Spaces:
Runtime error
Runtime error
File size: 3,362 Bytes
809b1fe ad77051 f76e239 4dd6ed2 1e13411 4dd6ed2 1e13411 4dd6ed2 ad77051 1e13411 8fd1381 4dd6ed2 8fd1381 ad77051 8fd1381 ad77051 8fd1381 ad77051 8fd1381 f76e239 8fd1381 ad77051 27cad67 ad77051 1e13411 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 |
import gradio as gr
from gradio_client import Client
import matplotlib.pyplot as plt
import numpy as np
import io
from PIL import Image
import base64
import os
import requests
# Load Hugging Face token from environment variable
HF_TOKEN = os.getenv("HF_TOKEN")
if not HF_TOKEN:
raise ValueError("Hugging Face token not found in environment variables.")
def get_dynamic_endpoint():
"""
Fetch the dynamic endpoint using the Hugging Face API.
Returns:
str: The current dynamic endpoint.
"""
api_url = "https://api.huggingface.co/space/duchaba/friendly-text-moderation"
headers = {"Authorization": f"Bearer {HF_TOKEN}"}
response = requests.get(api_url, headers=headers)
response.raise_for_status() # Raise an error for bad status codes
# Extract the endpoint from the response
data = response.json()
endpoint = data.get("url")
if not endpoint:
raise ValueError("Endpoint URL not found in the response.")
return endpoint
def moderate_text(text, safer_value):
"""
Moderate the given text using the Hugging Face API.
Args:
text (str): The text to be moderated.
safer_value (float): The safer value to be used for moderation.
Returns:
tuple: A tuple containing the moderation result and the generated image.
"""
try:
# Fetch the dynamic endpoint
dynamic_endpoint = get_dynamic_endpoint()
# Initialize the client with the dynamic endpoint
client = Client(dynamic_endpoint, hf_token=HF_TOKEN)
result = client.predict(
text,
safer_value,
api_name="/censor_me"
)
# Ensure the result contains the expected data
base64_data = result.get('plot', '').split(',')[1] if 'plot' in result else None
if not base64_data:
raise ValueError("Expected plot data not found in the result.")
# Decode base64 to bytes
img_data = base64.b64decode(base64_data)
# Convert bytes to PIL Image
img = Image.open(io.BytesIO(img_data))
return result, img
except Exception as e:
# Log the error for debugging purposes
print(f"Error occurred: {e}")
return str(e), None
# Define the Gradio interface
demo = gr.Interface(
fn=moderate_text,
inputs=[
gr.Textbox(label="Enter Text:", placeholder="Type your text here...", lines=5),
gr.Slider(minimum=0.005, maximum=0.1, value=0.005, label="Personalize Safer Value: (larger value is less safe)")
],
outputs=[gr.Textbox(label="Moderated Text:", lines=5), gr.Image(type="pil", label="Moderation Pie Chart")],
title="Friendly Text Moderator",
description="Enter text to be moderated and adjust the safer value to see how it affects the moderation."
)
# Customize the CSS
custom_css = """
body {
background-color: #f5f5f5;
font-family: Arial, sans-serif;
}
.gradio-container {
max-width: 800px;
margin: auto;
padding: 20px;
background-color: white;
border: 1px solid #ddd;
border-radius: 8px;
box-shadow: 0 2px 10px rgba(0, 0, 0, 0.1);
}
.gr-button {
background-color: #4CAF50;
color: white;
}
.gr-button:hover {
background-color: #45a049;
}
"""
# Add the custom CSS to the Gradio app
demo.css = custom_css
# Launch the app
demo.launch() |