import time
import gradio as gr
import os 
import json 
import requests

#Streaming endpoint
API_URL = os.getenv("API_URL") + "/generate_stream"

def predict(inputs, top_p, temperature, top_k, repetition_penalty, history=[]):
    if not inputs.startswith("User: "):
        inputs = "User: " + inputs + "\n"
    payload = {
        "inputs": inputs, #"My name is Jane and I",
        "parameters": {
            "details": True,
            "do_sample": True,
            "max_new_tokens": 100,
            "repetition_penalty": repetition_penalty, #1.03,
            "seed": 0,
            "temperature": temperature, #0.5,
            "top_k": top_k, #10,
            "top_p": top_p #0.95
        }
    }

    headers = {
        'accept': 'text/event-stream',
        'Content-Type': 'application/json'
    }
    
    history.append(inputs)
    # make a POST request to the API endpoint using the requests.post method, passing in stream=True
    response = requests.post(API_URL, headers=headers, json=payload, stream=True)
    token_counter = 0 
    partial_words = "" 
    # loop over the response data using the iter_lines method of the response object
    for chunk in response.iter_lines():
        # check whether each line is non-empty
      if chunk:
          # decode each line as response data is in bytes
        partial_words = partial_words + json.loads(chunk.decode()[5:])['token']['text']
        if token_counter == 0:
          history.append(" " + partial_words)
        else:
          history[-1] = partial_words
        chat = [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2) ]  # convert to tuples of list
        token_counter+=1
        yield chat, history #{chatbot: chat, state: history}  #[(partial_words, history)]


title = """<h1 align="center">Streaming your Chatbot output with Gradio</h1>"""
description = """Language models can be conditioned to act like dialogue agents through a conversational prompt that typically takes the form:
```
User: <utterance>
Assistant: <utterance>
User: <utterance>
Assistant: <utterance>
...
```
In this app, you can explore the outputs of a 20B large language model.
"""

with gr.Blocks(css = """#col_container {width: 700px; margin-left: auto; margin-right: auto;}
                "#chatbot {height: 400px; overflow: auto;}""") as demo:
    gr.HTML(title)
    with gr.Column(elem_id = "col_container"):
        chatbot = gr.Chatbot(elem_id='chatbot') #c
        inputs = gr.Textbox(placeholder= "Hi my name is Joe.", label= "Type an input and press Enter") #t
        state = gr.State([]) #s
        b1 = gr.Button()
    
        #inputs, top_p, temperature, top_k, repetition_penalty
        with gr.Accordion("Parameters", open=False):
            top_p = gr.Slider( minimum=-0, maximum=1.0, value=0.95, step=0.05, interactive=True, label="Top-p (nucleus sampling)",)
            temperature = gr.Slider( minimum=-0, maximum=5.0, value=0.5, step=0.1, interactive=True, label="Temperature",)
            top_k = gr.Slider( minimum=1, maximum=50, value=4, step=1, interactive=True, label="Top-k",)
            repetition_penalty = gr.Slider( minimum=0.1, maximum=3.0, value=1.03, step=0.01, interactive=True, label="Repetition Penalty", )
    
    #b1.click(predict, [t,s], [c,s])
    #inputs.submit(predict, [t,s], [c,s])
    inputs.submit( predict, [inputs, top_p, temperature, top_k, repetition_penalty, state], [chatbot, state],)
    b1.click( predict, [inputs, top_p, temperature, top_k, repetition_penalty, state], [chatbot, state],)

    gr.Markdown(description)
    demo.queue().launch(debug=True)