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from peft import PeftModel, PeftConfig
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from flask import Flask, render_template, request, jsonify
HUGGING_FACE_USER_NAME = "elalimy"
model_name = "my_awesome_peft_finetuned_helsinki_model"
peft_model_id = f"{HUGGING_FACE_USER_NAME}/{model_name}"
# Load model configuration (assuming it's saved locally)
config = PeftConfig.from_pretrained(peft_model_id)
# Load the base model from its local directory (replace with actual model type)
base_model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=False)
# Load the tokenizer from its local directory (replace with actual tokenizer type)
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
# Load the Peft model (assuming it's a custom class or adaptation)
AI_model = PeftModel.from_pretrained(base_model, peft_model_id)
# Flask app class
app = Flask(__name__, template_folder='templates') # Specify the templates folder
def generate_translation(source_text, device="cpu"):
# Encode the source text
input_ids = tokenizer.encode(source_text, return_tensors='pt').to(device)
# Move the model to the same device as input_ids
model = base_model.to(device)
# Generate the translation with adjusted decoding parameters
generated_ids = model.generate(
input_ids=input_ids,
max_length=512, # Adjust max_length if needed
num_beams=4,
length_penalty=5, # Adjust length_penalty if needed
no_repeat_ngram_size=4,
early_stopping=True
)
# Decode the generated translation excluding special tokens
generated_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
return generated_text
@app.route('/')
def index():
return render_template('index.html')
@app.route('/translate', methods=['POST'])
def translate_text():
data = request.get_json()
if 'text' not in data:
return jsonify({'error': 'No text to translate provided'}), 400
text_to_translate = data['text']
translated_text = generate_translation(text_to_translate)
return jsonify({'translated_text': translated_text})
if __name__ == "__main__":
app.run()
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