app0.py

# --- START OF FILE app.py ---

import sys
import os
import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from huggingface_hub import login
from dotenv import load_dotenv

# --- FIX: Add project root to Python's path ---
project_root = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(0, project_root)

# --- Updated Spaces import for Zero-GPU compatibility ---
try:
    import spaces
    print("'spaces' module imported successfully.")
except ImportError:
    print("Warning: 'spaces' module not found. Using dummy decorator for local execution.")
    class DummySpaces:
        def GPU(self, *args, **kwargs):
            def decorator(func):
                print(f"Note: Dummy @GPU decorator used for function '{func.__name__}'.")
                return func
            return decorator
    spaces = DummySpaces()

# --- Step 1: Hugging Face Authentication ---
load_dotenv()
HF_TOKEN = os.getenv("HF_TOKEN")

if not HF_TOKEN:
    raise ValueError("FATAL: Hugging Face token not found. Please set the HF_TOKEN environment variable.")

print("--- Logging in to Hugging Face Hub ---")
login(token=HF_TOKEN)


# --- Step 2: Initialize Model and Tokenizer (Load Once on Startup) ---

MODEL_NAME = "Gregniuki/ERNIE-4.5-0.3B-PT-Translator-EN-PL-EN"

print(f"--- Loading model from Hugging Face Hub: {MODEL_NAME} ---")

# --- Device Setup (Zero GPU Support) ---
if torch.cuda.is_available():
    device = torch.device("cuda")
    print("GPU detected. Using CUDA.")
else:
    device = torch.device("cpu")
    print("No GPU detected. Using CPU.")

dtype = torch.bfloat16 if device.type == "cuda" else torch.float32
print(f"--- Using dtype: {dtype} ---")

print(f"--- Loading tokenizer from Hub: {MODEL_NAME} ---")
try:
    tokenizer = AutoTokenizer.from_pretrained(
        MODEL_NAME,
        trust_remote_code=True
    )
    print("--- Tokenizer Loaded Successfully ---")
except Exception as e:
    raise RuntimeError(f"FATAL: Could not load tokenizer from the Hub. Error: {e}")

print(f"--- Loading Model with PyTorch from Hub: {MODEL_NAME} ---")
try:
    model = AutoModelForCausalLM.from_pretrained(
        MODEL_NAME,
        torch_dtype=dtype,
        trust_remote_code=True
    ).to(device)
    model.eval()
    print("--- Model Loaded Successfully ---")
except Exception as e:
    raise RuntimeError(f"FATAL: Could not load model from the Hub. Error: {e}")


# --- Helper function for chunking text (Unchanged) ---
def chunk_text(text: str, max_size: int) -> list[str]:
    """Splits text into chunks, trying to break at sentence endings."""
    if not text: return []
    chunks, start_index = [], 0
    while start_index < len(text):
        end_index = start_index + max_size
        if end_index >= len(text):
            chunks.append(text[start_index:])
            break
        split_pos = text.rfind('.', start_index, end_index)
        if split_pos != -1:
            chunk, start_index = text[start_index : split_pos + 1], split_pos + 1
        else:
            chunk, start_index = text[start_index:end_index], end_index
        chunks.append(chunk.strip())
    return [c for c in chunks if c]


# --- Step 3: Core Translation Function (REMOVED CONTEXT TO ENSURE CORRECTNESS) ---
@spaces.GPU
@torch.no_grad()
def translate_with_chunks(input_text: str, chunk_size: int, progress=gr.Progress()) -> str:
    """
    Processes text by translating each chunk independently. This prevents the model
    from incorrectly switching translation direction.
    """
    progress(0, desc="Starting...")
    print("--- Inference function with isolated chunks started ---")
    if not input_text or not input_text.strip():
        return "Input text is empty. Please enter some text to translate."

    progress(0.1, desc="Chunking Text...")
    text_chunks = chunk_text(input_text, chunk_size) if len(input_text) > chunk_size else [input_text]
    num_chunks = len(text_chunks)
    print(f"Processing {num_chunks} chunk(s).")

    all_results = []

    for i, chunk in enumerate(text_chunks):
        progress(0.2 + (i / num_chunks) * 0.7, desc=f"Translating chunk {i+1}/{num_chunks}")

        # Each chunk is treated as a new, single-turn conversation.
        # This prevents the model from getting confused by previous turns.
        messages = [{"role": "user", "content": chunk}]

        prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
        model_inputs = tokenizer([prompt], add_special_tokens=False, return_tensors="pt").to(device)

        generated_ids = model.generate(
            **model_inputs,
            max_new_tokens=2048,
            do_sample=True,
            temperature=0.7,
            top_p=0.95,
            top_k=50
        )
        
        input_token_len = model_inputs.input_ids.shape[1]
        output_ids = generated_ids[0][input_token_len:].tolist()
        result_text = tokenizer.decode(output_ids, skip_special_tokens=True).strip()
        
        all_results.append(result_text)
        print(f"Chunk {i+1} translated successfully.")

    progress(0.95, desc="Reassembling Results...")
    full_output = " ".join(all_results)

    progress(1.0, desc="Done!")
    return full_output

# --- Step 4: Create and Launch the Gradio App ---
print("\n--- Initializing Gradio Interface ---")

app = gr.Interface(
    fn=translate_with_chunks,
    inputs=[
        gr.Textbox(lines=15, label="Input Text", placeholder="Enter long text to process here..."),
        gr.Slider(minimum=128, maximum=1536, value=1024, step=64, label="Character Chunk Size")
    ],
    outputs=gr.Textbox(lines=15, label="Model Output", interactive=False),
    title="ERNIE 4.5 Text Translator",
    description="Processes long text by splitting it into independent chunks to ensure correct and reliable translation.",
    allow_flagging="never"
)

if __name__ == "__main__":
    app.queue().launch()