog Food

Input: Product arrived labeled as Jumbo Salted Peanuts...the peanuts were actually small sized unsalted. Not sure if this was an error or if the vendor intended to represent the product as 'Jumbo'.
Output: Not as Advertised

Input: Wolfgang Puck recently changed their Chicken soup. The old stuff is excellent. The new stuff is watered down, not filling and doesn't taste as good. The image and description on this item stated that it was the old soup. The soup I received is the new stuff. Being a food item, returns are not allowed. I feel this was false advertizing and am extremely displeased.
Output:
'''.strip()
        st.session_state.temperature = "0.0"
        st.session_state.top_p = "1.0"
        st.session_state.max_new_tokens = "10"
        st.session_state.stop = r'\n'
        
    elif st.session_state.preset == "Word Sense Disambiguation":
        
        st.session_state.prompt = '''
Identify which sense of a word is meant in a given context.

Context: The river overflowed the bank.
Word: bank
Sense: river bank

Context: A mouse takes much more room than a trackball.
Word: mouse
Sense: computer mouse

Context: The bank will not be accepting cash on Saturdays.
Word: bank
Sense: commercial (finance) banks

Context: Bill killed the project
Word: kill
Sense:
'''.strip()
        st.session_state.temperature = "0.0"
        st.session_state.top_p = "1.0"
        st.session_state.max_new_tokens = "10"
        st.session_state.stop = r'\n'
        
    elif st.session_state.preset == "Natural Language Inference":
        
        st.session_state.prompt = '''
Given a pair of sentences, choose whether the two sentences agree (entailment)/disagree (contradiction) with each other.
Possible labels: 1. entailment 2. contradiction

Sentence 1: The skier was on the edge of the ramp. Sentence 2: The skier was dressed in winter clothes.
Label: entailment

Sentence 1: The boy skated down the staircase railing. Sentence 2: The boy is a newbie skater.
Label: contradiction

Sentence 1: Two middle-aged people stand by a golf hole. Sentence 2: A couple riding in a golf cart.
Label:
'''.strip()
        st.session_state.temperature = "0.0"
        st.session_state.top_p = "1.0"
        st.session_state.max_new_tokens = "2"
        st.session_state.stop = r'\n'
    
    else:
        pass
    
    
def main():

    if 'preset' not in st.session_state:
        st.session_state.preset = "Sentiment Analysis"
        st.session_state.top_k = "40"
        st.session_state.stop = r'\n'
        set_preset()

    st.title("GPT-JT")
    
    col1, col2 = st.columns([1, 2])
    
    with col1:
        model_name = st.selectbox("Model", ["GPT-JT-6B-v1"])
        
    with col2:
        preset = st.selectbox(
            label="Examples", 
            options=('Question Answering', 'Sentiment Analysis', 
                     "Topic Classification", "Paraphrasing", "Text Summarization",
                     "Word Sense Disambiguation", "Natural Language Inference"), 
            on_change=set_preset,
            key="preset",
        )

    col3, col4 = st.columns([1, 5])

    with col3:
        max_new_tokens = st.text_input('Max new tokens', st.session_state.max_new_tokens)
        temperature = st.text_input('temperature', st.session_state.temperature)
        top_k = st.text_input('top_k', st.session_state.top_k)
        top_p = st.text_input('top_p', st.session_state.top_p)
        # num_completions = st.text_input('num_completions (only the best one will be returend)', "1")
        num_completions = "1"
        stop = st.text_input('stop, split by;', st.session_state.stop)
        # seed = st.text_input('seed', "42")
        seed = "42"

    with col4:

        prompt_area = st.empty()
        prompt = prompt_area.text_area(
            "Prompt",
            value=st.session_state.prompt,
            max_chars=4096,
            height=500,
        )

        generated_area = st.empty()
        generated_area.markdown("(Generate here)")

        button_submit = st.button("Submit")

        if button_submit:
            generated_area.markdown("<b>" + to_md(prompt) + "</b>", unsafe_allow_html=True)
            report_text = infer(
                prompt, model_name=model_name, max_new_tokens=max_new_tokens, temperature=temperature, top_p=top_p, top_k=top_k,
                num_completions=num_completions, seed=seed, stop=literal_eval("'''"+stop+"'''"),
            )
            generated_area.markdown("<b>" + to_md(prompt) + "</b><mark style='background-color: #cbeacd'>" + to_md(report_text)+"</mark>", unsafe_allow_html=True)
        
if __name__ == '__main__':
    main()