app.py

import gradio as gr
import requests
import io
import random
import os
import time
import json
import base64
from io import BytesIO
from datetime import datetime
from PIL import Image
from mistralai import Mistral
from deep_translator import GoogleTranslator
import json
from theme import theme
from fastapi import FastAPI

app = FastAPI()

# Based on a project by Nymbo

API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-dev"
API_TOKEN = os.getenv("HF_READ_TOKEN")
headers = {"Authorization": f"Bearer {API_TOKEN}"}
timeout = 100

api_key = os.getenv("MISTRAL_API_KEY")
Mistralclient = Mistral(api_key=api_key)

# Function to query the API and return the generated image
def query(prompt, is_negative=False, steps=35, cfg_scale=7, sampler="DPM++ 2M Karras", seed=-1, strength=0.7, width=1024, height=1024):
    if prompt == "" or prompt is None:
        return None

    key = random.randint(0, 999)
    
    API_TOKEN = random.choice([os.getenv("HF_READ_TOKEN")])
    headers = {"Authorization": f"Bearer {API_TOKEN}"}
    
    # Translate the prompt from Russian to English if necessary
    prompt = GoogleTranslator(source='ru', target='en').translate(prompt)
    print(f'\033[1mGeneration {key} translation:\033[0m {prompt}')

    # Add some extra flair to the prompt
    prompt = f"{prompt} | ultra detail, ultra elaboration, ultra quality, perfect."
    print(f'\033[1mGeneration {key}:\033[0m {prompt}')

    # If seed is -1, generate a random seed and use it
    if seed == -1:
        seed = random.randint(1, 1000000000)
    
    # Prepare the payload for the API call, including width and height
    payload = {
        "inputs": prompt,
        "is_negative": is_negative,
        "steps": steps,
        "cfg_scale": cfg_scale,
        "seed": seed if seed != -1 else random.randint(1, 1000000000),
        "strength": strength,
        "parameters": {
            "width": width,  # Pass the width to the API
            "height": height  # Pass the height to the API
        }
    }

    # Send the request to the API and handle the response
    response = requests.post(API_URL, headers=headers, json=payload, timeout=timeout)
    if response.status_code != 200:
        print(f"Error: Failed to get image. Response status: {response.status_code}")
        print(f"Response content: {response.text}")
        if response.status_code == 503:
            raise gr.Error(f"{response.status_code} : The model is being loaded")
        raise gr.Error(f"{response.status_code}")
    
    try:
        # Convert the response content into an image
        image_bytes = response.content
        image = Image.open(io.BytesIO(image_bytes))
        print(f'\033[1mGeneration {key} completed!\033[0m ({prompt})')
        return image
    except Exception as e:
        print(f"Error when trying to open the image: {e}")
        return None

def encode_image(image_path):
    """Encode the image to base64."""
    try:
        # Open the image file
        image = Image.open(image_path).convert("RGB")

        # Resize the image to a height of 512 while maintaining the aspect ratio
        base_height = 512
        h_percent = (base_height / float(image.size[1]))
        w_size = int((float(image.size[0]) * float(h_percent)))
        image = image.resize((w_size, base_height), Image.LANCZOS)

        # Convert the image to a byte stream
        buffered = BytesIO()
        image.save(buffered, format="JPEG")
        img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")

        return img_str
    except FileNotFoundError:
        print(f"Error: The file {image_path} was not found.")
        return None
    except Exception as e:  # Add generic exception handling
        print(f"Error: {e}")
        return None

def feifeichat(image):
    try:
        model = "pixtral-large-2411"
        # Define the messages for the chat
        base64_image = encode_image(image)
        messages = [{
            "role":
            "user",
            "content": [
                {
                    "type": "text",
                    "text": "Please provide a detailed description of this photo"
                },
                {
                    "type": "image_url",
                    "image_url": f"data:image/jpeg;base64,{base64_image}" 
                },
            ],
            "stream": False,
        }]
    
        partial_message = ""
        for chunk in Mistralclient.chat.stream(model=model, messages=messages):
            if chunk.data.choices[0].delta.content is not None:
                partial_message = partial_message + chunk.data.choices[
                    0].delta.content
                yield partial_message
    except Exception as e:  # Adding generic exception handling
        print(f"Error: {e}")
        return "Please upload a photo"        

# CSS to style the app
css = """
.gradio-container {background-color: MediumAquaMarine}
#app-container {
    max-width: 930px;
    margin-left: auto;
    margin-right: auto;
}
footer {
    visibility: hidden;
}
"""

examples = [
    "a beautiful woman with blonde hair and blue eyes",
    "a beautiful woman with brown hair and grey eyes",
    "a beautiful woman with black hair and brown eyes",
]

# Build the Gradio UI with Blocks
with gr.Blocks(theme=theme, css=css) as app:
    # Add a title to the app
    gr.HTML("<center><h1>FLUX.1-Dev</h1></center>")
    with gr.Tabs() as tabs:
        with gr.TabItem(label="🖼 Image To Prompt 📄", visible=True):
            with gr.Row():
                with gr.Column():
                    input_img = gr.Image(label="Input Picture 🖼️",height=320,type="filepath")
                    submit_btn = gr.Button(value="Submit", variant='primary')
                with gr.Column():
                    output_text = gr.Textbox(label="Flux Prompt ✍️", show_copy_button = True)
                    clr_button =gr.Button("Clear 🗑️ ",variant="primary", elem_id="clear_button")
                    clr_button.click(lambda: (None, None), None, [input_img, output_text], queue=False, show_api=False)
        
        submit_btn.click(feifeichat, [input_img], [output_text])
        
        with gr.TabItem("✍️ Text to Image 🖼", visible=True):
            # Container for all the UI elements
            with gr.Column(elem_id="app-container"):
                # Add a text input for the main prompt
                with gr.Row():
                    with gr.Column(elem_id="prompt-container"):
                        with gr.Group():
                            with gr.Row():
                                text_prompt = gr.Textbox(label="Image Prompt ✍️", placeholder="Enter a prompt here", lines=2, show_copy_button = True, elem_id="prompt-text-input")
                            
                            # Accordion for advanced settings
                            with gr.Row():
                                with gr.Accordion("Advanced Settings", open=False):
                                    negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="What should not be in the image", value=" (((hands:-1.25))), physical-defects:2, unhealthy-deformed-joints:2, unhealthy-hands:2, out of frame, (((bad face))), (bad-image-v2-39000:1.3), (((out of frame))), deformed body features,  (((poor facial details))), (poorly drawn face:1.3), jpeg artifacts, (missing arms:1.1), (missing legs:1.1), (extra arms:1.2), (extra legs:1.2), [asymmetrical features], warped expressions, distorted eyes ", lines=3, elem_id="negative-prompt-text-input")
                                    with gr.Row():
                                        width = gr.Slider(label="Width", value=896, minimum=64, maximum=1216, step=32)
                                        height = gr.Slider(label="Height", value=1152, minimum=64, maximum=1216, step=32)
                                    steps = gr.Slider(label="Sampling steps", value=35, minimum=1, maximum=100, step=1)
                                    cfg = gr.Slider(label="CFG Scale", value=7, minimum=1, maximum=20, step=1)
                                    strength = gr.Slider(label="Strength", value=90, minimum=0, maximum=100, step=10)
                                    seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=1000000000, step=1) # Setting the seed to -1 will make it random
                                    method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ 2S a Karras", "DPM2 a Karras", "DPM2 Karras", "DPM++ SDE Karras", "DEIS", "LMS", "DPM Adaptive", "DPM++ 2M", "DPM2 Ancestral", "DPM++ S", "DPM++ SDE", "DDPM", "DPM Fast", "dpmpp_2s_ancestral", "Euler", "Euler CFG PP", "Euler a", "Euler Ancestral", "Euler+beta", "Heun", "Heun PP2", "DDIM", "LMS Karras", "PLMS", "UniPC", "UniPC BH2"])
                        
                # Add a button to trigger the image generation
                with gr.Row():
                    text_button = gr.Button("Generate Image", variant='primary', elem_id="gen-button")
                
                # Image output area to display the generated image
                with gr.Row():
                    image_output = gr.Image(type="pil", label="Image Output", show_share_button=False, format="png", elem_id="gallery")
                with gr.Row():    
                    clear_prompt =gr.Button("Clear 🗑️",variant="primary", elem_id="clear_button")
                    clear_prompt.click(lambda: (None, None), None, [text_prompt, image_output], queue=False, show_api=False)
                    
                with gr.Row():        
                    gr.Examples(
                        examples = examples,    
                        inputs = [text_prompt],    
                    )            
                
                # Bind the button to the query function with the added width and height inputs
                text_button.click(query, inputs=[text_prompt, negative_prompt, steps, cfg, method, seed, strength, width, height], outputs=image_output)

        with gr.Tab("ℹ️ Tips"):
            with gr.Row():
                with gr.Column():
                    gr.Markdown(
            """
            <div style="max-width: 650px; margin: 2rem auto; padding: 1rem; border-radius: 10px; background-color: #f0f0f0;">
                <h2 style="float: left; font-size: 1.5rem; margin-bottom: 1rem;">How to Use</h2>
                <ol style="padding-left: 1.5rem;">
                    <li>Add an image to generate a prompt, this is optional.</li>
                    <li>If using an image to prompt, copy the prompt and paste into the prompt on tab 2</li>
                    <li>Enter a detailed description of the image you want to create.</li>
                    <li>Adjust advanced settings if desired (tap to expand).</li>
                    <li>Tap "Generate Image" and wait for your creation!</li>
                </ol>
                <p style="margin-top: 1rem; font-style: italic;">Tip: Be specific in your description for best results!</p>
                <p style="margin-top: 1rem; font-style: italic;">*Note: Some LoRA models will not work every time (not sure why), refresh the page and try again</p>
                <p style="margin-top: 1rem; font-style: italic;">*I'm still playing around to try to sort the issue, feel free to let me know if you find a fix</p>
            </div>
            """
            )

app.queue(default_concurrency_limit=200, max_size=200)  # <-- Sets up a queue with default parameters
if __name__ == "__main__":
    # Launch the Gradio app
    app.launch(show_api=False, share=True)