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import gradio as gr
import os
import io
import requests, json
from PIL import Image
import base64
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv()) # read local .env file
hf_api_key = os.environ['HF_API_KEY']
# Load the translation model (Turkish to English)
API_URL = "https://api-inference.huggingface.co/models/Helsinki-NLP/opus-mt-tr-en"
headers = {
"Authorization": f"Bearer {hf_api_key}",
"Content-Type": "application/json"
}
# Text-to-image endpoint
def get_completion(inputs, parameters=None, ENDPOINT_URL=os.environ['HF_API_TTI_STABILITY_AI']):
data = {"inputs": inputs}
if parameters is not None:
data.update({"parameters": parameters})
response = requests.post(ENDPOINT_URL, headers=headers, data=json.dumps(data))
# Check the content type of the response
content_type = response.headers.get('Content-Type', '')
print(content_type)
if 'application/json' in content_type:
return json.loads(response.content.decode("utf-8"))
elif 'image/' in content_type:
return response.content # return raw image data
response.raise_for_status() # raise an error for unexpected content types
# A helper function to convert the PIL image to base64
def base64_to_pil(img_base64):
base64_decoded = base64.b64decode(img_base64)
byte_stream = io.BytesIO(base64_decoded)
pil_image = Image.open(byte_stream)
return pil_image
def query(payload):
response = requests.post(API_URL, headers=headers, json=payload)
return response.json()
# Translation function
def translate_to_english(text):
try:
# Translate the input from Turkish to English
translation = query({"inputs": text})
print(translation)
translated_text = translation[0]['translation_text']
return translated_text
except Exception as e:
print(f"Translation error: {e}")
return text # If translation fails, return original text
# Main generation function with translation
def generate(prompt, negative_prompt, steps, guidance, width, height):
# Translate the prompt to English if it's in Turkish
translated_prompt = translate_to_english(prompt)
print(f"Translated Prompt: {translated_prompt}")
params = {
"negative_prompt": negative_prompt,
"num_inference_steps": steps,
"guidance_scale": guidance,
"width": width,
"height": height
}
output = get_completion(translated_prompt, params)
# Check if the output is an image (bytes) or JSON (dict)
if isinstance(output, dict):
raise ValueError("Expected an image but received JSON: {}".format(output))
# If output is raw image data, convert it to a PIL image
result_image = Image.open(io.BytesIO(output))
return (translated_prompt, result_image)
with gr.Blocks() as demo:
gr.Markdown("## Image Generation with Turkish Inputs")
gr.Markdown("### [`stabilityai/stable-diffusion-xl-base-1.0`](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) and [`Helsinki-NLP/opus-mt-tr-en`](https://huggingface.co/Helsinki-NLP/opus-mt-tr-en) models work under the hood!")
with gr.Row():
with gr.Column(scale=4):
prompt = gr.Textbox(label="Your prompt (in Turkish or English)") # Accept Turkish or English input
with gr.Column(scale=1, min_width=50):
btn = gr.Button("Submit")
with gr.Accordion("Advanced options", open=False):
negative_prompt = gr.Textbox(label="Negative prompt")
with gr.Row():
with gr.Column():
steps = gr.Slider(label="Inference Steps", minimum=1, maximum=100, value=50,
info="In how many steps will the denoiser denoise the image?")
guidance = gr.Slider(label="Guidance Scale", minimum=1, maximum=20, value=7,
info="Controls how much the text prompt influences the result")
with gr.Column():
width = gr.Slider(label="Width", minimum=64, maximum=1024, step=64, value=512)
height = gr.Slider(label="Height", minimum=64, maximum=1024, step=64, value=512)
translated_text = gr.Textbox(label="Translated text")
output = gr.Image(label="Result")
btn.click(fn=generate, inputs=[prompt, negative_prompt, steps, guidance, width, height], outputs=[translated_text, output])
gr.close_all()
demo.launch(share=True) |