f

app.py

@@ -12,7 +12,7 @@ peft_config = PeftConfig.from_pretrained(ADAPTER_REPO)
 print("PEFT Base Model:", peft_config.base_model_name_or_path)
 
 # 2. Load the tokenizer & base model
-tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True, language='de')
 base_model = AutoModelForCausalLM.from_pretrained(
     BASE_MODEL,
     revision="4831ee1375be5b4ff5a4abf7984e13628db44e35",
@@ -30,9 +30,9 @@ model = PeftModel.from_pretrained(
 
 def extract_food_term(text):
     """
-    Extract or simplify a food term to a single word or best descriptor.
+    Extract or simplify a German food term to a single word or best descriptor.
     """
-    prompt = f"Extract the best single word or term that describes this food item:\nInput: {text}\nOutput:"
+    prompt = f"Extrahiere das beste ein Wort oder den Begriff, der dieses Nahrungsmittel beschreibt:\nInput: {text}\nOutput:"
     inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
     with torch.no_grad():
         outputs = model.generate(
@@ -48,11 +48,11 @@ def extract_food_term(text):
     return answer
 
 with gr.Blocks() as demo:
-    gr.Markdown("## Qwen + LoRA Adapter: Food Term Extraction Demo")
-    input_box = gr.Textbox(lines=1, label="Enter a food item (e.g., 'blaubeertorte')")
-    output_box = gr.Textbox(lines=1, label="Best single-word descriptor")
+    gr.Markdown("## Qwen + LoRA Adapter: Lebensmittelbegriffserkennung Demo")
+    input_box = gr.Textbox(lines=1, label="Geben Sie ein Nahrungsmittel ein (z.B., 'Blaubeertorte')")
+    output_box = gr.Textbox(lines=1, label="Beste ein Wort-Beschreibung")
 
-    extract_btn = gr.Button("Extract Term")
+    extract_btn = gr.Button("Begriff extrahieren")
     extract_btn.click(fn=extract_food_term, inputs=input_box, outputs=output_box)
 
 if __name__ == "__main__":