correction for latency

app.py

@@ -1,24 +1,25 @@
 import streamlit as st
 import pandas as pd
+import plotly.express as px
 from src import *
 
-single = SinglePrediction()
-batch = BatchPrediction()
+model = ModelLoader()
+prediction = PredictionServices(model.Model, model.Tokenizer)
 
 def single_predict(text):
-    preds, fig = single.predict(text)
+    preds = prediction.single_predict(text)
 
     if preds < 0.5:
         st.success(f'Non Toxic Comment!!! :thumbsup:')
-        st.plotly_chart(fig, theme="streamlit", use_container_width=True)
     else:
         st.error(f'Toxic Comment!!! :thumbsdown:')
-        st.plotly_chart(fig, theme="streamlit", use_container_width=True)
+
+    prediction.plot(preds)
 
 def batch_predict(data):
-    if batch.data_validation(data):
+    if prediction.data_validation(data):
         st.success(f'Data Validation Successfull :thumbsup:')
-        preds = batch.predict(data)
+        preds = prediction.batch_predict(data)
         return preds.to_csv(index=False).encode('utf-8')
     else:
         st.error(f'Data Validation Failed :thumbsdown:')
@@ -29,9 +30,7 @@ choice = st.sidebar.radio("Menu",menu)
 
 if choice=="Single Value Prediciton":
     st.subheader("Prediction")
-    #comment = st.text_input("Comment", 'Enter your comment here') 
-    #trigger = st.button('Predict', on_click=single_predict(comment))
-    form = st.form("my_form")
+    form = st.form("comment_form")
     comment = form.text_input("Enter comment")
     form.form_submit_button("Predict",on_click=single_predict(comment))
 else:

requirements.txt

@@ -5,3 +5,4 @@ transformers[sentencepiece]
 streamlit
 matplotlib
 seaborn
+plotly

src/__init__.py

@@ -1,3 +1,2 @@
-
-from src.batch_predict import *
-from src.single_predict import *
+from predict import *
+from model_loader import *

src/model_loader.py

@@ -0,0 +1,10 @@
+import tensorflow as tf
+import transformers
+from transformers import AutoTokenizer
+from src.constants import *
+
+
+class ModelLoader:
+    def __init__(self):
+        self.Model = tf.keras.models.load_model(MODEL_PATH)
+        self.Tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_PATH)

src/{batch_predict.py → predict.py}

@@ -1,17 +1,19 @@
 import pandas as pd
 import numpy as np
 import tensorflow as tf
+import plotly.express as px
 import transformers
 from transformers import AutoTokenizer
 import os
 from src.constants import *
 import re
+import streamlit as st
 
 
-class BatchPrediction:
-    def __init__(self):
-        self.model = tf.keras.models.load_model(MODEL_PATH)
-        self.tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_PATH)
+class PredictionServices:
+    def __init__(self, model, tokenizer):
+        self.model = model
+        self.tokenizer = tokenizer
 
     def tokenizer(self, text:str):
         tokens = self.tokenizer(text, 
@@ -23,6 +25,17 @@ class BatchPrediction:
                                 return_token_type_ids = False)
         return dict(tokens)
     
+    def plot(self, pred):
+        fig = px.bar(x=[round(pred), round(1-pred)], 
+                     y=['toxic', 'non-toxic'],
+                     width=500, height=250, 
+                     template="plotly_dark", 
+                     text_auto='1', 
+                     title="Probabilities(%)")
+        fig.update_traces(width=0.3,textfont_size=15, textangle=0, textposition="outside")
+        fig.update_layout(yaxis_title=None,xaxis_title=None)
+        st.plotly_chart(fig, theme="streamlit", use_container_width=True)
+    
     def data_validation(data):
         df = pd.read_csv(data)
         status=True
@@ -31,7 +44,7 @@ class BatchPrediction:
                 status=False
         return status
                 
-    def predict(self, data):
+    def batch_predict(self, data):
         try:
             df = pd.read_csv(data)
             df.dropna(inplace=True)
@@ -41,5 +54,14 @@ class BatchPrediction:
             df['probabilities'] = preds
             df['toxic'] = np.where(df['probabilities']>0.5, 1, 0)
             return df
+        except Exception as e:
+            print(e)
+
+    def single_predict(self, text:str):
+        try:
+            text = re.sub('\n',' ',text).strip()
+            input = self.tokenizer(text)
+            pred = self.model.predict(input)[0][0]
+            return pred
         except Exception as e:
             print(e)

src/single_predict.py

@@ -1,46 +0,0 @@
-import pandas as pd
-import numpy as np
-import tensorflow as tf
-import plotly.express as px
-import transformers
-from transformers import AutoTokenizer
-import os
-from src.constants import *
-import re
-
-
-class SinglePrediction:
-    def __init__(self):
-        self.model = tf.keras.models.load_model(MODEL_PATH)
-        self.tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_PATH)
-
-    def tokenizer(self, text:str):
-        tokens = self.tokenizer(text, 
-                                max_length=MAX_LEN, 
-                                truncation=True, 
-                                padding="max_length",
-                                add_special_tokens=True,
-                                return_tensors="tf",
-                                return_token_type_ids = False)
-        return dict(tokens)
-    
-    def plot(self, pred):
-        fig = px.bar(x=[round(pred), round(1-pred)], 
-                     y=['toxic', 'non-toxic'],
-                     width=500, height=250, 
-                     template="plotly_dark", 
-                     text_auto='1', 
-                     title="Probabilities(%)")
-        fig.update_traces(width=0.3,textfont_size=15, textangle=0, textposition="outside")
-        fig.update_layout(yaxis_title=None,xaxis_title=None)
-        return fig
-
-    def predict(self, text:str):
-        try:
-            text = re.sub('\n',' ',text).strip()
-            input = self.tokenizer(text)
-            pred = self.model.predict(input)[0][0]
-            fig = self.plot(pred)
-            return pred, fig
-        except Exception as e:
-            print(e)