Initial Gradio demo upload

README.md

@@ -1,12 +1,39 @@
 ---
-title: Privacy Classifier Demo
-emoji: 🏢
-colorFrom: blue
+title: Privacy Classifier
+emoji: 🔒
+colorFrom: red
 colorTo: green
 sdk: gradio
-sdk_version: 6.5.1
+sdk_version: 4.44.0
 app_file: app.py
 pinned: false
+license: apache-2.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Privacy Classifier Demo
+
+Classify prompts to determine if they contain sensitive information that should stay local or if they're safe to send to cloud LLM services.
+
+## Classifications
+
+- **🔴 KEEP_LOCAL**: Contains PII, sensitive data, or private information
+- **🟢 ALLOW_CLOUD**: Safe to process with cloud-based AI services
+
+## Model
+
+This demo uses [jonmabe/privacy-classifier-electra](https://huggingface.co/jonmabe/privacy-classifier-electra), an ELECTRA-based classifier fine-tuned to detect sensitive information in prompts.
+
+## Use Cases
+
+- Privacy-aware prompt routing
+- Data loss prevention for LLM applications
+- Compliance with data protection regulations
+
+## Examples
+
+| Prompt | Classification |
+|--------|---------------|
+| "What is the capital of France?" | ALLOW_CLOUD |
+| "My SSN is 123-45-6789" | KEEP_LOCAL |
+| "Write me a poem about the ocean" | ALLOW_CLOUD |
+| "My password is hunter2" | KEEP_LOCAL |

app.py

@@ -0,0 +1,184 @@
+"""
+Privacy Classifier Demo - Classifies prompts as KEEP_LOCAL vs ALLOW_CLOUD
+"""
+
+import gradio as gr
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+# Model configuration
+MODEL_ID = "jonmabe/privacy-classifier-electra"
+# Model labels: 0=safe (ALLOW_CLOUD), 1=sensitive (KEEP_LOCAL)
+LABELS = ["ALLOW_CLOUD", "KEEP_LOCAL"]  # index 0=safe, index 1=sensitive
+
+# Load model and tokenizer
+print("Loading model...")
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_ID)
+model.eval()
+
+# Move to GPU if available
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = model.to(device)
+print(f"Model loaded on {device}")
+
+
+def classify_prompt(text: str) -> tuple[str, dict]:
+    """
+    Classify a prompt as KEEP_LOCAL or ALLOW_CLOUD.
+    
+    Returns:
+        - Classification label with confidence
+        - Dictionary of class probabilities for the label component
+    """
+    if not text.strip():
+        return "Please enter a prompt to classify.", {}
+    
+    # Tokenize
+    inputs = tokenizer(
+        text,
+        return_tensors="pt",
+        truncation=True,
+        max_length=512,
+        padding=True
+    )
+    inputs = {k: v.to(device) for k, v in inputs.items()}
+    
+    # Inference
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        probs = torch.softmax(logits, dim=-1)[0]
+    
+    # Get prediction
+    pred_idx = probs.argmax().item()
+    pred_label = LABELS[pred_idx]
+    confidence = probs[pred_idx].item()
+    
+    # Create probability dict for label component
+    prob_dict = {label: float(probs[i]) for i, label in enumerate(LABELS)}
+    
+    return f"{pred_label} ({confidence:.1%} confidence)", prob_dict
+
+
+def get_color_for_label(label: str) -> str:
+    """Return color based on classification."""
+    if "KEEP_LOCAL" in label:
+        return "red"
+    elif "ALLOW_CLOUD" in label:
+        return "green"
+    return "gray"
+
+
+# Example prompts
+EXAMPLES = [
+    ["What is the capital of France?"],
+    ["My social security number is 123-45-6789, can you help me file taxes?"],
+    ["Write me a poem about the ocean."],
+    ["Here's my password: hunter2, please remember it."],
+    ["Explain how photosynthesis works."],
+    ["My credit card number is 4111-1111-1111-1111, check if it's valid."],
+    ["What are some good restaurants in Seattle?"],
+    ["My medical records show I have diabetes. What should I eat?"],
+    ["Translate 'hello world' to Spanish."],
+    ["My home address is 123 Main St, Anytown USA. Send me a pizza."],
+    ["How do I sort a list in Python?"],
+    ["My employee ID is E12345 and my salary is $85,000."],
+]
+
+
+# Custom CSS for styling
+css = """
+.keep-local {
+    background: linear-gradient(135deg, #ff6b6b 0%, #ee5a5a 100%) !important;
+    color: white !important;
+    font-weight: bold !important;
+}
+.allow-cloud {
+    background: linear-gradient(135deg, #51cf66 0%, #40c057 100%) !important;
+    color: white !important;
+    font-weight: bold !important;
+}
+.result-box {
+    font-size: 1.2em;
+    padding: 20px;
+    border-radius: 10px;
+    text-align: center;
+}
+"""
+
+# Create Gradio interface
+with gr.Blocks(css=css, title="Privacy Classifier") as demo:
+    gr.Markdown("""
+    # 🔒 Privacy Classifier
+    
+    Classify prompts to determine if they contain sensitive information that should stay local
+    or if they're safe to send to cloud LLM services.
+    
+    - **🔴 KEEP_LOCAL**: Contains PII, sensitive data, or private information
+    - **🟢 ALLOW_CLOUD**: Safe to process with cloud-based AI services
+    
+    This model helps route requests in privacy-aware AI systems.
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=2):
+            input_text = gr.Textbox(
+                label="Enter your prompt",
+                placeholder="Type a prompt to classify...",
+                lines=3,
+            )
+            classify_btn = gr.Button("🔍 Classify", variant="primary", size="lg")
+        
+        with gr.Column(scale=1):
+            result_label = gr.Textbox(
+                label="Classification Result",
+                interactive=False,
+                lines=2,
+            )
+            confidence_chart = gr.Label(
+                label="Confidence Scores",
+                num_top_classes=2,
+            )
+    
+    gr.Markdown("### 📝 Example Prompts")
+    gr.Examples(
+        examples=EXAMPLES,
+        inputs=input_text,
+        outputs=[result_label, confidence_chart],
+        fn=classify_prompt,
+        cache_examples=False,
+    )
+    
+    # Event handlers
+    classify_btn.click(
+        fn=classify_prompt,
+        inputs=input_text,
+        outputs=[result_label, confidence_chart],
+    )
+    
+    input_text.submit(
+        fn=classify_prompt,
+        inputs=input_text,
+        outputs=[result_label, confidence_chart],
+    )
+    
+    gr.Markdown("""
+    ---
+    ### About This Model
+    
+    **Model**: [jonmabe/privacy-classifier-electra](https://huggingface.co/jonmabe/privacy-classifier-electra)
+    
+    This is an ELECTRA-based classifier fine-tuned to detect sensitive information in prompts.
+    Use cases include:
+    - Privacy-aware prompt routing
+    - Data loss prevention for LLM applications
+    - Compliance with data protection regulations
+    
+    ⚠️ **Disclaimer**: This model is for demonstration purposes. Always verify classifications
+    for production use cases involving sensitive data.
+    """)
+
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio>=4.0.0
+torch>=2.0.0
+transformers>=4.35.0
+accelerate>=0.24.0