Spaces:
Runtime error
Runtime error
| 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/kviimager2.0" | |
| api_key = os.environ.get('API_KEY') | |
| 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): | |
| 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 = "KVIImager 2.0 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. 'A peaceful garden with a small cottage'") | |
| 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 | |
| ) | |
| iface.launch() |