from openai import OpenAI
import gradio as gr
import os
import json
import functools
import random
import datetime

api_key = os.environ.get('FEATHERLESS_API_KEY')
client = OpenAI(
    base_url="https://api.featherless.ai/v1",
    api_key=api_key
)

def respond(message, history, model):
    history_openai_format = []
    for human, assistant in history:
        history_openai_format.append({"role": "user", "content": human })
        history_openai_format.append({"role": "assistant", "content":assistant})
    history_openai_format.append({"role": "user", "content": message})
  
    response = client.chat.completions.create(
        model=model,
        messages= history_openai_format,
        temperature=1.0,
        stream=True,
        max_tokens=2000
    )

    partial_message = ""
    for chunk in response:
        if chunk.choices[0].delta.content is not None:
              partial_message = partial_message + chunk.choices[0].delta.content
              yield partial_message

logo = open('./logo.svg').read()

with open('./model-cache.json', 'r') as f_model_cache:
    model_cache = json.load(f_model_cache)


model_class_filter = {
    "mistral-v02-7b-std-lc": True,
    "llama3-8b-8k": True,
    "llama2-solar-10b7-4k": True,
    "mistral-nemo-12b-lc": True,
    "llama2-13b-4k": True,
    "llama3-15b-8k": True,

    "qwen2-32b-lc":False,
    "llama3-70b-8k":False,
    "qwen2-72b-lc":False,
    "mixtral-8x22b-lc":False,
    "llama3-405b-lc":False,
}

def build_model_choices():
    all_choices = []
    for model_class in model_cache:
        if model_class not in model_class_filter:
            print(f"Warning: new model class {model_class}. Treating as blacklisted")
            continue

        if not model_class_filter[model_class]:
            continue
        all_choices += [ (f"{model_id} ({model_class})", model_id) for model_id in model_cache[model_class] ]

    return all_choices

model_choices = build_model_choices()

def initial_model(referer=None):

    if referer == 'http://127.0.0.1:7860/':
        return 'Sao10K/Venomia-1.1-m7'

    if referer and referer.startswith("https://huggingface.co/"):
        possible_model = referer[23:]
        full_model_list = functools.reduce(lambda x,y: x+y, model_cache.values(), [])
        model_is_supported = possible_model in full_model_list
        if model_is_supported:
            return possible_model

    # let's use a random but different model each day.
    key=os.environ.get('RANDOM_SEED', 'kcOtfNHA+e')
    o = random.Random(f"{key}-{datetime.date.today().strftime('%Y-%m-%d')}")
    return o.choice(model_choices)[1]

title_text="HuggingFace's missing inference widget"
with gr.Blocks(title_text, css='.logo-mark { fill: #ffe184; }') as demo:
    gr.HTML("""
        <h1 align="center">HuggingFace's missing inference widget</h1>
        <p align="center">
            Test any <=15B LLM from the hub.
        </p>
        <h2 align="center">
            Please select your model from the list 👇 as HF spaces can't see the refering model card.
        </h2>
    """)

    # hidden_state = gr.State(value=initial_model)

    model_selector = gr.Dropdown(
        label="Select your Model",
        choices=build_model_choices(),
        value=initial_model
        # value=hidden_state
    )

    gr.ChatInterface(
        respond,
        additional_inputs=[model_selector],
        head=""",
        <script>console.log("Hello from gradio!")</script>
        """,
    )
    gr.HTML(f"""
        <p align="center">
            Inference by <a href="https://featherless.ai">{logo}</a>
        </p>
    """)
    def update_initial_model_choice(request: gr.Request):
        return initial_model(request.headers.get('referer'))

    demo.load(update_initial_model_choice, outputs=model_selector)

demo.launch()