import time
import gradio as gr

API_URL = "https://joi-20b.ngrok.io/generate_stream"

def predict(inputs, history=[], top_p, temperature, top_k, repetition_penalty): 
    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": 20,
            "repetition_penalty": 1.03,
            "seed": 0,
            "stop": ["photographer"],
            "temperature": 0.5,
            "top_k": 10,
            "top_p": 0.95
        }
    }

    headers = {
        'accept': 'text/event-stream',
        'Content-Type': 'application/json'
    }
    
    history.append(inputs)
    response = requests.post(API_URL, headers=headers, json=payload)
    responses = response.text.split("\n\n")

    partial_words = ""
    for idx, resp in enumerate(responses):
      if resp[:4] == 'data':
        partial_words = partial_words + json.loads(resp[5:])['token']['text']
        #print(partial_words)
        time.sleep(0.05)
        if idx == 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
        
        yield chat, history #resembles {chatbot: chat, state: history}  

title = """<h1 align="center">Gradio Streaming</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 the Joi alpha language models.
"""

with gr.Blocks(css = "#chatbot {height: 400px, overflow: auto;}") as demo:
    gr.HTML(title)
    inputs = gr.Textbox(placeholder= "Hi my name is Joe.", label= "Type an input and press Enter") #t
    chatbot = gr.Chatbot(elem_id='chatbot') #c
    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( inference_chat, [inputs, state, top_p, temperature, top_k, repetition_penalty,], [chatbot, state],)
    b1.click( inference_chat, [inputs, state, top_p, temperature, top_k, repetition_penalty,], [chatbot, state],)

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