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README.md

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----
-title: Ethics Review Classifier
-emoji: 💻
-colorFrom: pink
-colorTo: pink
-sdk: gradio
-sdk_version: 6.0.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Ethics Review Classifier
+emoji: 📋
+colorFrom: blue
+colorTo: green
+sdk: gradio
+sdk_version: 4.44.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+# Ethics Review Classifier
+
+An AI model to classify research proposal text against ethics review guidelines.
+
+## Usage
+
+### Web Interface
+Use the form above to test the model interactively.
+
+### API Access
+This Space provides a free API endpoint:
+
+```python
+from gradio_client import Client
+
+client = Client("JohnLicode/ethics-review-classifier")
+result = client.predict(
+    text="The general objective is to develop an AI system...",
+    guideline_id="1.1",
+    guideline_name="Objectives",
+    api_name="/predict"
+)
+print(result)
+```
+
+### cURL Example
+```bash
+curl -X POST https://johnlicode-ethics-review-classifier.hf.space/api/predict \
+  -H "Content-Type: application/json" \
+  -d '{"data": ["Your text here", "1.1", "Objectives"]}'
+```
+
+## Labels
+- **ADDRESSED**: Text adequately addresses the guideline
+- **NEEDS_REVISION**: Text needs improvement or doesn't address the guideline

app.py

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+import gradio as gr
+from transformers import pipeline
+import os
+
+# Load model
+print("Loading model...")
+classifier = pipeline(
+    "text-classification",
+    model="JohnLicode/ethics-review-deberta",
+    device="cpu"  # Use CPU for free tier
+)
+print("Model loaded!")
+
+def classify_ethics(text: str, guideline_id: str = "", guideline_name: str = ""):
+    """Classify text for ethics guideline compliance."""
+    
+    # Format input like training data
+    if guideline_id and guideline_name:
+        input_text = f"Guideline {guideline_id} {guideline_name}: {text}"
+    else:
+        input_text = text
+    
+    # Truncate if too long
+    input_text = input_text[:1500]
+    
+    # Get prediction
+    result = classifier(input_text)[0]
+    
+    # Map labels
+    label = result['label']
+    if label == "LABEL_0":
+        label = "ADDRESSED"
+    elif label == "LABEL_1":
+        label = "NEEDS_REVISION"
+    
+    return {
+        "label": label,
+        "score": round(result['score'], 4),
+        "input_preview": input_text[:100] + "..."
+    }
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=classify_ethics,
+    inputs=[
+        gr.Textbox(label="Text to Analyze", lines=5, placeholder="Enter the text from research proposal..."),
+        gr.Textbox(label="Guideline ID (optional)", placeholder="e.g., 1.1"),
+        gr.Textbox(label="Guideline Name (optional)", placeholder="e.g., Objectives"),
+    ],
+    outputs=gr.JSON(label="Classification Result"),
+    title="Ethics Review Classifier",
+    description="Classify research proposal text against ethics guidelines. Returns ADDRESSED or NEEDS_REVISION.",
+    examples=[
+        ["The general objective is to develop an AI ethics review system. Specific objectives: 1) Create scanning module 2) Implement matching.", "1.1", "Objectives"],
+        ["All participant data will be encrypted using AES-256 and stored securely.", "3.2", "Privacy and confidentiality"],
+        ["The study explores innovative approaches.", "1.7", "Sampling design and size"],
+    ],
+)
+
+# Launch with API enabled
+demo.launch()

requirements.txt

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+transformers
+torch
+gradio
+accelerate
+sentencepiece