app.py

# Import necessary libraries
import os
import io
import IPython.display 
from IPython.display import Image, display, HTML
from PIL import Image
import base64
import requests
import json
from dotenv import load_dotenv, find_dotenv

# Load environment variables
load_dotenv(find_dotenv())
hf_api_key = os.getenv('HF_API_KEY')
endpoint_url = os.getenv('HF_API_TTI_BASE')

# Function to get image completion from the API
def get_completion(inputs, parameters=None, endpoint_url=endpoint_url):
    headers = {
        "Authorization": f"Bearer {hf_api_key}",
        "Content-Type": "application/json"
    }
    data = {"inputs": inputs}
    if parameters is not None:
        data.update({"parameters": parameters})
    response = requests.post(endpoint_url, headers=headers, data=json.dumps(data))
    if response.status_code != 200:
        raise Exception(f"Request failed: {response.status_code} - {response.text}")
    return response.content

# Function to convert base64 or binary data to PIL image
def base64_to_pil(img_data):
    if isinstance(img_data, bytes):
        byte_stream = io.BytesIO(img_data)
    else:
        base64_decoded = base64.b64decode(img_data)
        byte_stream = io.BytesIO(base64_decoded)
    pil_image = Image.open(byte_stream)
    return pil_image

import gradio as gr

# Gradio interface function
def generate(prompt):
    output = get_completion(prompt)
    result_image = base64_to_pil(output)
    return result_image

# Ensure all Gradio interfaces are closed before launching a new one
gr.close_all()

# Create the Gradio interface
demo = gr.Interface(
    fn=generate,
    inputs=[gr.Textbox(label="Your prompt")],
    outputs=[gr.Image(label="Result")],
    title="Image Generation with Stable Diffusion",
    description="Generate any image with Stable Diffusion.",
    allow_flagging="never",
    examples=[
        ["a dog in a park"],
        ["Astronaut riding a horse"]
    ]
)

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