feat: add pt-pt and en-uk language support with sidebar selector

app.py

@@ -13,6 +13,28 @@ HF_TOKEN = os.environ.get("HF_TOKEN")
 if HF_TOKEN:
     login(token=HF_TOKEN)
 
+TRANSLATIONS = {
+    "pt-pt": {
+        "title": "Classificação de Imagens - Compara Todos os Modelos",
+        "description": "🧪 Executa **todos os modelos** de classificação de imagens do Hugging Face numa só imagem e compara os resultados num tabela interativa.",
+        "upload": "Carregar Imagem",
+        "run_button": "Executar Todos os Modelos",
+        "results": "Resultados",
+        "download": "Descarregar CSV",
+    },
+    "en-uk": {
+        "title": "Image Classification - Compare All Models",
+        "description": "🧪 Run **all** Hugging Face image classification models on a single image and compare results in an interactive table.",
+        "upload": "Upload Image",
+        "run_button": "Run All Models",
+        "results": "Results",
+        "download": "Download CSV",
+    }
+}
+
+lang = st.sidebar.selectbox("Language / Idioma", ["en-uk", "pt-pt"])
+t = TRANSLATIONS[lang]
+
 MODELS = [
             ("google/vit-base-patch16-224", "General Image Classification"),
             ("microsoft/resnet-50", "General Image Classification"),
@@ -76,22 +98,21 @@ def format_results(results):
     return formatted
 
 def main():
-    st.title("Image Classification - Compare All Models")
-    st.write("This app runs ALL image classification models and displays results in a table.")
-    st.markdown(":white_check_mark: **:green[Run all models at once!]**")
+    st.title(t["title"])
+    st.markdown(t["description"])
     
     bmc_link = "https://www.buymeacoffee.com/nuno.tome" 
     image_url = "https://i.giphy.com/RETzc1mj7HpZPuNf3e.webp" 
     image_link_markdown = f"[![Buy Me a Coffee]({image_url})]({bmc_link})"
     st.markdown(image_link_markdown, unsafe_allow_html=True)
   
-    input_image = st.file_uploader("Upload Image")
+    input_image = st.file_uploader(t["upload"])
 
     if input_image is not None:
         image_to_classify = Image.open(input_image)
         st.image(image_to_classify, caption="Uploaded Image", use_column_width=True)
         
-        if st.button("Run All Models", type="primary"):
+        if st.button(t["run_button"], type="primary"):
             results_data = []
             progress_bar = st.progress(0)
             status_text = st.empty()
@@ -128,7 +149,7 @@ def main():
             
             if results_data:
                 df = pd.DataFrame(results_data)
-                st.subheader(f"Results ({len(results_data)} models)")
+                st.subheader(f"{t['results']} ({len(results_data)} models)")
                 
                 st.markdown("""
                 <style>
@@ -154,7 +175,7 @@ def main():
                 
                 csv = df.to_csv(index=False).encode('utf-8')
                 st.download_button(
-                    "Download Results CSV",
+                    t["download"],
                     csv,
                     "classification_results.csv",
                     "text/csv",