Update app.py

app.py

@@ -1,6 +1,11 @@
 import streamlit as st
 from transformers import T5Tokenizer, T5ForConditionalGeneration
 from pdfminer.high_level import extract_text
+import nltk
+from nltk import sent_tokenize
+
+# Download the punkt tokenizer for sentence segmentation
+nltk.download('punkt')
 
 def main():
     st.title("PDF Translation")    
@@ -10,42 +15,46 @@ def main():
     if uploaded_file is not None:
         # Extract text from pdf
         text = extract_text(uploaded_file)
-        tokenizer = T5Tokenizer.from_pretrained("t5-small")
-        model = T5ForConditionalGeneration.from_pretrained("t5-small")
+        tokenizer = T5Tokenizer.from_pretrained("t5-base")
+        model = T5ForConditionalGeneration.from_pretrained("t5-base")
 
         # Define translation prefixes for each language
         translation_prefixes = {
             "german": "translate English to German: ",
             "french": "translate English to French: "
         }
-
-        # Generate translations for each language
-        translations = {}
-
+        # Variables to track translation state
+        translated_german = False
+        translated_french = False
         # Buttons to trigger translation
         translate_german = st.button("Translate to German")
         translate_french = st.button("Translate to French")
-
-        for language, prefix in translation_prefixes.items():
-            # Translate the entire text, not page by page
-            text_to_translate = prefix + text
-            input_ids = tokenizer(text_to_translate, return_tensors="pt").input_ids
-            outputs = model.generate(input_ids=input_ids, max_length=150, num_beams=4, no_repeat_ngram_size=2)
-            translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-            translations[language] = translated_text
-
-        # Display the translations based on the button clicked
-        if translate_german:
-            display_translation(translations["german"], "German")
-
-        if translate_french:
-            display_translation(translations["french"], "French")
-
-
-def display_translation(translation, language):
+        # Translate and display for German
+        if translate_german and not translated_german:
+            translated_sentences_german = translate_text(text, translation_prefixes["german"], tokenizer, model)
+            display_translation(translated_sentences_german, "German")
+            translated_german = True
+        # Translate and display for French
+        if translate_french and not translated_french:
+            translated_sentences_french = translate_text(text, translation_prefixes["french"], tokenizer, model)
+            display_translation(translated_sentences_french, "French")
+            translated_french = True
+
+def translate_text(text, prefix, tokenizer, model):
+    # Split text into sentences
+    sentences = sent_tokenize(text)
+
+    # Translate each sentence
+    translated_sentences = []
+    for sentence in sentences:
+        text_to_translate = prefix + sentence
+        input_ids = tokenizer(text_to_translate, return_tensors="pt").input_ids
+        outputs = model.generate(input_ids=input_ids, max_length=500, num_beams=4, no_repeat_ngram_size=2)
+        translated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        translated_sentences.append(translated_text)
+
+    return translated_sentences
+
+def display_translation(translations, language):
     st.write(f"\nLanguage: {language}")
-    st.write(f"Translation:\n {translation}")
-
-
-if __name__ == "__main__":
-    main()
+    st.write(f"Translation:\n {' '.join(translations)}")