Update app.py

app.py

@@ -7,45 +7,45 @@ from sklearn.metrics.pairwise import paired_cosine_distances
 from sklearn.preprocessing import normalize
 from rolaser import RoLaserEncoder
 
-laser_checkpoint = f"{os.environ['LASER']}/models/laser2.pt"
-laser_vocab = f"{os.environ['LASER']}/models/laser2.cvocab"
-laser_tokenizer = 'spm'
-laser_model = RoLaserEncoder(model_path=laser_checkpoint, vocab=laser_vocab, tokenizer=laser_tokenizer)
-
-rolaser_checkpoint = f"{os.environ['ROLASER']}/models/rolaser.pt"
-rolaser_vocab = f"{os.environ['ROLASER']}/models/rolaser.cvocab"
-rolaser_tokenizer = 'roberta'
-rolaser_model = RoLaserEncoder(model_path=rolaser_checkpoint, vocab=rolaser_vocab, tokenizer=rolaser_tokenizer)
-
-c_rolaser_checkpoint = f"{os.environ['ROLASER']}/models/c-rolaser.pt"
-c_rolaser_vocab = f"{os.environ['ROLASER']}/models/c-rolaser.cvocab"
-c_rolaser_tokenizer = 'char'
-c_rolaser_model = RoLaserEncoder(model_path=c_rolaser_checkpoint, vocab=c_rolaser_vocab, tokenizer=c_rolaser_tokenizer)
-
-
-STD_SENTENCES = ['See you tomorrow.'] * 10
-UGC_SENTENCES = [
-    'See you t03orro3.',
-    'C. U. tomorrow.',
-    'sea you tomorrow.',
-    'See yo utomorrow.',
-    'See you tmrw.',
-    'See you tkmoerow.',
-    'Cu 2moro.',
-    'See yow tomorrow.',
-    'C. Yew tomorrow.',
-    'c ya 2morrow.'
-]
-
-def add_text_inputs(i):
-    col1, col2 = st.columns(2)
-    with col1:
-        text_input1 = st.text_input('Enter standard text here:', key=f'std{i}', value=STD_SENTENCES[i], label_visibility='collapsed')
-    with col2:
-        text_input2 = st.text_input('Enter non-standard text here:', key=f'ugc{i}', value=UGC_SENTENCES[i], label_visibility='collapsed')
-    return text_input1, text_input2
+@st.cache_resource(show_spinner=False)
+def load_models():
+    laser_checkpoint = f"{os.environ['LASER']}/models/laser2.pt"
+    laser_vocab = f"{os.environ['LASER']}/models/laser2.cvocab"
+    laser_tokenizer = 'spm'
+    laser_model = RoLaserEncoder(model_path=laser_checkpoint, vocab=laser_vocab, tokenizer=laser_tokenizer)
+
+    rolaser_checkpoint = f"{os.environ['ROLASER']}/models/rolaser.pt"
+    rolaser_vocab = f"{os.environ['ROLASER']}/models/rolaser.cvocab"
+    rolaser_tokenizer = 'roberta'
+    rolaser_model = RoLaserEncoder(model_path=rolaser_checkpoint, vocab=rolaser_vocab, tokenizer=rolaser_tokenizer)
+
+    c_rolaser_checkpoint = f"{os.environ['ROLASER']}/models/c-rolaser.pt"
+    c_rolaser_vocab = f"{os.environ['ROLASER']}/models/c-rolaser.cvocab"
+    c_rolaser_tokenizer = 'char'
+    c_rolaser_model = RoLaserEncoder(model_path=c_rolaser_checkpoint, vocab=c_rolaser_vocab, tokenizer=c_rolaser_tokenizer)
+    return laser_model, rolaser_model, c_rolaser_model
+
+@st.cache_data(show_spinner=False)
+def load_sample_data():
+    STD_SENTENCES = ['See you tomorrow.'] * 10
+    UGC_SENTENCES = [
+        'See you t03orro3.',
+        'C. U. tomorrow.',
+        'sea you tomorrow.',
+        'See yo utomorrow.',
+        'See you tmrw.',
+        'See you tkmoerow.',
+        'Cu 2moro.',
+        'See yow tomorrow.',
+        'C. Yew tomorrow.',
+        'c ya 2morrow.'
+    ]
+    return STD_SENTENCES, UGC_SENTENCES
 
 def main():
+    sample_std, sample_ugc = load_sample_data()
+    laser_model, rolaser_model, c_rolaser_model = load_models()
+
     st.title('Pairwise Cosine Distance Calculator')
 
     info = '''
@@ -59,52 +59,60 @@ def main():
             
     st.header('Standard and Non-standard Text Input Pairs:')
 
-    num_pairs = st.sidebar.number_input('Number of Text Input Pairs', min_value=1, max_value=10, value=5)
-
-    col1, col2 = st.columns(2)
-    with col1:
-        st.write('Enter standard text here:')
-    with col2:
-        st.write('Enter non-standard text here:')
-
-    std_text_inputs = []
-    ugc_text_inputs = []
-    for i in range(num_pairs):
-        pair = add_text_inputs(i)
-        std_text_inputs.append(pair[0])
-        ugc_text_inputs.append(pair[1])
-
-    st.caption('*The models are case-insensitive: all text will be lowercased.*')
-    if st.button('Submit'):
-        X_std_laser = normalize(laser_model.encode(std_text_inputs))
-        X_ugc_laser = normalize(laser_model.encode(ugc_text_inputs))
-        X_cos_laser = paired_cosine_distances(X_std_laser, X_ugc_laser)
-
-        X_std_rolaser = normalize(rolaser_model.encode(std_text_inputs))
-        X_ugc_rolaser = normalize(rolaser_model.encode(ugc_text_inputs))
-        X_cos_rolaser = paired_cosine_distances(X_std_rolaser, X_ugc_rolaser)
-
-        X_std_c_rolaser = normalize(c_rolaser_model.encode(std_text_inputs))
-        X_ugc_c_rolaser = normalize(c_rolaser_model.encode(ugc_text_inputs))
-        X_cos_c_rolaser = paired_cosine_distances(X_std_c_rolaser, X_ugc_c_rolaser)
-
-        outputs = pd.DataFrame(columns=[ 'model', 'pair', 'ugc', 'std', 'cos'])
-        outputs['model'] = np.repeat(['LASER', 'RoLASER', 'c-RoLASER'], num_pairs)   
-        outputs['pair'] = np.tile(np.arange(1,num_pairs+1), 3)
-        outputs['std'] = np.tile(std_text_inputs, 3)
-        outputs['ugc'] = np.tile(ugc_text_inputs, 3)
-        outputs['cos'] = np.concatenate([X_cos_laser, X_cos_rolaser, X_cos_c_rolaser])
-
-        st.header('Cosine Distance Scores:')
-        st.caption('*This bar plot is interactive: Hover on the bars to display values. Click on the legend items to filter models.*')
-        fig = px.bar(outputs, x='pair', y='cos', color='model', barmode='group', hover_data=['ugc', 'std'])
-        fig.update_xaxes(title_text='Text Input Pair')
-        fig.update_yaxes(title_text='Cosine Distance')
-        st.plotly_chart(fig, use_container_width=True)
-
-        st.header('Average Cosine Distance Scores:')
-        st.caption('*This data table is interactive: Click on a column header to sort values.*')
-        st.write(outputs.groupby('model')['cos'].describe())
+    num_pairs = st.sidebar.number_input('Number of Text Input Pairs (1-10)', min_value=1, max_value=10, value=5)
+
+    with st.form('text_input_form'):
+        col1, col2 = st.columns(2)
+        with col1:
+            st.write('Enter standard text here:')
+        with col2:
+            st.write('Enter non-standard text here:')
+
+        std_text_inputs = []
+        ugc_text_inputs = []
+
+        for i in range(num_pairs):
+            col1, col2 = st.columns(2)
+            with col1:
+                text_input1 = st.text_input('Enter standard text here:', key=f'std{i}', value=sample_std[i], label_visibility='collapsed')
+                std_text_inputs.append(text_input1)
+            with col2:
+                text_input2 = st.text_input('Enter non-standard text here:', key=f'ugc{i}', value=sample_ugc[i], label_visibility='collapsed')
+                ugc_text_inputs.append(text_input2)
+
+        st.caption('*The models are case-insensitive: all text will be lowercased.*')
+
+        st.form_submit_button('Compute')
+    
+    X_std_laser = normalize(laser_model.encode(std_text_inputs))
+    X_ugc_laser = normalize(laser_model.encode(ugc_text_inputs))
+    X_cos_laser = paired_cosine_distances(X_std_laser, X_ugc_laser)
+
+    X_std_rolaser = normalize(rolaser_model.encode(std_text_inputs))
+    X_ugc_rolaser = normalize(rolaser_model.encode(ugc_text_inputs))
+    X_cos_rolaser = paired_cosine_distances(X_std_rolaser, X_ugc_rolaser)
+
+    X_std_c_rolaser = normalize(c_rolaser_model.encode(std_text_inputs))
+    X_ugc_c_rolaser = normalize(c_rolaser_model.encode(ugc_text_inputs))
+    X_cos_c_rolaser = paired_cosine_distances(X_std_c_rolaser, X_ugc_c_rolaser)
+
+    outputs = pd.DataFrame(columns=[ 'model', 'pair', 'ugc', 'std', 'cos'])
+    outputs['model'] = np.repeat(['LASER', 'RoLASER', 'c-RoLASER'], num_pairs)   
+    outputs['pair'] = np.tile(np.arange(1,num_pairs+1), 3)
+    outputs['std'] = np.tile(std_text_inputs, 3)
+    outputs['ugc'] = np.tile(ugc_text_inputs, 3)
+    outputs['cos'] = np.concatenate([X_cos_laser, X_cos_rolaser, X_cos_c_rolaser])
+
+    st.header('Cosine Distance Scores:')
+    st.caption('*This bar plot is interactive: Hover on the bars to display values. Click on the legend items to filter models.*')
+    fig = px.bar(outputs, x='pair', y='cos', color='model', barmode='group', hover_data=['ugc', 'std'])
+    fig.update_xaxes(title_text='Text Input Pair')
+    fig.update_yaxes(title_text='Cosine Distance')
+    st.plotly_chart(fig, use_container_width=True)
+
+    st.header('Average Cosine Distance Scores:')
+    st.caption('*This data table is interactive: Click on a column header to sort values.*')
+    st.write(outputs.groupby('model')['cos'].describe())
 
         
 if __name__ == "__main__":