Gregniuki's picture
Rename app.py to app6.py
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# --- START OF FILE app.py ---
import sys
import os
import re
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 ---
MODEL_NAME = "Gregniuki/ERNIE-4.5-0.3B-PT-Translator-EN-PL-EN"
print(f"--- Loading model from Hugging Face Hub: {MODEL_NAME} ---")
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} ---")
try:
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=dtype, trust_remote_code=True).to(device)
model.eval()
print("--- Model and Tokenizer Loaded Successfully ---")
except Exception as e:
raise RuntimeError(f"FATAL: Could not load components. Error: {e}")
# --- Helper Functions ---
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]
def do_translation(text_to_translate: str) -> str:
"""Runs a single translation and returns the decoded string."""
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()
return tokenizer.decode(output_ids, skip_special_tokens=True).strip()
def preprocess_text(text: str) -> str:
"""Intelligently cleans text by handling newlines."""
if not text: return ""
text = re.sub(r'\n{2,}', ' ', text)
text = text.replace('\n', ' ')
text = re.sub(r'\s{2,}', ' ', text)
return text.strip()
# --- Step 3: Core Translation Function (ROBUST INSTRUCTIONAL PROMPT) ---
@spaces.GPU
@torch.no_grad()
def translate_with_chunks(input_text: str, chunk_size: int, context_sentences: int, progress=gr.Progress()) -> str:
"""
Processes chunks using a clear instructional prompt to provide context,
preventing overlap and translation direction errors.
"""
progress(0, desc="Starting...")
processed_text = preprocess_text(input_text)
if not processed_text: return "Input text is empty. Please enter some text to translate."
text_chunks = chunk_text(processed_text, chunk_size) if len(processed_text) > chunk_size else [processed_text]
num_chunks = len(text_chunks)
print(f"Processing {num_chunks} chunk(s).")
all_results = []
# This will hold the last few SENTENCES of the POLISH translation
polish_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 polish_context or context_sentences == 0:
# First chunk or context is disabled: Translate directly
prompt = chunk
else:
# Subsequent chunks: Use the instructional prompt format
prompt = (
"[Previous Translation Context]:\n"
f"{polish_context}\n\n"
"[New English Text to Translate and Continue]:\n"
f"{chunk}"
)
print(f"--- Prompt for Chunk {i+1} ---\n{prompt}\n--------------------")
# The model's output should now be ONLY the new translation
final_translation_for_chunk = do_translation(prompt)
all_results.append(final_translation_for_chunk)
print(f"Chunk {i+1} processed successfully.")
if context_sentences > 0:
# Update the context with the last N sentences from the new translation
# We use a simple sentence split on periods for this.
sentences = final_translation_for_chunk.split('.')
# Filter out any empty strings that might result from splitting
sentences = [s.strip() for s in sentences if s.strip()]
if sentences:
context_to_take = sentences[-context_sentences:]
polish_context = ". ".join(context_to_take) + "."
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=256, maximum=2048, value=1024, step=64, label="Character Chunk Size"),
gr.Slider(
minimum=0,
maximum=5,
value=2,
step=1,
label="Context Overlap (Sentences)",
info="Number of previous translated (Polish) sentences to provide as context. The most reliable method."
)
],
outputs=gr.Textbox(lines=15, label="Model Output", interactive=False),
title="ERNIE 4.5 Context-Aware Translator",
description="Processes long text using a robust instructional prompt to ensure high-quality, consistent translations.",
allow_flagging="never"
)
if __name__ == "__main__":
app.queue().launch()