Upload app.py

app.py

@@ -0,0 +1,143 @@
+import streamlit as st
+from PIL import Image
+from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer
+from streamlit_extras.app_logo import add_logo
+
+
+def logo():
+	add_logo("vocali_logo.jpeg", height=300)
+
+
+def get_result_text_es_pt (list_entity, text, lang):
+    result_words = []
+    if lang == "es":
+        punc_tags = ['¿', '?', '¡', '!', ',', '.', ':']
+    else:
+        punc_tags = ['?', '!', ',', '.', ':']
+    
+    for entity in list_entity: 
+        tag = entity["entity"]
+        word = entity["word"]
+        start = entity["start"]
+        end = entity["end"]
+        
+        # check punctuation
+        punc_in = next((p for p in punc_tags if p in tag), "")
+                
+        subword = False
+        # check subwords
+        if word[0] == "#": 
+            subword = True
+            if punc_in != "":
+                word = result_words[-1].replace(punc_in, "") + text[start:end]
+            else: 
+                word = result_words[-1] + text[start:end]
+        
+        if tag == "l": 
+            word = word 
+        elif tag == "u":
+            word = word.capitalize()
+        # case with punctuation
+        else:
+            if tag[-1] == "l":
+                word = (punc_in + word) if punc_in in ["¿", "¡"] else (word + punc_in)
+            elif tag[-1] == "u":
+                word = (punc_in + word.capitalize()) if punc_in in ["¿", "¡"] else (word.capitalize() + punc_in)     
+        
+		if tag != "l":
+			word = '<span style="font-weight:bold; color:rgb(142, 208, 129);">' + word + '</span>'
+		
+        if subword == True: 
+            result_words[-1] = word
+        else:
+            result_words.append(word)
+
+    return " ".join(result_words)
+            
+
+
+def get_result_text_ca (list_entity, text):
+    result_words = []
+    punc_tags = ['?', '!', ',', '.', ':']
+    
+    for entity in list_entity: 
+        start = entity["start"]
+        end = entity["end"]
+        tag = entity["entity"]
+        word = entity["word"]
+        
+        # check punctuation
+        punc_in = next((p for p in punc_tags if p in tag), "")
+                
+        subword = False
+        # check subwords
+        if word[0] != "Ġ": 
+            subword = True
+            if punc_in != "":
+                word = result_words[-1].replace(punc_in, "") + text[start:end]
+            else: 
+                word = result_words[-1] + text[start:end]
+        else: 
+            word = text[start:end]
+        
+        if tag == "l": 
+            word = word 
+        elif tag == "u":
+            word = word.capitalize()
+        # case with punctuation
+        else:
+            if tag[-1] == "l":
+                word = (punc_in + word) if punc_in in ["¿", "¡"] else (word + punc_in)
+            elif tag[-1] == "u":
+                word = (punc_in + word.capitalize()) if punc_in in ["¿", "¡"] else (word.capitalize() + punc_in)     
+        
+		if tag != "l":
+			word = '<span style="font-weight:bold; color:rgb(142, 208, 129);">' + word + '</span>'
+			
+        if subword == True: 
+            result_words[-1] = word
+        else:
+            result_words.append(word)
+
+    return " ".join(result_words)
+	
+
+if __name__ == "__main__":
+	logo()
+	st.title('Sanivert Punctuation And Capitalization Restoration')
+	
+	model_es = AutoModelForTokenClassification.from_pretrained("VOCALINLP/spanish_capitalization_punctuation_restoration_sanivert")
+    tokenizer_es = AutoTokenizer.from_pretrained("VOCALINLP/spanish_capitalization_punctuation_restoration_sanivert")
+	pipe_es = pipeline("token-classification", model=model_es, tokenizer=tokenizer_es)
+	
+	model_ca = ModelForTokenClassification.from_pretrained("VOCALINLP/catalan_capitalization_punctuation_restoration_sanivert")
+    tokenizer_ca = AutoTokenizer.from_pretrained("VOCALINLP/catalan_capitalization_punctuation_restoration_sanivert")
+	pipe_ca = pipeline("token-classification", model=model_ca, tokenizer=tokenizer_ca)
+	
+	model_pt = AutoModelForTokenClassification.from_pretrained("VOCALINLP/portuguese_capitalization_punctuation_restoration_sanivert")
+    tokenizer_pt = AutoTokenizer.from_pretrained("VOCALINLP/portuguese_capitalization_punctuation_restoration_sanivert")
+	pipe_pt = pipeline("token-classification", model=model_ca, tokenizer=tokenizer_ca)
+	
+	input_text = st.selectbox(
+      label = "Choose an language",
+      options = ["Spanish", "Portuguese", "Catalan"]
+	)
+	
+	st.subheader("Enter the text to be analyzed.")
+	text = st.text_input('Enter text') #text is stored in this variable    
+	if input_text == "Spanish": 
+		result_pipe = pipe_es(text)
+		out = get_result_text_es_pt(result_pipe, text, "es")
+	elif input_text == "Portuguese": 
+		result_pipe = pipe_pt(text)
+		out = get_result_text_es_pt(result_pipe, text, "pt")
+	elif input_text == "Catalan": 
+		result_pipe = pipe_ca(text)
+		out = get_result_text_ca(result_pipe, text)
+	
+	out = get_prediction(text, input_text)
+	st.markdown(out, unsafe_allow_html=True)
+	text = ""
+	
+	
+