Create app.py

app.py

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+"""Gradio app for Maritime Intelligence Classifier."""
+import gradio as gr
+from setfit import SetFitModel
+from pathlib import Path
+import os
+
+# Try to load model from Hugging Face Hub first, then fall back to local
+# Set MODEL_PATH environment variable or update this line with your Hugging Face repo ID
+MODEL_PATH = os.getenv("MODEL_PATH", "gamaly/maritime-intelligence-classifier")
+LOCAL_MODEL_PATH = "./maritime_classifier"
+
+# Load model
+print("Loading model...")
+try:
+    # Try Hugging Face Hub first
+    if "/" in MODEL_PATH and not Path(MODEL_PATH).exists():
+        model = SetFitModel.from_pretrained(MODEL_PATH)
+        print(f"✓ Loaded model from Hugging Face: {MODEL_PATH}")
+    else:
+        # Try local path
+        if Path(LOCAL_MODEL_PATH).exists():
+            model = SetFitModel.from_pretrained(LOCAL_MODEL_PATH)
+            print(f"✓ Loaded model from local path: {LOCAL_MODEL_PATH}")
+        else:
+            raise FileNotFoundError(f"Model not found at {MODEL_PATH} or {LOCAL_MODEL_PATH}")
+except Exception as e:
+    print(f"⚠️  Error loading model: {e}")
+    print("Make sure the model is trained or uploaded to Hugging Face")
+    model = None
+
+def predict_text(text):
+    """Predict whether text is actionable (YES) or not (NO)."""
+    if model is None:
+        return "Error: Model not loaded. Please train the model first.", 0.0, "error"
+    
+    if not text or not text.strip():
+        return "Please enter some text to classify.", 0.0, "neutral"
+    
+    try:
+        # Make prediction
+        prediction = model.predict([text])[0]
+        probabilities = model.predict_proba([text])[0]
+        
+        # Get confidence
+        confidence = probabilities[prediction] * 100
+        
+        # Convert to labels
+        label = "YES (Actionable)" if prediction == 1 else "NO (Not Actionable)"
+        
+        # Determine status for styling
+        status = "actionable" if prediction == 1 else "not_actionable"
+        
+        return label, confidence, status
+    except Exception as e:
+        return f"Error during prediction: {str(e)}", 0.0, "error"
+
+def get_explanation(status):
+    """Get explanation based on prediction status."""
+    explanations = {
+        "actionable": "✓ This text contains actionable vessel-specific evidence (e.g., specific vessel names, crimes, incidents).",
+        "not_actionable": "✗ This text does not contain actionable vessel-specific evidence (e.g., general maritime news, non-specific information).",
+        "error": "⚠️ An error occurred. Please check the model is properly loaded.",
+        "neutral": ""
+    }
+    return explanations.get(status, "")
+
+# Create Gradio interface
+with gr.Blocks(title="Maritime Intelligence Classifier", theme=gr.themes.Soft()) as app:
+    gr.Markdown(
+        """
+        # 🚢 Maritime Intelligence Classifier
+        
+        Classify maritime news articles as containing **actionable vessel-specific evidence** (YES) or not (NO).
+        
+        **Actionable articles** typically include:
+        - Specific vessel names
+        - Specific crimes or incidents
+        - Evidence that can be used for investigation
+        
+        **Non-actionable articles** are general maritime news without specific vessel details.
+        """
+    )
+    
+    with gr.Row():
+        with gr.Column(scale=2):
+            text_input = gr.Textbox(
+                label="Article Text",
+                placeholder="Paste or type the maritime news article text here...",
+                lines=10,
+                max_lines=20
+            )
+            
+            submit_btn = gr.Button("Classify", variant="primary", size="lg")
+        
+        with gr.Column(scale=1):
+            prediction_output = gr.Label(
+                label="Prediction",
+                value={"YES (Actionable)": 0.0, "NO (Not Actionable)": 0.0}
+            )
+            
+            confidence_output = gr.Number(
+                label="Confidence",
+                value=0.0,
+                precision=1
+            )
+            
+            explanation_output = gr.Markdown()
+    
+    # Example texts
+    gr.Markdown("### 📝 Example Texts")
+    with gr.Row():
+        example_yes = gr.Examples(
+            examples=[
+                ["The fishing vessel Marine 707 was involved in the disappearance of fisheries observer Samuel Abayateye in Ghanaian waters. The observer's decapitated body was found weeks later."],
+                ["Authorities detained the Meng Xin 15 after discovering evidence of illegal saiko transshipment and threats against fisheries observers."],
+            ],
+            inputs=text_input,
+            label="YES Examples (Actionable)"
+        )
+        
+        example_no = gr.Examples(
+            examples=[
+                ["A new maritime museum opened in the port city, showcasing historical ships and ocean exploration artifacts."],
+                ["Marine scientists are studying the effects of ocean acidification on coral reefs in tropical waters."],
+            ],
+            inputs=text_input,
+            label="NO Examples (Not Actionable)"
+        )
+    
+    # Connect the prediction function
+    def update_prediction(text):
+        label, confidence, status = predict_text(text)
+        
+        # Create label dict for gradio Label component
+        if status == "actionable":
+            label_dict = {"YES (Actionable)": confidence / 100, "NO (Not Actionable)": (100 - confidence) / 100}
+        elif status == "not_actionable":
+            label_dict = {"YES (Actionable)": (100 - confidence) / 100, "NO (Not Actionable)": confidence / 100}
+        else:
+            label_dict = {"YES (Actionable)": 0.0, "NO (Not Actionable)": 0.0}
+        
+        explanation = get_explanation(status)
+        
+        return label_dict, confidence, explanation
+    
+    submit_btn.click(
+        fn=update_prediction,
+        inputs=text_input,
+        outputs=[prediction_output, confidence_output, explanation_output]
+    )
+    
+    text_input.submit(
+        fn=update_prediction,
+        inputs=text_input,
+        outputs=[prediction_output, confidence_output, explanation_output]
+    )
+    
+    gr.Markdown(
+        """
+        ---
+        ### ℹ️ About
+        
+        This classifier uses SetFit to identify maritime news articles containing actionable vessel-specific evidence.
+        Built for The Outlaw Ocean Project.
+        
+        **Model**: SetFit (sentence-transformers/all-MiniLM-L6-v2 base)
+        """
+    )
+
+if __name__ == "__main__":
+    app.launch(share=False)
+