Update app.py

app.py

@@ -1,54 +1,37 @@
+import requests
+import pandas as pd
 import gradio as gr
 from transformers import MarianMTModel, MarianTokenizer
 
-# Function to dynamically load the model and tokenizer based on selected languages
-def translate_text(text, source_language_code, target_language_code):
-    # Construct model name using ISO 639-1 codes
-    model_name = f"Helsinki-NLP/opus-mt-{source_language_code}-{target_language_code}"
+# Fetch and parse language options from the provided URL
+url = "https://huggingface.co/Lenylvt/LanguageISO/resolve/main/iso.md"
+response = requests.get(url)
+# Assuming the response content is a Markdown table, convert it to a DataFrame
+df = pd.read_csv(url, delimiter="|", skiprows=2, header=None).dropna(axis=1, how='all')
+df.columns = ['ISO 639-1', 'ISO 639-2', 'Language Name', 'Native Name']
+df['ISO 639-1'] = df['ISO 639-1'].str.strip()
 
-    # Check if source and target languages are the same, which is not supported for translation
-    if source_language_code == target_language_code:
-        return "Translation between the same languages is not supported."
+# Prepare language options for the dropdown
+language_options = [(row['ISO 639-1'], f"{row['Language Name']} ({row['ISO 639-1']})") for index, row in df.iterrows()]
 
-    # Load tokenizer and model
-    try:
-        tokenizer = MarianTokenizer.from_pretrained(model_name)
-        model = MarianMTModel.from_pretrained(model_name)
-    except Exception as e:
-        return f"Failed to load model for {source_language_code} to {target_language_code}: {str(e)}"
+# Your translate_text function and Gradio interface setup goes here
+# Replace the previous static language_options list with the dynamic one created above
 
-    # Translate text
-    translated = model.generate(**tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512))
-    translated_text = tokenizer.decode(translated[0], skip_special_tokens=True)
-    
-    return translated_text
-
-# Define language options (ISO 639-1 codes and names)
-language_options = [
-    ('en', 'English (en)'),
-    ('es', 'Spanish (es)'),
-    ('fr', 'French (fr)'),
-    ('de', 'German (de)'),
-    ('zh', 'Chinese (zh)'),
-    ('ru', 'Russian (ru)'),
-    ('ar', 'Arabic (ar)'),
-    ('it', 'Italian (it)'),
-    ('pt', 'Portuguese (pt)'),
-    ('nl', 'Dutch (nl)'),
-    # Add more languages as needed
-]
+# Example translate_text function placeholder
+def translate_text(text, source_language, target_language):
+    return "Translation function implementation"
 
 # Create dropdowns for source and target languages, using only the codes for value
-source_language_dropdown = gr.Dropdown(choices=[(code, name) for code, name in language_options], label="Source Language")
-target_language_dropdown = gr.Dropdown(choices=[(code, name) for code, name in language_options], label="Target Language")
+source_language_dropdown = gr.Dropdown(choices=language_options, label="Source Language")
+target_language_dropdown = gr.Dropdown(choices=language_options, label="Target Language")
 
 # Define the interface
 iface = gr.Interface(
     fn=translate_text,
     inputs=[gr.Textbox(lines=2, placeholder="Enter text to translate..."), source_language_dropdown, target_language_dropdown],
     outputs=gr.Textbox(),
-    title="Text Translator with Dynamic Helsinki NLP Models",
-    description="Select source and target languages to translate text using Helsinki NLP models."
+    title="Text Translator with Dynamic Language Options",
+    description="Select source and target languages to translate text."
 )
 
 # Launch the app