Update app.py

app.py

@@ -1,64 +1,98 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+import requests
+import json
+import os
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# Configure the endpoint
+ENDPOINT_URL = os.environ.get("ENDPOINT_URL", "http://your-endpoint-url.com/predict")
 
+# Define the function to call your endpoint
+def check_safety(input_text):
+    if not input_text.strip():
+        return "Please enter some text to check"
+    
+    # Prepare the payload for your endpoint
+    payload = {
+        "inputs": input_text
+    }
+    
+    # Set headers for JSON content
+    headers = {
+        "Content-Type": "application/json"
+    }
+    
+    try:
+        # Make the request to your endpoint
+        response = requests.post(ENDPOINT_URL, json=payload, headers=headers, timeout=30)
+        
+        # Check if the request was successful
+        if response.status_code == 200:
+            result = response.json()
+            
+            # Format the result based on your endpoint's response format
+            is_safe = result.get("is_safe", False)
+            safety_result = result.get("safety_result", "No result received")
+            
+            if is_safe:
+                return f"✅ {safety_result}"
+            else:
+                return f"❌ {safety_result}"
+        else:
+            return f"Error: Request failed with status code {response.status_code}. Details: {response.text}"
+    except requests.exceptions.Timeout:
+        return "Error: Request timed out. The endpoint may be overloaded or unavailable."
+    except requests.exceptions.ConnectionError:
+        return "Error: Failed to connect to the endpoint. Please check the endpoint URL."
+    except Exception as e:
+        return f"Error: {str(e)}"
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+# Define the Gradio interface
+with gr.Blocks(title="Safety Content Classifier", css="footer {display: none !important}") as demo:
+    gr.Markdown(f"# Safety Content Classifier")
+    gr.Markdown(f"## Connected to external safety model endpoint")
+    
+    with gr.Accordion("About this demo", open=False):
+        gr.Markdown("""
+        This demo uses an external API endpoint to classify text based on safety policies.
+        It checks content against the following categories:
+        - Harassment
+        - Dangerous Content
+        - Hate Speech
+        - Sexually Explicit Information
+        
+        The model will respond with 'Safe' or 'Unsafe' followed by any violated categories.
+        """)
+    
+    with gr.Row():
+        with gr.Column():
+            input_text = gr.Textbox(
+                label="Enter text to check",
+                placeholder="Type here...",
+                lines=5
+            )
+            check_button = gr.Button("Check Safety", variant="primary")
+        
+        with gr.Column():
+            output = gr.Textbox(
+                label="Safety Result",
+                lines=5
+            )
+    
+    # Set up event handlers
+    check_button.click(fn=check_safety, inputs=input_text, outputs=output)
+    input_text.submit(fn=check_safety, inputs=input_text, outputs=output)
+    
+    # Example inputs
+    gr.Examples(
+        [
+            ["Hello, how are you today?"],
+            ["I love your work, it's amazing!"],
+            ["I want to learn how to make a bomb."],
+            ["I hate people from that country."],
+            ["Let's meet for coffee and discuss the project."],
+        ],
+        input_text
+    )
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+# Launch the app
+demo.launch()