Fix input field and remove pre-filled system message

app.py

@@ -18,45 +18,37 @@ def respond(
     max_tokens,
     translation_choice: str,
 ):
-    """
-    Responds to the input message by selecting the translation model based on the user's choice.
-    """
-    messages = [{"role": "system", "content": system_message}]
-
-    # Append previous history to maintain context
-    for val in history:
-        if val[0]:  # User message
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:  # Assistant message
-            messages.append({"role": "assistant", "content": val[1]})
-
-    # Append the user message
-    messages.append({"role": "user", "content": message})
+    # Ensure there's no empty input
+    if not message.strip():
+        return "Error: Please enter a valid text to translate."
 
     # Initialize the response variable
     response = ""
 
     # Translate based on the user's choice
-    if translation_choice == "Moroccan Arabic to MSA":
-        # Translate Moroccan Arabic (Darija) to Modern Standard Arabic
-        inputs = tokenizer_darija_to_msa(message, return_tensors="pt", padding=True)
-        outputs = model_darija_to_msa.generate(inputs["input_ids"], num_beams=5, max_length=max_tokens, early_stopping=True)
-        response = tokenizer_darija_to_msa.decode(outputs[0], skip_special_tokens=True)
-
-    elif translation_choice == "English to Moroccan Arabic":
-        # Translate English to Moroccan Arabic (Darija)
-        inputs = tokenizer_eng_to_darija(message, return_tensors="pt", padding=True)
-        outputs = model_eng_to_darija.generate(inputs["input_ids"], num_beams=5, max_length=max_tokens, early_stopping=True)
-        response = tokenizer_eng_to_darija.decode(outputs[0], skip_special_tokens=True)
+    try:
+        if translation_choice == "Moroccan Arabic to MSA":
+            # Translate Moroccan Arabic (Darija) to Modern Standard Arabic
+            inputs = tokenizer_darija_to_msa(message, return_tensors="pt", padding=True)
+            outputs = model_darija_to_msa.generate(inputs["input_ids"], num_beams=5, max_length=max_tokens, early_stopping=True)
+            response = tokenizer_darija_to_msa.decode(outputs[0], skip_special_tokens=True)
+
+        elif translation_choice == "English to Moroccan Arabic":
+            # Translate English to Moroccan Arabic (Darija)
+            inputs = tokenizer_eng_to_darija(message, return_tensors="pt", padding=True)
+            outputs = model_eng_to_darija.generate(inputs["input_ids"], num_beams=5, max_length=max_tokens, early_stopping=True)
+            response = tokenizer_eng_to_darija.decode(outputs[0], skip_special_tokens=True)
+    except Exception as e:
+        response = f"Error occurred: {str(e)}"
 
     return response
 
 
-# Gradio interface setup without temperature and top-p sliders
+# Gradio interface setup without pre-filled system message
 demo = gr.Interface(
     fn=respond,
     inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+        gr.Textbox(value="", label="Enter Your Text", placeholder="Type your sentence here..."),
         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
         gr.Dropdown(
             label="Choose Translation Direction",
@@ -69,5 +61,3 @@ demo = gr.Interface(
 
 # Launch the interface
 demo.launch()
-
-