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import gradio as gr
import requests
import os
import time
# Environment variables for API details
API_TOKEN = os.getenv("HUGGINGFACE_API_TOKEN") # Fetching the API token from environment variable
# Function to query Hugging Face API
def query_huggingface_api(api_url, prompt):
headers = {"Authorization": f"Bearer {API_TOKEN}"}
data = {"inputs": prompt}
response = requests.post(api_url, headers=headers, json=data)
if response.status_code == 200:
return response.content, None # Return the image and no error
else:
return None, f"Error {response.status_code}: {response.text}" # Return None and the error message
# Gradio function for generating the image
def generate_image(api_url, prompt):
# Attempt to query the API with retry logic for loading models
for attempt in range(5): # Try up to 5 times
result, error = query_huggingface_api(f"https://api-inference.huggingface.co/models/{api_url}", prompt)
if result:
return result, None
elif "Model is currently loading" in error:
estimated_time = float(error.split("estimated_time\":")[1].split("}")[0]) # Extract estimated time from error message
time.sleep(estimated_time + 5) # Wait for the model to load, with an additional buffer time
else:
return None, error # Return the error if it's not a loading issue
return None, "Model is still loading after multiple attempts. Please try again later." # Final error if all attempts fail
# Create Gradio Blocks Interface
with gr.Blocks(theme="nevreal/blues") as demo:
gr.Markdown(
"""
# Text to Image Generator
Enter a text prompt, and the custom model will generate an image.
"""
)
with gr.Row():
with gr.Column():
text_input = gr.Textbox(
label="Enter your prompt",
placeholder="Type something here...",
value=""
)
model_input = gr.Textbox(
label="Model URL",
placeholder="Enter the model URL...",
value=""
)
generate_btn = gr.Button("Generate Image")
with gr.Column():
image_output = gr.Image(label="Generated Image")
error_output = gr.Textbox(label="Error", interactive=False)
# Define the action for the button
generate_btn.click(
fn=generate_image,
inputs=[model_input, text_input], # Pass both model URL and prompt
outputs=[image_output, error_output]
)
# Launch the Gradio Blocks WebUI
demo.launch(share=True, debug=True)
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