Updated the code

app.py

@@ -1,38 +1,74 @@
 import streamlit as st
 import pandas as pd
 import numpy as np
-# from scipy.special import softmax
-# import os
+from PIL import Image
 from utils import run_sentiment_analysis, preprocess
 from transformers import AutoTokenizer, AutoConfig,AutoModelForSequenceClassification
 import os
 import time
 
-# Requirements
-model_path = "bright1/fine-tuned-distilbert-base-uncased"
-tokenizer = AutoTokenizer.from_pretrained(model_path)
-config = AutoConfig.from_pretrained(model_path)
-model = AutoModelForSequenceClassification.from_pretrained(model_path)
-
-# dark_theme = set_theme()
+# the two model trained
+dstbt_model_path = "bright1/fine-tuned-distilbert-base-uncased" # distilbert model
+rbta_model_path = "bright1/fine-tuned-twitter-Roberta-base-sentiment" # roberta model
 
+# function to load model 
+def load_model_components(model_path):
+    tokenizer = AutoTokenizer.from_pretrained(model_path)
+    config = AutoConfig.from_pretrained(model_path)
+    model = AutoModelForSequenceClassification.from_pretrained(model_path)
+    return model, tokenizer, config
 
+# configure page
 st.set_page_config(
     page_title="Tweet Analyzer",
     page_icon="🤖",
     initial_sidebar_state="expanded",
     menu_items={
-        'About': "# This is a header. This is an *extremely* cool app!"
+        'About': "# This is a Sentiment Analysis App. Call it the Covid Vaccine tweet Analyzer!"
     }
 )  
 
+# Define custom CSS style
 
-my_expander  = st.container()
+# Apply custom CSS
+# st.markdown("""<style> 
+#         [data-testid="stAppViewContainer"] {
+#             background-image: url("app\download.png");
+#              background-attachment: fixed;
+#             background-size: cover
+#             }
+# </style>""", unsafe_allow_html=True)
+
+
+
+# create a sidebar and contents
+st.sidebar.markdown("""
+## Demo App
+
+This app analyzes your tweets on covid vaccines and classifies them us Neutral, Negative or Positive
+""")
+
+# create a three column layout
+model_type = st.sidebar.selectbox(label=':red[Select your model]', options=('distilbert', 'roberta'))
+st.markdown("""<style> 
+        [data-testid="stMarkdownContainer"] {
+            font-size: 30px;
+            font-weight: 800;
+            }
+</style>""", unsafe_allow_html=True)
 
+# set a default model path
+model_path = dstbt_model_path
+if model_type == 'roberta':
+    model_path = rbta_model_path
+
+
+# create app interface 
+my_expander  = st.container()
 
 # st.sidebar.selectbox('Menu', ['About', 'Model'])
 with my_expander:
-    
+    # center text in the container
     st.markdown("""
         <style>
         h1 {
@@ -40,15 +76,16 @@ with my_expander:
         }
         </style>
         """, unsafe_allow_html=True)
+    
+    #set title for the app
     st.title(':green[Covid-19 Vaccines Tweets Analyzer]')
-    st.sidebar.markdown("""
-    ## Demo App
 
-    This app analyzes your tweets on covid vaccines and classifies them us Neutral, Negative or Positive
-    """)        
-    # my_expander.write('Container')
-    # create a three column layout
 
+    # load model components
+    model, tokenizer, config = load_model_components(model_path)
+
+
+    # size columns
     col1, col2, col3 = st.columns((1.6, 1,0.3))
     # col2.markdown("""
     #         <p style= font-color:red>
@@ -62,24 +99,41 @@ with my_expander:
         }
         </style>
         """, unsafe_allow_html=True)
+    
+    # set textarea to receive tweet
     tweet = col1.text_area('Tweets to analyze',height=200, max_chars=520, placeholder='Write your Tweets here')
+
+    # divide container into columns
     colA, colb, colc, cold = st.columns(4)
     clear_button = colA.button(label='Clear', type='secondary', use_container_width=True)
+
+    # create a submit button
     submit_button = colb.button(label='Submit', type='primary', use_container_width=True)
-    empty_container = col2.container()
+
+    # set an empty container for the results
+    empty_container = col2.container() # for progress bars
     empty_container.text("Results from Analyzer")
-    empty_container2 = col3.container()
+
+    empty_container2 = col3.container() # for scores
     empty_container2.text('Scores')
     text = preprocess(tweet)
+
+    # run the analysis on the tweet
     results = run_sentiment_analysis(text=text, model=model, tokenizer=tokenizer)
+    
+    # when the tweet is submitted
     if submit_button:
+        # print a success message 
         success_message = st.success('Success', icon="✅")
+        time.sleep(3)
+        success_message.empty()
         
+        # create am expander to contain the results
         with empty_container:
-            
             neutral = st.progress(value=results['Neutral'], text='Neutral',)
             negative = st.progress(value=results['Negative'], text='Negative')
             positive = st.progress(value=results['Positive'], text='Positive')
+
         with empty_container2:
             st.markdown(
                 """
@@ -87,18 +141,28 @@ with my_expander:
             [data-testid="stMetricValue"] {
                 font-size: 20px;
             }
+            .st-ed {
+                background-color: #FF4B4B;  
+            
+            }
+            .st-ee {
+                background-color: #1B9C85;
+            }
+            .st-eb {
+                background-color: #FFD95A;
+            }
             </style>
             """,
                 unsafe_allow_html=True,
             )
+
+            # class=""
+            # dispay the scores with metric widget 
             neutral_score = st.metric(label='Score', value=round(results['Neutral'], 4), label_visibility='collapsed')
             negative_score = st.metric(label='Score', value=round(results['Negative'], 4), label_visibility='collapsed')
             positive_score = st.metric(label='Score', value=round(results['Positive'], 4), label_visibility='collapsed')
-            time.sleep(5)
-            success_message.empty()
-    # interpret_button = col2.button(label='Interpret',type='secondary', use_container_width=True)
+            
+            # interpret_button = col2.button(label='Interpret',type='secondary', use_container_width=True)
 
 
-# st.help()
-    # create a date input to receive date
 

utils.py

@@ -4,12 +4,6 @@ from transformers import AutoTokenizer, AutoConfig,AutoModelForSequenceClassific
 from scipy.special import softmax
 import os
 
-# Requirements
-# model_path = "bright1/fine-tuned-distilbert-base-uncased"
-# tokenizer = AutoTokenizer.from_pretrained(model_path)
-# config = AutoConfig.from_pretrained(model_path)
-# model = AutoModelForSequenceClassification.from_pretrained(model_path)
-
 
 
 def check_csv(csv_file, data):
@@ -27,7 +21,6 @@ def preprocess(text):
         t = "http" if t.startswith("http") else t
         print(t)
         new_text.append(t)
-    print(new_text)
 
     return " ".join(new_text)