Create app.py

app.py

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+import gradio as gr
+from huggingface_hub import InferenceClient
+
+# --- MODEL DATA ---
+MODELS = {
+    "Phase 2: Stable (Formal)": {
+        "id": "st192011/Maltese-EuroLLM-1.7B-Phase2-Stable",
+        "description": "The 'Bureaucrat Bot'. Trained on 200k rows of EU/Government data (TildeMODEL). High fidelity for legal and official documents.",
+        "chrf": "60.18",
+        "comet": "0.6431"
+    },
+    "Phase 4: Anchored (Native)": {
+        "id": "st192011/Maltese-EuroLLM-1.7B-Phase4-Anchored",
+        "description": "The 'Native Speaker'. Uses Anchored Reasoning (CoT) distilled from Llama-70B. Designed for natural phrasing and cultural awareness.",
+        "chrf": "52.68",
+        "comet": "0.6567"
+    }
+}
+
+def translate_logic(text, selected_models, temp):
+    results = {}
+    
+    for model_name in selected_models:
+        model_id = MODELS[model_name]["id"]
+        client = InferenceClient(model=model_id)
+        
+        # Prompt format consistent with training
+        prompt = f"### INGLIŻ: {text}\n### MALTI:"
+        
+        try:
+            output = client.text_generation(
+                prompt,
+                max_new_tokens=150,
+                temperature=temp,
+                do_sample=True if temp > 0.1 else False,
+                repetition_penalty=1.2
+            )
+            # Clean up the response
+            clean_output = output.strip().split("### MALTI:")[-1].replace("<|endoftext|>", "").strip()
+            results[model_name] = clean_output
+        except Exception as e:
+            results[model_name] = f"Error: Inference API is still loading or unavailable. ({str(e)})"
+            
+    # Return formatted outputs for the UI
+    # We return a list of outputs corresponding to the two textboxes
+    out_p2 = results.get("Phase 2: Stable (Formal)", "Model not selected.")
+    out_p4 = results.get("Phase 4: Anchored (Native)", "Model not selected.")
+    
+    return out_p2, out_p4
+
+# --- GRADIO UI ---
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🇲🇹 Maltese-MT Arena")
+    gr.Markdown("Compare different generations of fine-tuned EuroLLM models for English-to-Maltese translation.")
+    
+    with gr.Row():
+        with gr.Column(scale=2):
+            input_text = gr.Textbox(label="English Source Text", placeholder="Enter English text here...", lines=4)
+            model_selector = gr.CheckboxGroup(
+                choices=list(MODELS.keys()), 
+                value=list(MODELS.keys()), 
+                label="Select Models to Compare"
+            )
+            temp_slider = gr.Slider(minimum=0.1, maximum=1.0, value=0.1, step=0.1, label="Creativity (Temperature)")
+            btn = gr.Button("🚀 Run Translation", variant="primary")
+
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("### Phase 2: Stable")
+            p2_out = gr.Textbox(label="Output", interactive=False, lines=5)
+            gr.Markdown(f"**Training:** {MODELS['Phase 2: Stable (Formal)']['description']}")
+            gr.Markdown(f"**Metrics:** ChrF++: `{MODELS['Phase 2: Stable (Formal)']['chrf']}` | COMET: `{MODELS['Phase 2: Stable (Formal)']['comet']}`")
+            
+        with gr.Column():
+            gr.Markdown("### Phase 4: Anchored")
+            p4_out = gr.Textbox(label="Output", interactive=False, lines=5)
+            gr.Markdown(f"**Training:** {MODELS['Phase 4: Anchored (Native)']['description']}")
+            gr.Markdown(f"**Metrics:** ChrF++: `{MODELS['Phase 4: Anchored (Native)']['chrf']}` | COMET: `{MODELS['Phase 4: Anchored (Native)']['comet']}`")
+
+    gr.Examples(
+        examples=[
+            ["The ferry to Gozo leaves every 45 minutes."],
+            ["We now have 4-month-old mice that are non-diabetic that used to be diabetic."],
+            ["This regulation shall be binding in its entirety and directly applicable in all Member States."]
+        ],
+        inputs=input_text
+    )
+
+    btn.click(
+        fn=translate_logic, 
+        inputs=[input_text, model_selector, temp_slider], 
+        outputs=[p2_out, p4_out]
+    )
+
+demo.launch()