Update app.py

app.py

@@ -1,20 +1,18 @@
 import streamlit as st
 from transformers import pipeline
+from io import StringIO
 
 unmasker = pipeline('fill-mask', model='dsfsi/zabantu-xlm-roberta')
 
 st.set_page_config(layout="wide")
 
-st.stop_rerun = True
-
 def fill_mask(sentences):
     results = {}
     warnings = []
-    for key, (language, sentence) in sentences.items():
+    for sentence in sentences:
         if "<mask>" in sentence:
-            masked_sentence = sentence.replace('<mask>', unmasker.tokenizer.mask_token)
-            unmasked = unmasker(masked_sentence)
-            results[key] = (language, unmasked)
+            unmasked = unmasker(sentence)
+            results[sentence] = unmasked
         else:
             warnings.append(f"Warning: No <mask> token found in sentence: {sentence}")
     return results, warnings
@@ -23,7 +21,9 @@ def replace_mask(sentence, predicted_word):
     return sentence.replace("<mask>", f"**{predicted_word}**")
 
 st.title("Fill Mask | Zabantu-XLM-Roberta")
-st.markdown("Zabantu-XLMR refers to a fleet of models trained on different combinations of South African Bantu languages.")
+st.write(f"")
+
+st.markdown("Zabantu-XLMR refers to a fleet of models trained on different combinations of South African Bantu languages. It supports the following languages Tshivenda, Nguni languages (Zulu, Xhosa, Swati), Sotho languages (Northern Sotho, Southern Sotho, Setswana), and Xitsonga.")
 
 col1, col2 = st.columns(2)
 
@@ -33,87 +33,165 @@ if 'text_input' not in st.session_state:
 if 'warnings' not in st.session_state:
     st.session_state['warnings'] = []
 
-if 'result' not in st.session_state:
-    st.session_state['result'] = {}
-
-language_options = ['Choose language', 'Zulu', 'Tshivenda', 'Sepedi', 'Tswana', 'Tsonga']
-
-input_sentences = {}
-
 with col1:
-    with st.container():
+    with st.container(border=True):
         st.markdown("Input :clipboard:")
 
-        input1, input2 = st.columns(2)
-        
-        for i in range(5):
-            with input1:
-                language = st.selectbox(f"Select language for sentence {i+1}:", language_options, key=f'language_{i}', index=0)
-            with input2:
-                sentence = st.text_input(f"Enter sentence for {language} (with <mask>):", key=f'text_input_{i}')
-                if sentence:
-                    input_sentences[f'{language.lower()}_{i+1}'] = (language.lower(), sentence)
-
-        button1, button2, _ = st.columns([2, 2, 4])
-
-        if st.button("Test Example"):
-            sample_sentences = {
-                'zulu': "Le ndoda ithi izo <mask> ukudla.",
-                'tshivenda': "Mufana uyo <mask> vhukuma.",
-                'sepedi': "Mosadi o <mask> pheka.",
-                'tswana': "Monna o <mask> tsamaya.",
-                'tsonga': "N'wana wa xisati u <mask> ku tsaka."
-            }
-            st.session_state['result'], st.session_state['warnings'] = fill_mask(sample_sentences)
-
-        if st.button("Submit"):
-            st.session_state['result'], st.session_state['warnings'] = fill_mask(input_sentences)
+        select_options = ['Choose option', 'Enter text input', 'Upload a file(csv/txt)']
+        sample_sentence = "Rabulasi wa <mask> u khou bvelela nga u lima."
+
+        option_selected = st.selectbox(f"Select an input option:", select_options, index=0)
 
+        if option_selected == 'Enter text input':
+            text_input = st.text_area(
+                "Enter sentences with <mask> token(one sentence per line):",
+                value=st.session_state['text_input']
+            )
+        
+            input_sentences = text_input.split("\n")
+    
+            if st.button("Submit",use_container_width=True):
+                result, warnings = fill_mask(input_sentences)
+                st.session_state['warnings'] = warnings 
+
+        if option_selected == 'Upload a file(csv/txt)':
+            
+            uploaded_file = st.file_uploader("Choose a file-(one sentence per line)")
+            if uploaded_file is not None:
+                
+                stringio = StringIO(uploaded_file.getvalue().decode("utf-8"))
+                string_data = stringio.read()
+                
+                input_sentences = string_data.split("\n")
+    
+                if st.button("Submit",use_container_width=True):
+                    result, warnings = fill_mask(input_sentences)
+                    st.session_state['warnings'] = warnings 
+    
         if st.session_state['warnings']:
             for warning in st.session_state['warnings']:
                 st.warning(warning)
 
         st.markdown("Example")
-        st.code({
-            'zulu': "Le ndoda ithi izo <mask> ukudla.",
-            'tshivenda': "Mufana uyo <mask> vhukuma.",
-            'sepedi': "Mosadi o <mask> pheka.",
-            'tswana': "Monna o <mask> tsamaya.",
-            'tsonga': "N'wana wa xisati u <mask> ku tsaka."
-        }, wrap_lines=True)
+        st.code(sample_sentence, wrap_lines=True)
+        if st.button("Test Example",use_container_width=True):
+            result, warnings = fill_mask(sample_sentence.split("\n"))
 
 with col2:
-    with st.container():
+    with st.container(border=True):
         st.markdown("Output :bar_chart:")
-        if st.session_state['result']:
-            for key, (language, predictions) in st.session_state['result'].items():
-                original_sentence = input_sentences[key][1] if key in input_sentences else ""
-                if predictions:
-                    top_prediction = predictions[0]
-                    predicted_word = top_prediction['token_str']
-                    score = top_prediction['score'] * 100
-
-                    st.markdown(f"""
-                    <div class="bar">
-                        <div class="bar-fill" style="width: {score}%;"></div>
-                    </div>
-                    <div class="container">
-                        <div style="align-items: left;">{predicted_word} ({language})</div>
-                        <div style="align-items: right;">{score:.2f}%</div>
-                    </div>
-                    """, unsafe_allow_html=True)
-
-if 'predictions' in locals():  
+        if 'result' in locals() and result:  
+            if len(result) == 1:
+                for sentence, predictions in result.items():
+                    for prediction in predictions:
+                        predicted_word = prediction['token_str']
+                        score = prediction['score'] * 100
+    
+                        st.markdown(f"""
+                        <div class="bar">
+                            <div class="bar-fill" style="width: {score}%;"></div>
+                        </div>
+                        <div class="container">
+                            <div style="align-items: left;">{predicted_word}</div>
+                            <div style="align-items: center;">{score:.2f}%</div>
+                        </div>
+                        """, unsafe_allow_html=True)
+
+            else:
+                index = 0
+                for sentence, predictions in result.items():
+                    index += 1
+                    if predictions:
+                        top_prediction = predictions[0]
+                        predicted_word = top_prediction['token_str']
+                        score = top_prediction['score'] * 100
+    
+                        st.markdown(f"""
+                        <div class="bar">
+                            <div class="bar-fill" style="width: {score}%;"></div>
+                        </div>
+                        <div class="container">
+                            <div style="align-items: left;">{predicted_word} (line {index})</div>
+                            <div style="align-items: right;">{score:.2f}%</div>
+                        </div>
+                        """, unsafe_allow_html=True)
+
+                
+if 'result' in locals():  
     if result:
-        for language, language_predictions in result.items(): 
-            original_sentence = sample_sentence[language]
-            predicted_sentence = replace_mask(original_sentence, language_predictions[0]['token_str'])  
-            st.write(f"{language}: {predicted_sentence}\n")
+        line = 0
+        for sentence, predictions in result.items():
+            line += 1
+            predicted_word = predictions[0]['token_str']
+            full_sentence = replace_mask(sentence, predicted_word)
+            st.write(f"**Sentence {line}:** {full_sentence }")
 
 css = """
 <style>
 footer {display:none !important;}
 
+.gr-button-primary {
+    z-index: 14;
+    height: 43px;
+    width: 130px;
+    left: 0px;
+    top: 0px;
+    padding: 0px;
+    cursor: pointer !important; 
+    background: none rgb(17, 20, 45) !important;
+    border: none !important;
+    text-align: center !important;
+    font-family: Poppins !important;
+    font-size: 14px !important;
+    font-weight: 500 !important;
+    color: rgb(255, 255, 255) !important;
+    line-height: 1 !important;
+    border-radius: 12px !important;
+    transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important;
+    box-shadow: none !important;
+}
+.gr-button-primary:hover{
+    z-index: 14;
+    height: 43px;
+    width: 130px;
+    left: 0px;
+    top: 0px;
+    padding: 0px;
+    cursor: pointer !important;
+    background: none rgb(66, 133, 244) !important;
+    border: none !important;
+    text-align: center !important;
+    font-family: Poppins !important;
+    font-size: 14px !important;
+    font-weight: 500 !important;
+    color: rgb(255, 255, 255) !important;
+    line-height: 1 !important;
+    border-radius: 12px !important;
+    transition: box-shadow 200ms ease 0s, background 200ms ease 0s !important;
+    box-shadow: rgb(0 0 0 / 23%) 0px 1px 7px 0px !important;
+}
+.hover\:bg-orange-50:hover {
+    --tw-bg-opacity: 1 !important;
+    background-color: rgb(229,225,255) !important;
+}
+.to-orange-200 {
+    --tw-gradient-to: rgb(37 56 133 / 37%) !important;
+}
+.from-orange-400 {
+    --tw-gradient-from: rgb(17, 20, 45) !important;
+    --tw-gradient-to: rgb(255 150 51 / 0);
+    --tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to) !important;
+}
+.group-hover\:from-orange-500{
+    --tw-gradient-from:rgb(17, 20, 45) !important; 
+    --tw-gradient-to: rgb(37 56 133 / 37%);
+    --tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to) !important;
+}
+.group:hover .group-hover\:text-orange-500{
+    --tw-text-opacity: 1 !important;
+    color:rgb(37 56 133 / var(--tw-text-opacity)) !important;
+}
+
 .container {
     display: flex;
     justify-content: space-between;
@@ -122,6 +200,7 @@ footer {display:none !important;}
     width: 100%;
 }
 .bar {
+    # width: 70%;
     background-color: #e6e6e6;
     border-radius: 12px;
     overflow: hidden;
@@ -133,6 +212,8 @@ footer {display:none !important;}
     height: 100%;
     border-radius: 12px;
 }
+
 </style>
 """
+
 st.markdown(css, unsafe_allow_html=True)