import streamlit as st import requests import os from dotenv import load_dotenv # --- Configuration --- # Attempt to load .env file. # Assumes .env is in the parent directory of this script's location (e.g., ../.env) # If your app.py is in the root of your project (where .env also is), # load_dotenv() without arguments might work. # For Hugging Face Spaces, you'll set secrets directly in the Space settings. dotenv_path = os.path.join( os.path.dirname(__file__), "..", ".env" ) # Path to .env in parent directory if os.path.exists(dotenv_path): load_dotenv(dotenv_path=dotenv_path) else: # Fallback if .env is in the current directory (less likely for multi-project setup) load_dotenv() API_TOKEN = os.getenv("HF_TOKEN") API_URL_BASE = "https://api-inference.huggingface.co/models/" HEADERS = {"Authorization": f"Bearer {API_TOKEN}"} # Define available models (user-friendly name: model_id) # You can find more models at https://huggingface.co/models?pipeline_tag=translation # Filter by source language and target language. TRANSLATION_MODELS = { "English to Spanish": "Helsinki-NLP/opus-mt-en-es", "English to French": "Helsinki-NLP/opus-mt-en-fr", "English to German": "Helsinki-NLP/opus-mt-en-de", "English to Chinese (Simplified)": "Helsinki-NLP/opus-mt-en-zh", "English to Japanese": "Helsinki-NLP/opus-mt-en-jap", # Check model hub for exact ID if this doesn't work "Spanish to English": "Helsinki-NLP/opus-mt-es-en", "French to English": "Helsinki-NLP/opus-mt-fr-en", # Add more models/languages as desired } # --- Hugging Face API Call Function --- def query_translation(text_to_translate, model_id): """ Sends a request to the Hugging Face Inference API for translation. """ if not API_TOKEN: # Check if token was loaded st.error( "Hugging Face API Token not found. Please configure it in your .env file or Space secrets." ) return None api_url = API_URL_BASE + model_id payload = {"inputs": text_to_translate} try: response = requests.post( api_url, headers=HEADERS, json=payload, timeout=30 ) # Added timeout response.raise_for_status() # Raises an HTTPError for bad responses (4XX or 5XX) return response.json() except requests.exceptions.HTTPError as errh: st.error(f"Translation API HTTP Error: {errh}") error_details = "No additional details from API." try: error_details = response.json().get("error", response.text) except ValueError: # If response.text is not JSON error_details = response.text st.info(f"Details: {error_details}") return None except requests.exceptions.ConnectionError as errc: st.error(f"Translation API Connection Error: {errc}") return None except requests.exceptions.Timeout as errt: st.error(f"Translation API Timeout Error: {errt}") return None except requests.exceptions.RequestException as err: st.error(f"Translation API Request Error: {err}") return None except ( ValueError ): # If response is not JSON (should be caught by response.json() above but good to have) st.error("Error: Received non-JSON response from translation API.") st.info( f"Raw Response: {response.text if 'response' in locals() else 'No response object'}" ) return None # --- Streamlit UI --- st.set_page_config(page_title="🌍 Text Translator", layout="wide") st.title("🌍 Text Translation Tool") st.markdown( "Translate text into various languages using Hugging Face's Inference API. " "This app demonstrates API integration for NLP tasks." ) # Check for API token at the beginning of UI rendering if not API_TOKEN: st.error("Hugging Face API Token not configured. The application cannot function.") st.markdown( "Please ensure your `HUGGING_FACE_API_TOKEN` is set in a `.env` file " "in the root of your `ai-portfolio` project or as a secret if deploying on Hugging Face Spaces." ) st.stop() # Stop further execution of the script if token is missing # Layout columns col1, col2 = st.columns([2, 1]) # Text area takes 2/3, selectbox takes 1/3 with col1: text_input = st.text_area( "Enter text to translate:", height=200, key="text_input_translate", placeholder="Type or paste your text here...", ) with col2: selected_language_name = st.selectbox( "Select target language:", options=list(TRANSLATION_MODELS.keys()), index=0, # Default to the first language in the list key="lang_select", ) model_id_to_use = TRANSLATION_MODELS[selected_language_name] st.caption(f"Using model: `{model_id_to_use}`") if st.button("Translate Text", key="translate_button", type="primary"): if text_input: if not API_TOKEN: # Redundant check, but good for safety st.error("API Token is missing. Cannot proceed.") else: with st.spinner(f"Translating to {selected_language_name}... Please wait."): translation_result = query_translation(text_input, model_id_to_use) if translation_result: # The API returns a list with a dictionary inside if ( isinstance(translation_result, list) and len(translation_result) > 0 and "translation_text" in translation_result[0] ): translated_text = translation_result[0]["translation_text"] st.subheader("📜 Translation:") st.success(translated_text) # Sometimes the API might return a dictionary directly with an error elif isinstance(translation_result, dict) and translation_result.get( "error" ): # Error is already displayed by the query_translation function st.warning("Translation failed. See error message above.") else: st.error( "Translation failed or the API returned an unexpected format." ) st.json(translation_result) # Show the raw response for debugging # If translation_result is None, query_translation already showed an error else: st.warning("Please enter some text to translate.") st.divider() st.sidebar.header("ℹī¸ About This App") st.sidebar.info( "This tool demonstrates the use of the Hugging Face Inference API " "for text translation. It allows users to input text and select a target " "language, then displays the translated output." "\n\n**Key Skills Showcased:**" "\n- Python & Streamlit for UI" "\n- Hugging Face API Integration" "\n- Handling API responses & errors" "\n- Basic NLP application" ) st.sidebar.markdown("---") st.sidebar.markdown("Project for **AI Project Portfolio (4 Weeks)**")