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Update app.py
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app.py
CHANGED
@@ -13,7 +13,12 @@ import pkg_resources
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import folium
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import country_converter as coco
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import time
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current_dir = os.path.dirname(os.path.abspath(__file__))
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font_path = os.path.join(current_dir, "ArabicR2013-J25x.ttf")
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@@ -33,7 +38,7 @@ ARABIC_STOP_WORDS = {
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'ู
ูุฐ', 'ููุณ', 'ุญูุซ', 'ููุงู', 'ุฌุฏุง', 'ุฐุงุช', 'ุถู
ู', 'ุงูู', 'ูุฏู',
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'ุนููู', 'ู
ุซู', 'ุฃู
ุง', 'ูุฏู', 'ููู', 'ููู
', 'ููู', 'ุงูุถุง', 'ูุงุฒู
',
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'ูุฌุจ', 'ุตุงุฑ', 'ุตุงุฑุช', 'ุถุฏ', 'ูุง', 'ูุง', 'ุงู
ุง',
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-
'ุจูุง', 'ุงู', 'ุจู', 'ุงูู', 'ูู
ุง', 'ุงูุง', 'ุงููู', 'ูู', 'ูู', 'ููุช',
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# Middle group prefixed with "ู"
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'ูุซู
', 'ูุฃู', 'ูู', 'ูุจ', 'ูู', 'ููู', 'ูุงู',
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@@ -402,6 +407,10 @@ def process_and_summarize(df, bert_tokenizer, bert_model, emotion_classifier, to
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all_emotions = []
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embeddings = []
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for i, text in enumerate(texts):
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try:
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embedding = get_embedding_for_text(text, bert_tokenizer, bert_model)
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@@ -413,6 +422,9 @@ def process_and_summarize(df, bert_tokenizer, bert_model, emotion_classifier, to
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except Exception as e:
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st.warning(f"Error generating embedding for text {i+1} in {country}: {str(e)}")
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continue
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progress = (i + 1) / len(texts) * 0.4
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progress_bar.progress(progress, text=f"Generated embeddings for {i+1}/{len(texts)} poems...")
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@@ -420,9 +432,13 @@ def process_and_summarize(df, bert_tokenizer, bert_model, emotion_classifier, to
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texts = texts[:len(embeddings)]
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embeddings = np.array(embeddings)
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for i, text in enumerate(texts):
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emotion = classify_emotion(text, emotion_classifier)
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all_emotions.append(emotion)
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progress = 0.4 + ((i + 1) / len(texts) * 0.3)
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progress_bar.progress(progress, text=f"Classified emotions for {i+1}/{len(texts)} poems...")
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import folium
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import country_converter as coco
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import time
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import gc
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def clear_memory():
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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gc.collect()
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current_dir = os.path.dirname(os.path.abspath(__file__))
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font_path = os.path.join(current_dir, "ArabicR2013-J25x.ttf")
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'ู
ูุฐ', 'ููุณ', 'ุญูุซ', 'ููุงู', 'ุฌุฏุง', 'ุฐุงุช', 'ุถู
ู', 'ุงูู', 'ูุฏู',
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'ุนููู', 'ู
ุซู', 'ุฃู
ุง', 'ูุฏู', 'ููู', 'ููู
', 'ููู', 'ุงูุถุง', 'ูุงุฒู
',
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'ูุฌุจ', 'ุตุงุฑ', 'ุตุงุฑุช', 'ุถุฏ', 'ูุง', 'ูุง', 'ุงู
ุง',
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'ุจูุง', 'ุงู', 'ุจู', 'ุงูู', 'ูู
ุง', 'ุงูุง', 'ุงููู', 'ูู', 'ูู','ุงุฐุง','ุจูุง','ุงู','ูุฏูู','ูุฏูู', 'ููุช',
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# Middle group prefixed with "ู"
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'ูุซู
', 'ูุฃู', 'ูู', 'ูุจ', 'ูู', 'ููู', 'ูุงู',
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all_emotions = []
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embeddings = []
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clear_memory()
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for i, text in enumerate(texts):
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try:
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embedding = get_embedding_for_text(text, bert_tokenizer, bert_model)
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except Exception as e:
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st.warning(f"Error generating embedding for text {i+1} in {country}: {str(e)}")
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continue
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if i % 10 == 0:
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clear_memory()
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progress = (i + 1) / len(texts) * 0.4
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progress_bar.progress(progress, text=f"Generated embeddings for {i+1}/{len(texts)} poems...")
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texts = texts[:len(embeddings)]
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embeddings = np.array(embeddings)
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clear_memory()
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for i, text in enumerate(texts):
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emotion = classify_emotion(text, emotion_classifier)
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all_emotions.append(emotion)
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if i % 10 == 0:
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clear_memory()
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progress = 0.4 + ((i + 1) / len(texts) * 0.3)
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progress_bar.progress(progress, text=f"Classified emotions for {i+1}/{len(texts)} poems...")
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