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import gradio as gr
import requests
import io
from io import BytesIO
import os
from PIL import Image
API_URL = "https://api-inference.huggingface.co/models/Kvikontent/midjourney-v6"
api_key = os.environ.get('api_token')
headers = {"Authorization": f"Bearer {api_key}"}
# Define custom Exception class for better error handling
class QueryError(Exception):
pass
def query(payload):
try:
# Make sure we have valid JSON data before sending the request
assert type(payload) == dict
# Send the POST request to the API URL
response = requests.post(API_URL, headers=headers, json=payload)
# Check if the status code indicates success (HTTP Status Code 2xx)
if not str(response.status_code).startswith("2"):
raise QueryError(f"Query failed! Response status code was '{response.status_code}'")
else:
# Return the raw bytes from the response object
return response.content
except AssertionError:
print("Invalid Payload Error: Please provide a dictionary.")
except RequestException as e:
print("Request Failed: ", e)
except ConnectionError as ce:
print("Connection Error: Unable to connect to the API.", ce)
except Timeout as t:
print("Timeout Error: Request timed out while trying to reach the API.", t)
except TooManyRedirects as tmr:
print("Too Many Redirects Error: Exceeded maximum number of redirects.", tmr)
except HTTPError as he:
print("HTTP Error: Invalid HTTP response.", he)
except QueryError as qe:
print(qe)
except Exception as ex:
print("Unknown Error occurred: ", ex)
def generate_image_from_prompt(prompt_text):
gr.Info("Image generation started")
image_bytes = query({"inputs": prompt_text})
img = BytesIO(image_bytes) # Convert to BytesIO stream
pil_img = Image.open(img) # Open the image using PIL library
return pil_img # Return the converted PIL image
title = "Midjourney V6 Demo 🎨"
description = "This app uses Hugging Face AI model to generate an image based on the provided text prompt 🖼."
input_prompt = gr.Textbox(label="Enter Prompt 📝", placeholder="E.g. 'Astronaut riding a horse'")
output_generated_image = gr.Image(label="Generated Image")
iface = gr.Interface(
fn=generate_image_from_prompt,
inputs=input_prompt,
outputs=output_generated_image,
title=title,
description=description,
theme="soft"
)
iface.launch() |