Update app.py

app.py

@@ -1,4 +1,64 @@
 import streamlit as st
+from transformers import MarianTokenizer, MarianMTModel
+from ar_correct import ar_correct
+import mishkal.tashkeel
+from transformers import BertTokenizer, AutoModelForSeq2SeqLM, pipeline
+from arabert.preprocess import ArabertPreprocessor
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+# Initialize Mishkal vocalizer
+vocalizer = mishkal.tashkeel.TashkeelClass()
+
+# Initialize Marian tokenizer and model for translation
+mname = "marefa-nlp/marefa-mt-en-ar"
+tokenizer = MarianTokenizer.from_pretrained(mname)
+model = MarianMTModel.from_pretrained(mname)
+
+# Initialize BERT tokenizer and model for summarization
+model_name = "malmarjeh/mbert2mbert-arabic-text-summarization"
+preprocessor = ArabertPreprocessor(model_name="")
+tokenizer_summarization = BertTokenizer.from_pretrained(model_name)
+model_summarization = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+pipeline_summarization = pipeline("text2text-generation", model=model_summarization, tokenizer=tokenizer_summarization)
+
+
+def main():
+    st.title("U3reb Demo")
+
+    # Text Input
+    input_text = st.text_area("Enter Arabic Text:")
+
+    # Tokenization
+    st.subheader("Tokenization (Mishkal)")
+    if input_text:
+        text_mishkal = vocalizer.tashkeel(input_text)
+        st.write("Tokenized Text (with diacritics):", text_mishkal)
+
+    # Translation
+    st.subheader("Translation")
+    if input_text:
+        translated_tokens = model.generate(**tokenizer.prepare_seq2seq_batch([input_text], return_tensors="pt"))
+        translated_text = [tokenizer.decode(t, skip_special_tokens=True) for t in translated_tokens]
+        st.write("Translated Text:", translated_text)
+
+    # Arabic Text Correction
+    st.subheader("Arabic Text Correction (ar_correct)")
+    if input_text:
+        corrected_text = ar_correct(input_text)
+        st.write("Corrected Text:", corrected_text)
+
+    # Text Summarization
+    st.subheader("Text Summarization")
+    if input_text:
+        preprocessed_text = preprocessor.preprocess(input_text)
+        result = pipeline_summarization(preprocessed_text,
+                                        pad_token_id=tokenizer_summarization.eos_token_id,
+                                        num_beams=3,
+                                        repetition_penalty=3.0,
+                                        max_length=200,
+                                        length_penalty=1.0,
+                                        no_repeat_ngram_size=3)[0]['generated_text']
+        st.write("Summarized Text:", result)
+
+
+if __name__ == "__main__":
+    main()