Create app.py

app.py

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+# 1. Install necessary libraries (if you haven't already)
+pip install gradio transformers torch sentencepiece
+
+# 2. Import libraries
+import gradio as gr
+from transformers import pipeline
+import torch # PyTorch is needed as a backend for transformers
+
+# 3. Load the translation pipeline
+# Using the NLLB model which supports many languages including English and Telugu
+# You might need to adjust device mapping based on your hardware (e.g., device=0 for GPU)
+try:
+    # Try loading the specific model mentioned implicitly by language codes
+    translator = pipeline('translation', model='facebook/nllb-200-distilled-600M', device=-1) # Use -1 for CPU
+    print("Translator pipeline loaded successfully.")
+except Exception as e:
+    print(f"Error loading translator pipeline: {e}")
+    # Define a dummy function if pipeline fails to load, so Gradio interface still runs
+    def translator(text, src_lang, tgt_lang):
+        return f"Error: Could not load translation model. {e}"
+
+# 4. Define the translation function for Gradio
+def translate_text(text_to_translate, source_language, target_language):
+    """
+    Translates text using the loaded Hugging Face pipeline.
+
+    Args:
+        text_to_translate (str): The text to translate.
+        source_language (str): The source language code (e.g., 'eng_Latn').
+        target_language (str): The target language code (e.g., 'tel_Telu').
+
+    Returns:
+        str: The translated text or an error message.
+    """
+    if not text_to_translate:
+        return "Please enter text to translate."
+    if not source_language or not target_language:
+        return "Please select both source and target languages."
+
+    try:
+        # Perform the translation using the pipeline
+        # Note: The pipeline function expects keyword arguments src_lang and tgt_lang
+        translated_output = translator(text_to_translate,
+                                       src_lang=source_language,
+                                       tgt_lang=target_language)
+        # The output is usually a list containing a dictionary
+        if translated_output and isinstance(translated_output, list):
+             return translated_output[0]['translation_text']
+        else:
+            # Handle unexpected output format
+            return f"Translation failed. Unexpected output: {translated_output}"
+    except Exception as e:
+        print(f"Translation error: {e}")
+        # Provide a user-friendly error message
+        return f"An error occurred during translation: {e}. Make sure the language codes are correct and supported by the model."
+
+# 5. Define language choices for dropdowns (using NLLB codes)
+# Add more languages as needed from the NLLB supported list
+language_choices = [
+    ("English", "eng_Latn"),
+    ("Telugu", "tel_Telu"),
+    ("Hindi", "hin_Deva"),
+    ("Tamil", "tam_Taml"),
+    ("Spanish", "spa_Latn"),
+    ("French", "fra_Latn"),
+    ("German", "deu_Latn"),
+    ("Chinese (Simplified)", "zho_Hans"),
+]
+
+# 6. Create the Gradio interface
+with gr.Blocks(theme=gr.themes.Soft()) as iface:
+    gr.Markdown("# Text Translator using NLLB Model")
+    gr.Markdown("Enter text and select the source and target languages.")
+
+    with gr.Row():
+        # Input text area
+        input_text = gr.Textbox(label="Text to Translate", placeholder="Enter text here...", lines=5)
+        # Output text area
+        output_text = gr.Textbox(label="Translated Text", placeholder="Translation will appear here...", lines=5, interactive=False)
+
+    with gr.Row():
+        # Source language dropdown
+        source_lang = gr.Dropdown(
+            label="Source Language",
+            choices=language_choices,
+            value="eng_Latn" # Default to English
+        )
+        # Target language dropdown
+        target_lang = gr.Dropdown(
+            label="Target Language",
+            choices=language_choices,
+            value="tel_Telu" # Default to Telugu
+        )
+
+    # Translate button
+    translate_button = gr.Button("Translate", variant="primary")
+
+    # Define the action when the button is clicked
+    translate_button.click(
+        fn=translate_text,
+        inputs=[input_text, source_lang, target_lang],
+        outputs=output_text,
+        api_name="translate" # Name for API endpoint if needed
+    )
+
+    gr.Markdown("Powered by Hugging Face Transformers and Gradio.")
+
+# 7. Launch the Gradio app
+# When running locally, this will provide a URL.
+# On Hugging Face Spaces, this line makes the app run.
+if __name__ == "__main__":
+    iface.launch()