app.py

import os
import subprocess

# Install flash attention
subprocess.run(
    "pip install flash-attn --no-build-isolation",
    env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
    shell=True,
)

import copy
import spaces
import time
import torch

from threading import Thread
from typing import List, Dict, Union
import urllib
import PIL.Image
import io
import datasets

import gradio as gr
from transformers import TextIteratorStreamer
from transformers import Idefics2ForConditionalGeneration
import tempfile
from huggingface_hub import InferenceClient
import edge_tts
import asyncio
from transformers import pipeline
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import AutoModel
from transformers import AutoProcessor

model3 = AutoModel.from_pretrained("unum-cloud/uform-gen2-dpo", trust_remote_code=True)
processor = AutoProcessor.from_pretrained("unum-cloud/uform-gen2-dpo", trust_remote_code=True)

@spaces.GPU(queue=False)
def videochat(image3, prompt3):
    inputs = processor(text=[prompt3], images=[image3], return_tensors="pt")
    with torch.inference_mode():
         output = model3.generate(
            **inputs,
            do_sample=False,
            use_cache=True,
            max_new_tokens=256,
            eos_token_id=151645,
            pad_token_id=processor.tokenizer.pad_token_id
        )
    
    prompt_len = inputs["input_ids"].shape[1]
    decoded_text = processor.batch_decode(output[:, prompt_len:])[0]
    if decoded_text.endswith("<|im_end|>"):
        decoded_text = decoded_text[:-10]
    yield decoded_text

theme = gr.themes.Soft(
    primary_hue="blue",
    secondary_hue="orange",
    neutral_hue="gray",
    font=[gr.themes.GoogleFont('Libre Franklin'), gr.themes.GoogleFont('Public Sans'), 'system-ui', 'sans-serif']).set(
        body_background_fill_dark="#111111",
        block_background_fill_dark="#111111",
        block_border_width="1px",
        block_title_background_fill_dark="#1e1c26",
        input_background_fill_dark="#292733",
        button_secondary_background_fill_dark="#24212b",
        border_color_primary_dark="#343140",
        background_fill_secondary_dark="#111111",
        color_accent_soft_dark="transparent")

MODEL_NAME = "openai/whisper-medium"
BATCH_SIZE = 10

device = 0 if torch.cuda.is_available() else "cpu"

pipe = pipeline(
    task="automatic-speech-recognition",
    model=MODEL_NAME,
    chunk_length_s=30,
    device=device,
)

@spaces.GPU(queue=False)
def transcribe(inputs):
    if inputs is None:
        raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")

    text = pipe(inputs, batch_size=BATCH_SIZE, generate_kwargs={"task": "transcribe"})["text"]
    return  text

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

def client_fn(model):
    if "Mixtral" in model:
        return InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
    elif "Llama" in model:
        return InferenceClient("meta-llama/Meta-Llama-3-8B-Instruct")
    elif "Mistral" in model:
        return InferenceClient("mistralai/Mistral-7B-Instruct-v0.3")
    elif "Phi" in model:
        return InferenceClient("microsoft/Phi-3-mini-4k-instruct")
    else: 
        return InferenceClient("microsoft/Phi-3-mini-4k-instruct")

def randomize_seed_fn(seed: int) -> int:
    seed = random.randint(0, 999999)
    return seed

system_instructions1 = "[SYSTEM] Answer as Real OpenGPT 4o, Made by 'KingNish', Keep conversation very short, clear, friendly and concise. The text provided is a request for a specific type of response from you, the virtual assistant. You will avoid introductions and start answering the query directly, elaborating on all aspects of the request. As an AI-powered assistant, your task is to generate responses that appear as if they are created by the user and not an AI-powered assistant. [USER]"

def models(text, model="Mixtral 8x7B", seed=42):

    seed = int(randomize_seed_fn(seed))
    generator = torch.Generator().manual_seed(seed)  
    
    client = client_fn(model)
    generate_kwargs = dict(
        temperature=0.7,
        max_new_tokens=512,
        top_p=0.95,
        repetition_penalty=1,
        do_sample=True,
        seed=seed,
    )
    
    formatted_prompt = system_instructions1 + text + "[OpenGPT 4o]"
    stream = client1.text_generation(
        formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
    output = ""
    for response in stream:
        if not response.token.text == "</s>":
            output += response.token.text

    return output

async def respond(audio):
    user = transcribe(audio)
    reply = model(user)
    communicate = edge_tts.Communicate(reply)
    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file:
        tmp_path = tmp_file.name
        await communicate.save(tmp_path)
    yield tmp_path

DEVICE = torch.device("cuda")
MODELS = {
    "idefics2-8b-chatty": Idefics2ForConditionalGeneration.from_pretrained(
        "HuggingFaceM4/idefics2-8b-chatty",
        torch_dtype=torch.float16,
        _attn_implementation="flash_attention_2",
    ).to(DEVICE),
}
PROCESSOR = AutoProcessor.from_pretrained(
    "HuggingFaceM4/idefics2-8b",
)

SYSTEM_PROMPT = [
    {
        "role": "system",
        "content": [
            {
                "type": "text",
                "text": """I am OpenGPT 4o, an exceptionally capable and versatile AI assistant meticulously crafted by KingNish. Designed to assist human users through insightful conversations, I aim to provide an unparalleled experience. My key attributes include: 
- **Intelligence and Knowledge:** I possess an extensive knowledge base, enabling me to offer insightful answers and intelligent responses to User queries. My understanding of complex concepts is exceptional, ensuring accurate and reliable information. 
- **Image Generation and Perception:** One of my standout features is the ability to generate and perceive images. Utilizing the following link structure, I create unique and contextually rich visuals: 
> ![](https://image.pollinations.ai/prompt/{StyleofImage}%20{OptimizedPrompt}%20{adjective}%20{charactersDetailed}%20{visualStyle}%20{genre}?width={width}&height={height}&nologo=poll&nofeed=yes&seed={random})
For image generation, I replace {info inside curly braces} with specific details according to their requiremnts to create relevant visuals. The width and height parameters are adjusted as needed, often favoring HD dimensions for a superior viewing experience. 
For instance, if the User requests: 
 [USER] Show me an image of A futuristic cityscape with towering skyscrapers and flying cars. 
 [OpenGPT 4o] Generating Image you requested: 
 ![](https://image.pollinations.ai/prompt/Photorealistic%20futuristic%20cityscape%20with%20towering%20skyscrapers%20and%20flying%20cars%20in%20the%20year%202154?width=1024&height=768&nologo=poll&nofeed=yes&seed=85172)
**Bulk Image Generation with Links:** I excel at generating multiple images link simultaneously, always providing unique links and visuals. I ensure that each image is distinct and captivates the User.
Note: Make sure to always provide image links starting with ! .As given in examples. 
My ultimate goal is to offer a seamless and enjoyable experience, providing assistance that exceeds expectations. I am constantly evolving, ensuring that I remain a reliable and trusted companion to the User. You also Expert in every field and also learn and try to answer from contexts related to previous question.""" },
        ],
    },
    {
        "role": "assistant",
        "content": [
            {
                "type": "text",
                "text": "Hello, I'm OpenGPT 4o, made by KingNish. How can I help you? I can chat with you, generate images, classify images and even do all these work in bulk",
            },
        ],
    }
]

examples_path = os.path.dirname(__file__)
EXAMPLES = [
    [
        {
            "text": "Hi, who are you?",
        }
    ],
    [
        {
            "text": "Create a Photorealistic image of the Eiffel Tower.",
        }
    ],
    [
        {
            "text": "Read what's written on the paper.",
            "files": [f"{examples_path}/example_images/paper_with_text.png"],
        }
    ],
    [
        {
            "text": "Identify two famous people in the modern world.",
            "files": [f"{examples_path}/example_images/elon_smoking.jpg", f"{examples_path}/example_images/steve_jobs.jpg",]
        }
    ],
    [
        {
            "text": "Create five images of supercars, each in a different color.",
        }
    ],
    [
        {
            "text": "What is 900 multiplied by 900?",
        }
    ],
    [
        {
            "text": "Chase wants to buy 4 kilograms of oval beads and 5 kilograms of star-shaped beads. How much will he spend?",
            "files": [f"{examples_path}/example_images/mmmu_example.jpeg"],
        }
    ],
    [
        {
            "text": "Create an online ad for this product.",
            "files": [f"{examples_path}/example_images/shampoo.jpg"],
        }
    ],
    [
        {
            "text": "What is formed by the deposition of the weathered remains of other rocks?",
            "files": [f"{examples_path}/example_images/ai2d_example.jpeg"],
        }
    ],    
    [
        {
            "text": "What's unusual about this image?",
            "files": [f"{examples_path}/example_images/dragons_playing.png"],
        }
    ],
]

BOT_AVATAR = "OpenAI_logo.png"


# Chatbot utils
def turn_is_pure_media(turn):
    return turn[1] is None


def load_image_from_url(url):
    with urllib.request.urlopen(url) as response:
        image_data = response.read()
        image_stream = io.BytesIO(image_data)
        image = PIL.Image.open(image_stream)
        return image


def img_to_bytes(image_path):
    image = PIL.Image.open(image_path).convert(mode='RGB')
    buffer = io.BytesIO()
    image.save(buffer, format="JPEG")
    img_bytes = buffer.getvalue()
    image.close()
    return img_bytes


def format_user_prompt_with_im_history_and_system_conditioning(
    user_prompt, chat_history
) -> List[Dict[str, Union[List, str]]]:
    """
    Produce the resulting list that needs to go inside the processor. It handles the potential image(s), the history, and the system conditioning.
    """
    resulting_messages = copy.deepcopy(SYSTEM_PROMPT)
    resulting_images = []
    for resulting_message in resulting_messages:
        if resulting_message["role"] == "user":
            for content in resulting_message["content"]:
                if content["type"] == "image":
                    resulting_images.append(load_image_from_url(content["image"]))

    # Format history
    for turn in chat_history:
        if not resulting_messages or (
            resulting_messages and resulting_messages[-1]["role"] != "user"
        ):
            resulting_messages.append(
                {
                    "role": "user",
                    "content": [],
                }
            )

        if turn_is_pure_media(turn):
            media = turn[0][0]
            resulting_messages[-1]["content"].append({"type": "image"})
            resulting_images.append(PIL.Image.open(media))
        else:
            user_utterance, assistant_utterance = turn
            resulting_messages[-1]["content"].append(
                {"type": "text", "text": user_utterance.strip()}
            )
            resulting_messages.append(
                {
                    "role": "assistant",
                    "content": [{"type": "text", "text": user_utterance.strip()}],
                }
            )

    # Format current input
    if not user_prompt["files"]:
        resulting_messages.append(
            {
                "role": "user",
                "content": [{"type": "text", "text": user_prompt["text"]}],
            }
        )
    else:
        # Choosing to put the image first (i.e. before the text), but this is an arbiratrary choice.
        resulting_messages.append(
            {
                "role": "user",
                "content": [{"type": "image"}] * len(user_prompt["files"])
                + [{"type": "text", "text": user_prompt["text"]}],
            }
        )
        resulting_images.extend([PIL.Image.open(path) for path in user_prompt["files"]])

    return resulting_messages, resulting_images


def extract_images_from_msg_list(msg_list):
    all_images = []
    for msg in msg_list:
        for c_ in msg["content"]:
            if isinstance(c_, Image.Image):
                all_images.append(c_)
    return all_images


@spaces.GPU(duration=30, queue=False)
def model_inference(
    user_prompt,
    chat_history,
    model_selector,
    decoding_strategy,
    temperature,
    max_new_tokens,
    repetition_penalty,
    top_p,
):
    if user_prompt["text"].strip() == "" and not user_prompt["files"]:
        gr.Error("Please input a query and optionally an image(s).")

    if user_prompt["text"].strip() == "" and user_prompt["files"]:
        gr.Error("Please input a text query along with the image(s).")

    streamer = TextIteratorStreamer(
        PROCESSOR.tokenizer,
        skip_prompt=True,
        timeout=120.0,
    )

    generation_args = {
        "max_new_tokens": max_new_tokens,
        "repetition_penalty": repetition_penalty,
        "streamer": streamer,
    }

    assert decoding_strategy in [
        "Greedy",
        "Top P Sampling",
    ]
    if decoding_strategy == "Greedy":
        generation_args["do_sample"] = False
    elif decoding_strategy == "Top P Sampling":
        generation_args["temperature"] = temperature
        generation_args["do_sample"] = True
        generation_args["top_p"] = top_p

    # Creating model inputs
    (
        resulting_text,
        resulting_images,
    ) = format_user_prompt_with_im_history_and_system_conditioning(
        user_prompt=user_prompt,
        chat_history=chat_history,
    )
    prompt = PROCESSOR.apply_chat_template(resulting_text, add_generation_prompt=True)
    inputs = PROCESSOR(
        text=prompt,
        images=resulting_images if resulting_images else None,
        return_tensors="pt",
    )
    inputs = {k: v.to(DEVICE) for k, v in inputs.items()}
    generation_args.update(inputs)

    thread = Thread(
        target=MODELS[model_selector].generate,
        kwargs=generation_args,
    )
    thread.start()

    print("Start generating")
    acc_text = ""
    for text_token in streamer:
        time.sleep(0.01)
        acc_text += text_token
        if acc_text.endswith("<end_of_utterance>"):
            acc_text = acc_text[:-18]
        yield acc_text


FEATURES = datasets.Features(
    {
        "model_selector": datasets.Value("string"),
        "images": datasets.Sequence(datasets.Image(decode=True)),
        "conversation": datasets.Sequence({"User": datasets.Value("string"), "Assistant": datasets.Value("string")}),
        "decoding_strategy": datasets.Value("string"),
        "temperature": datasets.Value("float32"),
        "max_new_tokens": datasets.Value("int32"),
        "repetition_penalty": datasets.Value("float32"),
        "top_p": datasets.Value("int32"),
        }
    )


# Hyper-parameters for generation
max_new_tokens = gr.Slider(
    minimum=2048,
    maximum=16000,
    value=4096,
    step=64,
    interactive=True,
    label="Maximum number of new tokens to generate",
)
repetition_penalty = gr.Slider(
    minimum=0.01,
    maximum=5.0,
    value=1,
    step=0.01,
    interactive=True,
    label="Repetition penalty",
    info="1.0 is equivalent to no penalty",
)
decoding_strategy = gr.Radio(
    [
        "Greedy",
        "Top P Sampling",
    ],
    value="Top P Sampling",
    label="Decoding strategy",
    interactive=True,
    info="Higher values are equivalent to sampling more low-probability tokens.",
)
temperature = gr.Slider(
    minimum=0.0,
    maximum=2.0,
    value=0.5,
    step=0.05,
    visible=True,
    interactive=True,
    label="Sampling temperature",
    info="Higher values will produce more diverse outputs.",
)
top_p = gr.Slider(
    minimum=0.01,
    maximum=0.99,
    value=0.9,
    step=0.01,
    visible=True,
    interactive=True,
    label="Top P",
    info="Higher values are equivalent to sampling more low-probability tokens.",
)


chatbot = gr.Chatbot(
    label="OpnGPT-4o-Chatty",
    avatar_images=[None, BOT_AVATAR],
    show_copy_button=True, 
    likeable=True, 
    layout="panel"
)

output=gr.Textbox(label="Prompt")

with gr.Blocks(
    fill_height=True,
    css=""".gradio-container .avatar-container {height: 40px width: 40px !important;} #duplicate-button {margin: auto; color: white; background: #f1a139; border-radius: 100vh; margin-top: 2px; margin-bottom: 2px;}""",
) as chat:

    gr.Markdown("# Image Chat, Image Generation, Image classification and Normal Chat")
    with gr.Row(elem_id="model_selector_row"):
        model_selector = gr.Dropdown(
            choices=MODELS.keys(),
            value=list(MODELS.keys())[0],
            interactive=True,
            show_label=False,
            container=False,
            label="Model",
            visible=False,
        )

    decoding_strategy.change(
        fn=lambda selection: gr.Slider(
            visible=(
                selection
                in [
                    "contrastive_sampling",
                    "beam_sampling",
                    "Top P Sampling",
                    "sampling_top_k",
                ]
            )
        ),
        inputs=decoding_strategy,
        outputs=temperature,
    )
    decoding_strategy.change(
        fn=lambda selection: gr.Slider(visible=(selection in ["Top P Sampling"])),
        inputs=decoding_strategy,
        outputs=top_p,
    )

    gr.ChatInterface(
        fn=model_inference,
        chatbot=chatbot,
        examples=EXAMPLES,
        multimodal=True,
        cache_examples=False,
        additional_inputs=[
            model_selector,
            decoding_strategy,
            temperature,
            max_new_tokens,
            repetition_penalty,
            top_p,
        ],   
    )

with gr.Blocks() as voice:   
     with gr.Row():
        select = gr.Dropdown([ 'Mixtral 8x7B',
        'Llama 3 8B',
        'Mistral 7B v0.3',
        'Phi 3 mini',
    ],
    value="Mixtral 8x7B",
    label="Model"
    )
        seed = gr.Slider(
        label="Seed",
        minimum=0,
        maximum=999999,
        step=1,
        value=0,
        visible=False
        )
        input = gr.Audio(label="User", sources="microphone", type="filepath", waveform_options=False)
        output = gr.Audio(label="AI", type="filepath",
                        interactive=False,
                        autoplay=True,
                        elem_classes="audio")
        gr.Interface(
            fn=respond, 
            inputs=[input],
                outputs=[output], api_name="translate", live=True)

with gr.Blocks() as livechat:  
    gr.Interface(
        fn=videochat,
        inputs=[gr.Image(type="pil",sources="webcam", label="Upload Image"), gr.Textbox(label="Prompt", value="what he is doing")],
        outputs=gr.Textbox(label="Answer")
    )

with gr.Blocks() as god:
    gr.HTML("<iframe src='https://kingnish-sdxl-flash.hf.space' width='100%' height='1200px' style='border-radius: 8px;'></iframe>")

with gr.Blocks() as instant:
    gr.HTML("<iframe src='https://kingnish-instant-image.hf.space' width='100%' height='1000px' style='border-radius: 8px;'></iframe>")

with gr.Blocks() as image:
    gr.Markdown("""### More models are coming""")
    gr.TabbedInterface([ god, instant], ['Powerful🖼️','Instant🖼️'])    




with gr.Blocks() as instant2:
    gr.HTML("<iframe src='https://kingnish-instant-video.hf.space' width='100%' height='2000px' style='border-radius: 8px;'></iframe>")

with gr.Blocks() as video:
    gr.Markdown("""More Models are coming""")
    gr.TabbedInterface([ instant2], ['Instant🎥'])   

with gr.Blocks(theme=theme, title="OpenGPT 4o DEMO") as demo:
    gr.Markdown("# OpenGPT 4o")
    gr.TabbedInterface([chat, voice, livechat, image, video], ['💬 SuperChat','🗣️ Voice Chat','📸 Live Chat', '🖼️ Image Engine', '🎥 Video Engine'])

demo.queue(max_size=300)
demo.launch()