app.py

import torch
from diffusers import DiffusionPipeline
import gradio as gr
import numpy as np
import openai
import os
import spaces
import base64

# Setup logging
# logging.basicConfig(level=logging.DEBUG)
# logger = logging.getLogger(__name__)

# Retrieve the OpenAI API key from the environment
API_KEY = os.getenv('OPEN_AI_API_KEY')

DESCRIPTION = '''
<div>
<h1 style="text-align: center;">Book-Reader</h1>
<p style="text-align: center;">This contains a Stable Diffusor from <a href="https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0"><b>stabilityai/stable-diffusion-xl-base-1.0</b></a></p>
<p style="text-align: center;">For Instructions on how to use the models <a href="https://huggingface.co/spaces/sandz7/chimera/blob/main/README.md"><b>view this</b></a></p>
</div>
'''

# load both base and refiner
base = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, use_safetensors=True, variant="fp16").to("cuda:0")
refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0",
                                            text_encoder_2=base.text_encoder_2,
                                            vae=base.vae,
                                            torch_dtype=torch.float16,
                                            use_safetensor=True,
                                            variant="fp16").to("cuda:0")

chat_mode = {}

def encode_image(image_path):
    chat_mode["the_mode"] = "diffusing"
    with open(image_path, "rb") as image_file:
        return base64.b64encode(image_file.read()).decode('utf-8')

def generation(message, history):
    """
    Generates a response based on the input message and optionally an image.
    """
    global chat_mode
    image_path = None
    if "files" in message and message["files"]:
        if type(message["files"][-1]) == dict:
            image_path = message["files"][-1]["path"]
        else:
            image_path = message["files"][-1]
    else:
        for hist in history:
            if type(hist[0]) == tuple:
                image_path = hist[0][0]

    input_prompt = message if isinstance(message, str) else message.get("text", "")

    if image_path is None:
        chat_mode["mode"] = "text"
        client = openai.OpenAI(api_key=API_KEY)
        stream = client.chat.completions.create(
            model="gpt-3.5-turbo",
            messages=[{"role": "system", "content": "You are a helpful assistant called 'chimera'."},
                        {"role": "user", "content": input_prompt}],
            stream=True,
        )
        return stream
    else:
        chat_mode["mode"] = "image"
        base64_image = encode_image(image_path=image_path)
        client = openai.OpenAI(api_key=API_KEY)
        stream = client.chat.completions.create(
            model="gpt-4o",
            messages=[{"role": "system", "content": "You are a helpful assistant called 'chimera'."},
                        {"role": "user", "content": [
                            {"type": "text", "text": input_prompt},
                            {"type": "image_url", "image_url": {
                                "url": f"data:image/jpeg;base64,{base64_image}"
                            }}
                        ]}],
            stream=True,
        )
        return stream

# function to take input and generate text tokena
@spaces.GPU(duration=120)
def diffusing(prompt: str,
              n_steps: int,
              denoising: float):
    """
    Takes input, passes it into the pipeline, 
    get the top 5 scores, and ouput those scores into images
    """

    # Generate image based on text
    image_base = base(
        prompt=prompt,
        num_inference_steps=n_steps,
        denoising_end=denoising,
        output_type="latent"
    ).images

    image = refiner(
        prompt=prompt,
        num_inference_steps=n_steps,
        denoising_start=denoising,
        image=image_base
    ).images[0]
    
    return image
    
def check_cuda_availability():
    if torch.cuda.is_available():
        return f"GPU: {torch.cuda.get_device_name(0)}"
    else:
        return "No CUDA device found."
    
# Image created from diffusing
image_created = {}

@spaces.GPU(duration=120)
def bot_comms(message, history):
    """
    Handles communication between Gradio and the models.
    """

    # ensures message is a dictionary
    if not isinstance(message, dict):
        message = {"text": message}

    if message["text"] == "check cuda":
        yield check_cuda_availability()
        return
        
    buffer = ""
    gpt_outputs = []
    stream = generation(message, history)

    for chunk in stream:
        if chunk.choices[0].delta.content is not None:
            text = chunk.choices[0].delta.content
            if text:
                gpt_outputs.append(text)
                buffer += text
            yield "".join(gpt_outputs)

chat_input = gr.MultimodalTextbox(interactive=True, file_types=["images"], placeholder="Enter your question or upload an image.", show_label=False)

with gr.Blocks(fill_height=True) as demo:
    with gr.Row():
        # Diffusing
        with gr.Column():
            gr.Markdown(DESCRIPTION)
            image_prompt = gr.Textbox(label="Image Prompt")
            output_image = gr.Image(label="Generated Image")
            generate_image_button = gr.Button("Generate Image")
            # generate_image_button.click(fn=diffusing, inputs=image_prompt, outputs=output_image)
            with gr.Accordion(label="⚙️ Parameters", open=False):
                steps_slider = gr.Slider(
                    minimum=20,
                    maximum=100,
                    step=1,
                    value=40,
                    label="Number of Inference Steps"
                )
                denoising_slider = gr.Slider(
                    minimum=0.0,
                    maximum=1.0,
                    step=0.1,
                    value=0.8,
                    label="High Noise Fraction"
                )
            generate_image_button.click(
                fn=diffusing,
                inputs=[image_prompt, steps_slider, denoising_slider],
                outputs=output_image
            )
        with gr.Column():
            # GPT-3.5
            gr.Markdown('''
<div>
<h1 style="text-align: center;">Smart Reader</h1>
<p style="text-align: center;">This contains a Generative LLM from <a href="https://openai.com/"><b>Open AI</b></a> called GPT-3.5-Turbo and Vision.</p>
<p style="text-align: center;">For Instructions on how to use the models <a href="https://huggingface.co/spaces/sandz7/chimera/blob/main/README.md"><b>view this</b></a></p>
</div>
''')
            chat = gr.ChatInterface(fn=bot_comms,
                                    multimodal=True,
                                    textbox=chat_input)

demo.launch()