test

app-bak.py

@@ -0,0 +1,172 @@
+import streamlit as st
+import os
+import io
+from transformers import M2M100Tokenizer, M2M100ForConditionalGeneration
+import time
+import json
+from typing import List
+import torch
+import random
+import logging
+
+if torch.cuda.is_available():
+    device = torch.device("cuda:0")
+else:
+    device = torch.device("cpu")
+    logging.warning("GPU not found, using CPU, translation will be very slow.")
+
+st.cache(suppress_st_warning=True, allow_output_mutation=True)
+st.set_page_config(page_title="M2M100 Translator")
+
+lang_id = {
+    "Afrikaans": "af",
+    "Amharic": "am",
+    "Arabic": "ar",
+    "Asturian": "ast",
+    "Azerbaijani": "az",
+    "Bashkir": "ba",
+    "Belarusian": "be",
+    "Bulgarian": "bg",
+    "Bengali": "bn",
+    "Breton": "br",
+    "Bosnian": "bs",
+    "Catalan": "ca",
+    "Cebuano": "ceb",
+    "Czech": "cs",
+    "Welsh": "cy",
+    "Danish": "da",
+    "German": "de",
+    "Greeek": "el",
+    "English": "en",
+    "Spanish": "es",
+    "Estonian": "et",
+    "Persian": "fa",
+    "Fulah": "ff",
+    "Finnish": "fi",
+    "French": "fr",
+    "Western Frisian": "fy",
+    "Irish": "ga",
+    "Gaelic": "gd",
+    "Galician": "gl",
+    "Gujarati": "gu",
+    "Hausa": "ha",
+    "Hebrew": "he",
+    "Hindi": "hi",
+    "Croatian": "hr",
+    "Haitian": "ht",
+    "Hungarian": "hu",
+    "Armenian": "hy",
+    "Indonesian": "id",
+    "Igbo": "ig",
+    "Iloko": "ilo",
+    "Icelandic": "is",
+    "Italian": "it",
+    "Japanese": "ja",
+    "Javanese": "jv",
+    "Georgian": "ka",
+    "Kazakh": "kk",
+    "Central Khmer": "km",
+    "Kannada": "kn",
+    "Korean": "ko",
+    "Luxembourgish": "lb",
+    "Ganda": "lg",
+    "Lingala": "ln",
+    "Lao": "lo",
+    "Lithuanian": "lt",
+    "Latvian": "lv",
+    "Malagasy": "mg",
+    "Macedonian": "mk",
+    "Malayalam": "ml",
+    "Mongolian": "mn",
+    "Marathi": "mr",
+    "Malay": "ms",
+    "Burmese": "my",
+    "Nepali": "ne",
+    "Dutch": "nl",
+    "Norwegian": "no",
+    "Northern Sotho": "ns",
+    "Occitan": "oc",
+    "Oriya": "or",
+    "Panjabi": "pa",
+    "Polish": "pl",
+    "Pushto": "ps",
+    "Portuguese": "pt",
+    "Romanian": "ro",
+    "Russian": "ru",
+    "Sindhi": "sd",
+    "Sinhala": "si",
+    "Slovak": "sk",
+    "Slovenian": "sl",
+    "Somali": "so",
+    "Albanian": "sq",
+    "Serbian": "sr",
+    "Swati": "ss",
+    "Sundanese": "su",
+    "Swedish": "sv",
+    "Swahili": "sw",
+    "Tamil": "ta",
+    "Thai": "th",
+    "Tagalog": "tl",
+    "Tswana": "tn",
+    "Turkish": "tr",
+    "Ukrainian": "uk",
+    "Urdu": "ur",
+    "Uzbek": "uz",
+    "Vietnamese": "vi",
+    "Wolof": "wo",
+    "Xhosa": "xh",
+    "Yiddish": "yi",
+    "Yoruba": "yo",
+    "Chinese": "zh",
+    "Zulu": "zu",
+}
+
+
+@st.cache(suppress_st_warning=True, allow_output_mutation=True)
+def load_model(
+    pretrained_model: str = "facebook/m2m100_1.2B",
+    cache_dir: str = "models/",
+):
+    tokenizer = M2M100Tokenizer.from_pretrained(pretrained_model, cache_dir=cache_dir)
+    model = M2M100ForConditionalGeneration.from_pretrained(
+        pretrained_model, cache_dir=cache_dir
+    ).to(device)
+    model.eval()
+    return tokenizer, model
+
+
+st.title("M2M100 Translator")
+st.write("M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation. It was introduced in this paper https://arxiv.org/abs/2010.11125 and first released in https://github.com/pytorch/fairseq/tree/master/examples/m2m_100 repository. The model that can directly translate between the 9,900 directions of 100 languages.\n")
+
+st.write(" This demo uses the facebook/m2m100_1.2B model. For local inference see https://github.com/ikergarcia1996/Easy-Translate")
+
+
+user_input: str = st.text_area(
+    "Input text",
+    height=200,
+    max_chars=5120,
+)
+
+source_lang = st.selectbox(label="Source language", options=list(lang_id.keys()))
+target_lang = st.selectbox(label="Target language", options=list(lang_id.keys()))
+
+if st.button("Run"):
+    time_start = time.time()
+    tokenizer, model = load_model()
+
+    src_lang = lang_id[source_lang]
+    trg_lang = lang_id[target_lang]
+    tokenizer.src_lang = src_lang
+    with torch.no_grad():
+        encoded_input = tokenizer(user_input, return_tensors="pt").to(device)
+        generated_tokens = model.generate(
+            **encoded_input, forced_bos_token_id=tokenizer.get_lang_id(trg_lang)
+        )
+        translated_text = tokenizer.batch_decode(
+            generated_tokens, skip_special_tokens=True
+        )[0]
+
+    time_end = time.time()
+    st.success(translated_text)
+
+    st.write(f"Computation time: {round((time_end-time_start),3)} segs")

app.py

@@ -1,12 +1,8 @@
 import streamlit as st
-import os
-import io
 from transformers import M2M100Tokenizer, M2M100ForConditionalGeneration
 import time
-import json
 from typing import List
 import torch
-import random
 import logging
 
 if torch.cuda.is_available():