myapp.py

from flask import Flask, request, jsonify
from flask_cors import CORS
import os
from huggingface_hub import InferenceClient
from io import BytesIO
from PIL import Image
import gradio as gr  # Import Gradio

# Initialize the Flask app
myapp = Flask(__name__)
CORS(myapp)  # Enable CORS for all routes

# Initialize the InferenceClient with your Hugging Face token
HF_TOKEN = os.environ.get("HF_TOKEN")  # Ensure to set your Hugging Face token in the environment
client = InferenceClient(token=HF_TOKEN)

# Function to generate an image from a text prompt
def generate_image(prompt, seed=1, model="prompthero/openjourney-v4"):
    try:
        # Generate the image using Hugging Face's inference API
        result_image = client.text_to_image(prompt=prompt, seed=seed, model=model)
        return result_image
    except Exception as e:
        print(f"Error generating image: {str(e)}")
        return None

# Gradio interface function
def gradio_interface(prompt, seed, model_name):
    image = generate_image(prompt, seed, model_name)

    if image:
        img_byte_arr = BytesIO()
        image.save(img_byte_arr, format='PNG')  # Convert the image to PNG
        img_byte_arr.seek(0)  # Move to the start of the byte stream
        return img_byte_arr  # Return the image as bytes
    else:
        return "Failed to generate image"

# Set up the Gradio interface
gr.Interface(
    fn=gradio_interface,
    inputs=[
        gr.Textbox(label="Prompt", placeholder="Enter a text prompt", lines=2),
        gr.Number(label="Seed", value=1, precision=0),
        gr.Textbox(label="Model Name", value="prompthero/openjourney-v4", placeholder="Enter model name"),
    ],
    outputs="image",
    title="Image Generation with Hugging Face",
    description="Enter a prompt, seed, and model name to generate an image."
).launch()  # Launch the Gradio interface

# Add this block to make sure your app runs when called
if __name__ == "__main__":
    myapp.run(host='0.0.0.0', port=7860)  # Run directly if needed for testing