Gregniuki's picture
Rename app.py to app5.py
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# --- 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 ---
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
dtype = torch.bfloat16 if device.type == "cuda" else torch.float32
print(f"--- Using device: {device}, dtype: {dtype} ---")
# --- Load Tokenizer and Define Marker ---
try:
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
# Use a semantically correct separator token from your model's vocab
MARKER_STRING = "<|LOC_SEP|>"
marker_token_id = tokenizer.convert_tokens_to_ids(MARKER_STRING)
if marker_token_id == tokenizer.unk_token_id:
raise ValueError(f"Marker token '{MARKER_STRING}' not found in tokenizer vocabulary!")
print(f"--- Using marker '{MARKER_STRING}' (ID: {marker_token_id}) for precise overlap removal. ---")
except Exception as e:
raise RuntimeError(f"FATAL: Could not load tokenizer. Error: {e}")
# --- Load Model ---
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. Error: {e}")
# --- Helper function for chunking text (Unchanged) ---
def chunk_text(text: str, max_size: int) -> list[str]:
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]
# --- Modified translation helper to return IDs ---
def do_translation(text_to_translate: str) -> tuple[str, list[int]]:
"""Runs a single translation and returns both the decoded string and the token IDs."""
if not text_to_translate.strip(): return "", []
messages = [{"role": "user", "content": text_to_translate}]
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_tensor = 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_tensor[0][input_token_len:].tolist()
decoded_text = tokenizer.decode(output_ids, skip_special_tokens=True).strip()
return decoded_text, output_ids
# --- Step 3: Core Translation Function (PRECISE TOKEN ID METHOD + DIFF FALLBACK) ---
@spaces.GPU
@torch.no_grad()
def translate_with_chunks(input_text: str, chunk_size: int, context_words: int, progress=gr.Progress()) -> str:
"""
Processes chunks using a precise token ID search for overlap removal, with a robust 'diff' fallback.
"""
progress(0, desc="Starting...")
if not input_text or not input_text.strip(): return "Input text is empty. Please enter some text to translate."
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 = []
english_context = ""
for i, chunk in enumerate(text_chunks):
progress(0.2 + (i / num_chunks) * 0.7, desc=f"Translating chunk {i+1}/{num_chunks}")
if not english_context:
# First chunk: no context needed
final_translation_for_chunk, _ = do_translation(chunk)
else:
prompt_with_marker = f"{english_context} {MARKER_STRING} {chunk}"
full_translation_str, full_translation_ids = do_translation(prompt_with_marker)
# --- Primary Method: Search for Marker Token ID ---
try:
marker_index = full_translation_ids.index(marker_token_id)
print("Precise marker token ID found. Slicing output.")
clean_ids = full_translation_ids[marker_index + 1:]
final_translation_for_chunk = tokenizer.decode(clean_ids, skip_special_tokens=True).strip()
# --- Fallback Method: 'Diff' Algorithm ---
except ValueError:
print(f"Warning: Marker token ID {marker_token_id} not in output. Falling back to diff algorithm.")
translated_context_str, _ = do_translation(english_context)
context_words_list = translated_context_str.split()
full_translation_words_list = full_translation_str.split()
overlap_len_in_words = 0
for j in range(min(len(context_words_list), len(full_translation_words_list))):
if context_words_list[j].strip('.,!?;:').lower() != full_translation_words_list[j].strip('.,!?;:').lower():
break
overlap_len_in_words += 1
final_translation_for_chunk = " ".join(full_translation_words_list[overlap_len_in_words:])
all_results.append(final_translation_for_chunk)
print(f"Chunk {i+1} processed successfully.")
if context_words > 0:
english_context = " ".join(chunk.split()[-context_words:])
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"),
gr.Slider(minimum=0, maximum=50, value=15, step=5, label="Context Overlap (Source Words)")
],
outputs=gr.Textbox(lines=15, label="Model Output", interactive=False),
title="ERNIE 4.5 Context-Aware Translator",
description="Processes long text using a precise token-based method with a robust 'diff' fallback to ensure high-quality, consistent translations.",
allow_flagging="never"
)
if __name__ == "__main__":
app.queue().launch()