Update app.py

app.py

@@ -5,75 +5,28 @@ import numpy as np
 import io
 from PIL import Image
 import base64
-import os
-import requests
-from tenacity import retry, wait_exponential, stop_after_attempt
 
-# 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.")
+#client = Client("https://duchaba-friendly-text-moderation.hf.space/--replicas/gffry/")
+client = Client("https://duchaba-friendly-text-moderation.hf.space/--replicas/6rx2j/")
 
-@retry(wait=wait_exponential(multiplier=1, min=4, max=10), stop=stop_after_attempt(5))
-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"
-        )
+    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.")
+    # Example structure of the result
+    base64_data = result.get('plot', '').split(',')[1]
 
-        # Decode base64 to bytes
-        img_data = base64.b64decode(base64_data)
+    # Decode base64 to bytes
+    img_data = base64.b64decode(base64_data)
 
-        # Convert bytes to PIL Image
-        img = Image.open(io.BytesIO(img_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
+    return result, img
 
 # Define the Gradio interface
 demo = gr.Interface(
@@ -84,7 +37,8 @@ demo = gr.Interface(
     ],
     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."
+    description="Enter text to be moderated and adjust the safer value to see how it affects the moderation.",
+    theme="compact"
 )
 
 # Customize the CSS