app.py

# --- 1. INSTALL LIBRARIES ---
print("--- Installing Gradio and Transformers... ---")
# We only need gradio and the AI libraries
!pip install gradio transformers sentencepiece sacremoses

# --- 2. IMPORT LIBRARIES AND LOAD MODEL ---
import gradio as gr
from transformers import pipeline

print("--- Loading FAST 'aryaumesh/english-to-marathi' model... ---")
# Load the fast and small model
try:
    translator = pipeline("translation", model="aryaumesh/english-to-marathi")
    print("--- Model loaded successfully! ---")
except Exception as e:
    print(f"Error loading model: {e}")
    translator = None

# --- 3. DEFINE THE TRANSLATION FUNCTION ---
# This is the function Gradio will call
def translate_text(input_text):
    if translator is None:
        return "Error: Model not loaded"

    try:
        # Run the translation
        result = translator(input_text)
        return result[0]['translation_text']
    except Exception as e:
        return f"Error: {str(e)}"

# --- 4. CREATE AND LAUNCH THE GRADIO APP ---
print("--- Launching Gradio App... ---")

# This creates the UI: a text input, a text output, and a title
demo = gr.Interface(
    fn=translate_text,
    inputs=gr.Textbox(lines=5, placeholder="Enter text in English..."),
    outputs="text",
    title="English-to-Marathi AI Translator",
    description="A simple translator built for my project using Gradio and a Hugging Face model."
)

# share=True is the magic. It creates a public URL.
demo.launch(share=True)