initial commit

app.py

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+import gradio as gr
+import os, shutil
+import easyocr
+from PIL import Image
+import py3langid as langid
+from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
+
+languages = ['en', 'fr', 'es']
+threshold = 0.2
+langid.set_languages(languages)
+
+def read_text_from_image(img):
+  reader = easyocr.Reader(languages)
+  result = reader.readtext(img)
+  texts = []
+
+  for (bbox, text, prob) in result:
+    # print(f"Text: {text}, Probability: {prob}")
+    # filter by prob
+    if prob > threshold:
+      texts.append(text)
+
+  if len(texts) == 0:
+    raise ValueError("No text detected")
+
+  concatenated_text = " ".join(texts).lower()
+  return concatenated_text
+
+def detect_language(text):
+  lang, prob = langid.classify(text)
+  # print(f"The text {text} is classify as {lang} with probability {prob}")
+  return lang
+
+def translate_to_id(text, lang):
+  query = f"translate from {lang} to ge: {text}"
+
+  tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_1.2B", src_lang=lang)
+  model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_1.2B")
+
+  input_ids = tokenizer(query, return_tensors="pt").input_ids
+  outputs = model.generate(input_ids, forced_bos_token_id=tokenizer.get_lang_id("id"))
+
+  translated = tokenizer.decode(outputs[0], skip_special_tokens=True)
+  return translated
+
+def predict(img):
+  try:
+    text = read_text_from_image(img)
+    lang = detect_language(text)
+    translated = translate_to_id(text, lang)
+
+    # print(f"Text: {text}. Language: {lang}. Translated: {translated}")
+    return text, lang, translated
+  except ValueError as e:
+    print(e)
+    return e
+
+app = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(label="Input Image"),
+    outputs='text',
+)
+
+app.launch()