app.py

import gradio as gr
from huggingface_hub import InferenceClient
from PIL import Image
import time
import os
import base64
from io import BytesIO

HF_TOKEN = os.environ.get("HF_TOKEN")

if not HF_TOKEN:
    HF_TOKEN_ERROR = "Hugging Face API token (HF_TOKEN) not found.  Please set it as an environment variable or Gradio secret."
else:
    HF_TOKEN_ERROR = None

client = InferenceClient(token=HF_TOKEN)
PROMPT_IMPROVER_MODEL = "HuggingFaceH4/zephyr-7b-beta"

def improve_prompt(original_prompt):
    if HF_TOKEN_ERROR:
        raise gr.Error(HF_TOKEN_ERROR)

    try:
        system_prompt = "You are a helpful assistant that improves text prompts for image generation models.  Make the prompt more descriptive, detailed, and artistic, while keeping the user's original intent."
        prompt_for_llm = f"""<|system|>
{system_prompt}</s>
<|user|>
Improve this prompt: {original_prompt}
</s>
<|assistant|>
"""
        improved_prompt = client.text_generation(
            prompt=prompt_for_llm,
            model=PROMPT_IMPROVER_MODEL,
            max_new_tokens=128,
            temperature=0.7,
            top_p=0.9,
            repetition_penalty=1.2,
            stop_sequences=["</s>"],

        )

        return improved_prompt.strip()

    except Exception as e:
        print(f"Error improving prompt: {e}")
        return original_prompt


def generate_image(prompt, progress=gr.Progress()):
    if HF_TOKEN_ERROR:
        raise gr.Error(HF_TOKEN_ERROR)

    progress(0, desc="Improving prompt...")
    improved_prompt = improve_prompt(prompt)

    progress(0.2, desc="Sending request to Hugging Face...")
    try:
        image = client.text_to_image(improved_prompt, model="black-forest-labs/FLUX.1-schnell")

        if not isinstance(image, Image.Image):
             raise Exception(f"Expected a PIL Image, but got: {type(image)}")

        progress(0.8, desc="Processing image...")
        time.sleep(0.5)
        progress(1.0, desc="Done!")
        return image, improved_prompt
    except Exception as e:
        if "rate limit" in str(e).lower():
            error_message = f"Rate limit exceeded. Please try again later.  Error: {e}"
        else:
            error_message = f"An error occurred: {e}"
        raise gr.Error(error_message)

def pil_to_base64(img):
    buffered = BytesIO()
    img.save(buffered, format="PNG")
    img_str = base64.b64encode(buffered.getvalue()).decode()
    return f"data:image/png;base64,{img_str}"

css = """
.container {
    max-width: 800px;
    margin: auto;
    padding: 20px;
    border: 1px solid #ddd;
    border-radius: 10px;
    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
}
.title {
    text-align: center;
    font-size: 2.5em;
    margin-bottom: 0.5em;
    color: #333;
    font-family: 'Arial', sans-serif;
}
.description {
    text-align: center;
    font-size: 1.1em;
    margin-bottom: 1.5em;
    color: #555;
}
.input-section, .output-section {
    margin-bottom: 1.5em;
}
.output-section img {
    display: block;
    margin: auto;
    max-width: 100%;
    height: auto;
    border-radius: 8px;
    box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
}

@keyframes fadeIn {
    from { opacity: 0; transform: translateY(20px); }
    to   { opacity: 1; transform: translateY(0); }
}
.output-section.animate img {
    animation: fadeIn 0.8s ease-out;
}

.submit-button {
    display: block;
    margin: auto;
    padding: 10px 20px;
    font-size: 1.1em;
    color: white;
    background-color: #4CAF50;
    border: none;
    border-radius: 5px;
    cursor: pointer;
    transition: background-color 0.3s ease;
}
.submit-button:hover {
    background-color: #367c39;
}

.error-message {
    color: red;
    text-align: center;
    margin-top: 1em;
    font-weight: bold;
}
label{
    font-weight: bold;
    display: block;
    margin-bottom: 0.5em;
}

.improved-prompt-display {
    margin-top: 10px;
    padding: 8px;
    border: 1px solid #ccc;
    border-radius: 4px;
    background-color: #f9f9f9;
    font-style: italic;
    color: #444;
}
.download-link {
    display: block;
    text-align: center;
    margin-top: 10px;
    color: #4CAF50;
    text-decoration: none;
    font-weight: bold;
}

.download-link:hover{
  text-decoration: underline;
}
"""


with gr.Blocks(css=css) as demo:  
    gr.Markdown(  
        """  
        # Xylaria Iris Image Generator  
        """,  
        elem_classes="title"  
    )  

    with gr.Row():  
        with gr.Column():  
            with gr.Group(elem_classes="input-section"):  
                prompt_input = gr.Textbox(label="Enter your prompt", placeholder="e.g., A cat", lines=3)  
                generate_button = gr.Button("Generate Image", elem_classes="submit-button")  
        with gr.Column():  
            with gr.Group(elem_classes="output-section") as output_group:  
                image_output = gr.Image(label="Generated Image", interactive=False)  

    def on_generate_click(prompt):  
        output_group.elem_classes = ["output-section", "animate"]  
        image, _ = generate_image(prompt)  # Ignore the improved prompt  
        output_group.elem_classes = ["output-section"]  
        image_b64 = pil_to_base64(image)  
        download_html = f'<a class="download-link" href="{image_b64}" download="generated_image.png">Download Image</a>'  

        return image, download_html  # No improved prompt to return  

    generate_button.click(on_generate_click, inputs=prompt_input, outputs=[image_output, download_link])  
    prompt_input.submit(on_generate_click, inputs=prompt_input, outputs=[image_output, download_link])  

    gr.Examples(  
        [["A dog"],  
         ["A house on a hill"],  
         ["A spaceship"]],  
        inputs=prompt_input  
    )  

if __name__ == "__main__":  
    demo.queue().launch()