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app.py

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+import torch
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import spacy
+
+class ModelSingleton:
+    _instance = None
+
+    def __new__(cls, *args, **kwargs):
+        if not cls._instance:
+            cls._instance = super(ModelSingleton, cls).__new__(cls, *args, **kwargs)
+        return cls._instance
+
+    def __init__(self):
+        if not hasattr(self, 'initialized'):
+            self.nlp_en = spacy.load("en_core_web_sm")
+            self.nlp_it = spacy.load("it_core_news_sm")
+
+            # Load translation models and tokenizers
+            self.tokenizer_en_it = AutoTokenizer.from_pretrained("puettmann/Foglietta-mt-en-it")
+            self.model_en_it = AutoModelForSeq2SeqLM.from_pretrained("puettmann/Foglietta-mt-en-it", torch_dtype=torch.bfloat16)
+
+            self.tokenizer_it_en = AutoTokenizer.from_pretrained("puettmann/Foglietta-mt-it-en")
+            self.model_it_en = AutoModelForSeq2SeqLM.from_pretrained("puettmann/Foglietta-mt-it-en", torch_dtype=torch.bfloat16)
+
+            self.initialized = True
+
+model_singleton = ModelSingleton()
+
+def generate_response_en_it(input_text):
+    input_ids = model_singleton.tokenizer_en_it("translate English to Italian: " + input_text, return_tensors="pt").input_ids
+    output = model_singleton.model_en_it.generate(input_ids, max_new_tokens=256)
+    return model_singleton.tokenizer_en_it.decode(output[0], skip_special_tokens=True)
+
+def generate_response_it_en(input_text):
+    input_ids = model_singleton.tokenizer_it_en("translate Italian to English: " + input_text, return_tensors="pt").input_ids
+    output = model_singleton.model_it_en.generate(input_ids, max_new_tokens=256)
+    return model_singleton.tokenizer_it_en.decode(output[0], skip_special_tokens=True)
+
+def translate_text(input_text, direction):
+    if direction == "en-it":
+        nlp = model_singleton.nlp_en
+        generate_response = generate_response_en_it
+    elif direction == "it-en":
+        nlp = model_singleton.nlp_it
+        generate_response = generate_response_it_en
+    else:
+        return "Invalid direction selected."
+
+    doc = nlp(input_text)
+    sentences = [sent.text for sent in doc.sents]
+
+    sentence_translations = []
+    for sentence in sentences:
+        sentence_translation = generate_response(sentence)
+        sentence_translations.append(sentence_translation)
+
+    full_translation = " ".join(sentence_translations)
+    return full_translation
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=translate_text,
+    inputs=[gr.Textbox(lines=5, placeholder="Enter text to translate...", label="Input Text"),
+            gr.Dropdown(choices=["en-it", "it-en"], label="Translation Direction")],
+    outputs=gr.Textbox(lines=5, label="Translation"),
+    description="Translation using the super small Foglietta models. Initilization might take a couple of seconds the first time. This spaces uses the Foglietta models for it-en and en-it text translation tasks."
+)
+
+# Launch the interface
+iface.launch()
+

requirements.txt

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+gradio
+transformers
+spacy
+spaces
+torch
+en-core-web-sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.1/en_core_web_sm-3.7.1-py3-none-any.whl
+it-core-news-sm @ https://github.com/explosion/spacy-models/releases/download/it_core_news_sm-3.8.0/it_core_news_sm-3.8.0-py3-none-any.whl