Update app.py

app.py

@@ -8,7 +8,7 @@ from PIL import Image
 import base64
 
 # Load Hugging Face token from environment variable
-HF_TOKEN = os.getenv("HF_TOKEN", "your_default_hf_token")
+HF_TOKEN = os.getenv("HF_TOKEN", os.environ['API_TOKEN'])
 
 def get_dynamic_endpoint():
     """
@@ -17,16 +17,20 @@ def get_dynamic_endpoint():
     Returns:
         str: The current dynamic endpoint.
     """
-    api_url = "https://api.huggingface.co/space/duchaba/friendly-text-moderation"
-    headers = {"Authorization": f"Bearer {HF_TOKEN}"}
+    try:
+        api_url = "https://huggingface.co/api/spaces/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
+        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")
-    return endpoint
+        # Extract the endpoint from the response
+        data = response.json()
+        endpoint = data.get("url")
+        return endpoint
+    except Exception as e:
+        print(f"Error fetching dynamic endpoint: {e}")
+        return "https://default-endpoint.hf.space/--replicas/default/"
 
 # Fetch the dynamic endpoint
 dynamic_endpoint = get_dynamic_endpoint()
@@ -46,22 +50,26 @@ def moderate_text(text, safer_value):
         result (dict): The moderation result.
         img (PIL.Image): The moderation pie chart.
     """
-    result = client.predict(
-        text,
-        safer_value,
-        api_name="/censor_me"
-    )
-
-    # Example structure of the result
-    base64_data = result.get('plot', '').split(',')[1]
-
-    # 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
+    try:
+        result = client.predict(
+            text,
+            safer_value,
+            api_name="/censor_me"
+        )
+
+        # Example structure of the result
+        base64_data = result.get('plot', '').split(',')[1]
+
+        # 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:
+        print(f"Error during moderation: {e}")
+        return {"error": "Failed to moderate text"}, None
 
 # Define the Gradio interface
 demo = gr.Interface(