import json
import os
import shutil
import requests
import gradio as gr
from huggingface_hub import Repository, InferenceClient

HF_TOKEN = os.environ.get("HF_TOKEN", None)
USER_NAME = os.environ.get("USER_NAME", None)
APP_PASSWORD = os.environ.get("APP_PASSWORD", None)

# Define the model URL using the model_name
model_url = 'https://api-inference.huggingface.co/models/google/flan-t5-small'

# Create the InferenceClient
client = InferenceClient(model_url, headers={"Authorization": f"Bearer {HF_TOKEN}"})

STOP_SEQUENCES = ["\nUser:", "<|endoftext|>", " User:", "###"]

EXAMPLES = [
    ["Please explain me about machine learning"],
    ["Do you know about python programming? Please create simple application for me."],
    ["What is the history of AI?"],
    ["Can you tell me more about Data Science?"],
    ["Can you write a short tweet about the release of our latest AI model, Falcon 180B LLM?"]
    ]

def format_prompt(message, history, system_prompt):
  prompt = ""
  if system_prompt:
    prompt += f"System: {system_prompt}\n"
  for user_prompt, bot_response in history:
    prompt += f"User: {user_prompt}\n"
    prompt += f"GuruAI: {bot_response}\n" # Response already contains "GuruAI: "
  prompt += f"""User: {message}
GuruAI:"""
  return prompt

seed = 42

def generate(
    prompt, history, system_prompt="", temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,
):
    temperature = float(temperature)
    if temperature < 1e-2:
        temperature = 1e-2
    top_p = float(top_p)
    global seed
    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        stop_sequences=STOP_SEQUENCES,
        do_sample=True,
        seed=seed,
    )
    seed = seed + 1
    formatted_prompt = format_prompt(prompt, history, system_prompt)

    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
    output = ""

    for response in stream:
        output += response.token.text

        for stop_str in STOP_SEQUENCES:
            if output.endswith(stop_str):
                output = output[:-len(stop_str)]
                output = output.rstrip()
                yield output
        yield output
    return output

additional_inputs=[
    gr.Textbox("", label="Optional system prompt"),
    gr.Slider(
        label="Temperature",
        value=0.9,
        minimum=0.0,
        maximum=1.0,
        step=0.05,
        interactive=True,
        info="Higher values produce more diverse outputs",
    ),
    gr.Slider(
        label="Max new tokens",
        value=1088,
        minimum=0,
        maximum=8192,
        step=64,
        interactive=True,
        info="The maximum numbers of new tokens",
    ),
    gr.Slider(
        label="Top-p (nucleus sampling)",
        value=0.90,
        minimum=0.0,
        maximum=1,
        step=0.05,
        interactive=True,
        info="Higher values sample more low-probability tokens",
    ),
    gr.Slider(
        label="Repetition penalty",
        value=1.2,
        minimum=1.0,
        maximum=2.0,
        step=0.05,
        interactive=True,
        info="Penalize repeated tokens",
    )
]


with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column(scale=0.4):
            gr.Image("https://padek.jawapos.com/wp-content/uploads/2022/10/861213472.jpg", elem_id="banner-image", show_label=False)
        with gr.Column():
            gr.Markdown(
                """
                # GuruAI
                
                This is AI as Teacher, It will teach you about anything.
                
                ⚠️ **Limitations**: the model can and will produce factually incorrect information, hallucinating facts and actions. As it has not undergone any advanced tuning/alignment, it can produce problematic outputs, especially if prompted to do so. Finally, this demo is limited to a session length of about 1,000 words.

                Example:
                Model Name = "tiiuae/falcon-180B-chat"
                """
            )

    gr.ChatInterface(
        generate, 
        examples=EXAMPLES,
        additional_inputs=additional_inputs,
    )

demo.queue(concurrency_count=100, api_open=True).launch(show_api=True, auth=(USER_NAME, APP_PASSWORD))