Create app.py

app.py

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+import streamlit as st 
+import altair as alt
+import plotly.express as px 
+import pandas as pd 
+import numpy as np 
+from datetime import datetime
+from transformers import pipeline
+
+# Loading pre-trained emotion classifier pipeline
+emotion_classifier = pipeline("text-classification", model="j-hartmann/emotion-english-roberta-large", top_k=None)
+
+from track_utils import create_page_visited_table, add_page_visited_details, view_all_page_visited_details, add_prediction_details, view_all_prediction_details, create_emotionclf_table
+
+def predict_emotions(docx):
+    results = emotion_classifier(docx)
+    results_sorted = sorted(results[0], key=lambda x: x['score'], reverse=True)
+    return results_sorted[0]['label']
+
+def get_prediction_proba(docx):
+    results = emotion_classifier(docx)
+    return {result['label']: result['score'] for result in results[0]}
+
+
+def set_bg_hack_url():
+    '''
+    A function to unpack an image from url and set as bg.
+    Returns
+    -------
+    The background.
+    '''
+        
+    st.markdown(
+         f"""
+         <style>
+         .stApp {{
+             background: url("https://png.pngtree.com/background/20210709/original/pngtree-simple-technology-business-line-picture-image_938206.jpg");
+             background-size: cover;
+         }}
+         /* General body styling */
+         body {{
+             font-family: 'Arial', sans-serif;
+         }}
+         /* Sidebar styling */
+         [data-testid="stSidebar"] {{
+             background: linear-gradient(180deg, #52079A, #062879); /* Gradient from dark blue to orange */
+             color: white;
+         }}
+         [data-testid="stSidebar"] .css-1d391kg {{
+             color: white;
+         }}
+         /* Title and headers */
+         h1, h2, h3 {{
+             color: #FFFFFF; /* White */
+         }}
+         /* Custom button style */
+         .stButton button {{
+             background-color: #004080; /* Dark Blue */
+             color: white;
+             border-radius: 8px;
+             border: none;
+             font-size: 16px;
+             padding: 10px 20px;
+             cursor: pointer;
+         }}
+         .stButton button:hover {{
+             background-color: #FFA500; /* Orange */
+         }}
+         /* DataFrame styling */
+         .css-17z80pu {{
+             background-color: #d3d3d3; /* Grey */
+             border: 1px solid #ddd;
+             border-radius: 4px;
+             padding: 10px;
+         }}
+         /* Custom chart area */
+         .stAltairChart {{
+             background-color: #d3d3d3; /* Grey */
+             border: 1px solid #ddd;
+             border-radius: 5px;
+             padding: 10px;
+         }}
+         /* Text area styling */
+         .css-91z34k {{
+             background-color: #e0e0e0; /* Light Grey for Text Area Box */
+             border: 1px solid #ddd;
+             border-radius: 4px;
+             padding: 10px;
+         }}
+         /* Top bar styling */
+         header[data-testid="stHeader"] {{
+             background: rgba(0, 0, 0, 0); /* Transparent */
+         }}
+         </style>
+         """,
+         unsafe_allow_html=True
+     )
+
+
+
+emotions_emoji_dict = {"anger":"😠","disgust":"🤮", "fear":"😨😱", "happiness":"🤗", "joy":"😂", "neutral":"😐", "sadness":"😔", "surprise":"😮"}
+
+def main():
+    st.set_page_config(page_title="Emotion Classifier App: Veer", layout="wide")
+    set_bg_hack_url()
+
+    st.sidebar.title("Menu")
+    menu = ["🏠 Home", "📊 Monitor", "ℹ️ About"]
+    choice = st.sidebar.selectbox("Select an Option", menu)
+    
+
+    create_page_visited_table()
+    create_emotionclf_table()
+    
+    if choice == "🏠 Home":
+        add_page_visited_details("Home", datetime.now())
+        st.title("Emotion Classifier App")
+        st.subheader("Enter text to analyze its emotion")
+        
+        with st.form(key='emotion_clf_form'):
+            raw_text = st.text_area("Type Here")
+            submit_text = st.form_submit_button(label='Submit')
+
+        if submit_text:
+            prediction = predict_emotions(raw_text)
+            probability = get_prediction_proba(raw_text)
+            
+            add_prediction_details(raw_text, prediction, max(probability.values()), datetime.now())
+
+            col1, col2 = st.columns(2)
+
+            with col1:
+                st.success("Input Text")
+                st.write(raw_text)
+
+                st.success("Sentiment Prediction")
+                emoji_icon = emotions_emoji_dict[prediction]
+                st.write(f"{prediction}: {emoji_icon}")
+                st.write(f"Confidence: {max(probability.values()):.2f}")
+
+            with col2:
+                st.success("Prediction Probability")
+                proba_df = pd.DataFrame(list(probability.items()), columns=["emotions", "probability"])
+
+                fig = alt.Chart(proba_df).mark_bar().encode(x='emotions', y='probability', color='emotions')
+                st.altair_chart(fig, use_container_width=True)
+
+    elif choice == "📊 Monitor":
+        add_page_visited_details("Monitor", datetime.now())
+        st.title("App Monitoring")
+
+        with st.expander("Page Metrics"):
+            page_visited_details = pd.DataFrame(view_all_page_visited_details(), columns=['Pagename','Time_of_Visit'])
+            st.dataframe(page_visited_details)    
+
+            pg_count = page_visited_details['Pagename'].value_counts().rename_axis('Pagename').reset_index(name='Counts')
+            c = alt.Chart(pg_count).mark_bar().encode(x='Pagename', y='Counts', color='Pagename')
+            st.altair_chart(c, use_container_width=True)    
+
+            p = px.pie(pg_count, values='Counts', names='Pagename')
+            st.plotly_chart(p, use_container_width=True)
+
+        with st.expander('Emotion Classifier Metrics'):  #initially showed Unicode decode error: utf-8 codec cant decode byte; fix:
+            try:
+                prediction_details = view_all_prediction_details()
+                df_emotions = pd.DataFrame(prediction_details, columns=['Rawtext','Prediction','Probability','Time_of_Visit'])
+
+                # fix for unicodedecodeerror: Ensuring all columns are converted to strings to avoid decoding errors.
+                df_emotions = df_emotions.applymap(lambda x: x.decode('utf-8', 'ignore') if isinstance(x, bytes) else str(x))
+                st.dataframe(df_emotions)
+
+                prediction_count = df_emotions['Prediction'].value_counts().rename_axis('Prediction').reset_index(name='Counts')
+                pc = alt.Chart(prediction_count).mark_bar().encode(x='Prediction', y='Counts', color='Prediction')
+                st.altair_chart(pc, use_container_width=True)
+            except UnicodeDecodeError as e:
+                st.error(f"Error decoding data: {e}")
+            
+    else:
+        st.title("About")
+        add_page_visited_details("About", datetime.now())
+        st.subheader("Emotion Classifier App")
+        st.text("A simple application to classify emotions from text.")
+
+if __name__ == '__main__':
+    main()