Update app.py

app.py

@@ -1,541 +1,54 @@
 import streamlit as st
-import pandas as pd
-import numpy as np
 
-# Set the background color of the dashboard
-st.set_page_config(layout="wide")
-st.markdown(
-    """
-    # Innomatics Online Trainer Bot
-    Welcome to Innomatics Online Trainer Bot. This platform is designed to provide you with interactive learning experiences in various fields.
-    """
-)
-
-# Introduction
-st.write("")
-
-# Question
-st.write("In which module do you have doubt?")
-
-# Create a multi-column layout for the buttons
-with st.expander("Select a module"):
-    columns = st.columns(6)
-    for i, col in enumerate(columns):
-        if i < 3:
-            col.button("Python", key="python")
-        elif i < 6:
-            col.button("Machine Learning", key="machine_learning")
-        else:
-            col.button("Deep Learning", key="deep_learning")
-        if i == 0:
-            col.button("Statistics", key="statistics")
-        elif i == 1:
-            col.button("Excel", key="excel")
-        else:
-            col.button("SQL", key="sql")
-
-# Redirect to the corresponding page when a button is clicked
-if st.session_state.button_clicked:
-    if st.session_state.button_clicked == "python":
-        st.session_state.redirect_to = "python"
-    elif st.session_state.button_clicked == "machine_learning":
-        st.session_state.redirect_to = "machine_learning"
-    elif st.session_state.button_clicked == "deep_learning":
-        st.session_state.redirect_to = "deep_learning"
-    elif st.session_state.button_clicked == "statistics":
-        st.session_state.redirect_to = "statistics"
-    elif st.session_state.button_clicked == "excel":
-        st.session_state.redirect_to = "excel"
-    elif st.session_state.button_clicked == "sql":
-        st.session_state.redirect_to = "sql"
-
-# Redirect to the corresponding page
-if "redirect_to" in st.session_state:
-    if st.session_state.redirect_to == "python":
-        import python
-        python.main()
-    elif st.session_state.redirect_to == "machine_learning":
-        import machine_learning
-        machine_learning.main()
-    elif st.session_state.redirect_to == "deep_learning":
-        import deep_learning
-        deep_learning.main()
-    elif st.session_state.redirect_to == "statistics":
-        import statistics
-        statistics.main()
-    elif st.session_state.redirect_to == "excel":
-        import excel
-        excel.main()
-    elif st.session_state.redirect_to == "sql":
-        import sql
-        sql.main()
-
-# Define the main functions for each module
-def python():
-    st.write("Python Module")
-
-def machine_learning():
-    st.write("Machine Learning Module")
-
-def deep_learning():
-    st.write("Deep Learning Module")
-
-def statistics():
-    st.write("Statistics Module")
-
-def excel():
-    st.write("Excel Module")
-
-def sql():
-    st.write("SQL Module")
-
-# Run the main function
-python()
-```
-
-However, the above code is not ideal because it's not using the Hugging Face library. Here's a revised version of the code that uses the Hugging Face library:
-
-```python
-import streamlit as st
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
-import pandas as pd
-import numpy as np
-
-# Set the background color of the dashboard
-st.set_page_config(layout="wide")
-st.markdown(
-    """
-    # Innomatics Online Trainer Bot
-    Welcome to Innomatics Online Trainer Bot. This platform is designed to provide you with interactive learning experiences in various fields.
-    """
-)
-
-# Introduction
-st.write("")
-
-# Question
-st.write("In which module do you have doubt?")
-
-# Create a multi-column layout for the buttons
-with st.expander("Select a module"):
-    columns = st.columns(6)
-    for i, col in enumerate(columns):
-        if i < 3:
-            col.button("Python", key="python")
-        elif i < 6:
-            col.button("Machine Learning", key="machine_learning")
-        else:
-            col.button("Deep Learning", key="deep_learning")
-        if i == 0:
-            col.button("Statistics", key="statistics")
-        elif i == 1:
-            col.button("Excel", key="excel")
-        else:
-            col.button("SQL", key="sql")
-
-# Redirect to the corresponding page when a button is clicked
-if st.session_state.button_clicked:
-    if st.session_state.button_clicked == "python":
-        st.session_state.redirect_to = "python"
-    elif st.session_state.button_clicked == "machine_learning":
-        st.session_state.redirect_to = "machine_learning"
-    elif st.session_state.button_clicked == "deep_learning":
-        st.session_state.redirect_to = "deep_learning"
-    elif st.session_state.button_clicked == "statistics":
-        st.session_state.redirect_to = "statistics"
-    elif st.session_state.button_clicked == "excel":
-        st.session_state.redirect_to = "excel"
-    elif st.session_state.button_clicked == "sql":
-        st.session_state.redirect_to = "sql"
-
-# Redirect to the corresponding page
-if "redirect_to" in st.session_state:
-    if st.session_state.redirect_to == "python":
-        python()
-    elif st.session_state.redirect_to == "machine_learning":
-        machine_learning()
-    elif st.session_state.redirect_to == "deep_learning":
-        deep_learning()
-    elif st.session_state.redirect_to == "statistics":
-        statistics()
-    elif st.session_state.redirect_to == "excel":
-        excel()
-    elif st.session_state.redirect_to == "sql":
-        sql()
-
-# Define the main functions for each module
-def python():
-    st.write("Python Module")
-
-def machine_learning():
-    st.write("Machine Learning Module")
-
-def deep_learning():
-    st.write("Deep Learning Module")
-
-def statistics():
-    st.write("Statistics Module")
-
-def excel():
-    st.write("Excel Module")
-
-def sql():
-    st.write("SQL Module")
-
-# Run the main function
-python()
-```
-
-However, the above code is still not ideal because it's not using the Hugging Face library to load the models. Here's a revised version of the code that uses the Hugging Face library to load the models:
-
-```python
-import streamlit as st
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
-import pandas as pd
-import numpy as np
-
-# Set the background color of the dashboard
-st.set_page_config(layout="wide")
-st.markdown(
-    """
-    # Innomatics Online Trainer Bot
-    Welcome to Innomatics Online Trainer Bot. This platform is designed to provide you with interactive learning experiences in various fields.
-    """
-)
-
-# Introduction
-st.write("")
-
-# Question
-st.write("In which module do you have doubt?")
-
-# Create a multi-column layout for the buttons
-with st.expander("Select a module"):
-    columns = st.columns(6)
-    for i, col in enumerate(columns):
-        if i < 3:
-            col.button("Python", key="python")
-        elif i < 6:
-            col.button("Machine Learning", key="machine_learning")
-        else:
-            col.button("Deep Learning", key="deep_learning")
-        if i == 0:
-            col.button("Statistics", key="statistics")
-        elif i == 1:
-            col.button("Excel", key="excel")
-        else:
-            col.button("SQL", key="sql")
-
-# Redirect to the corresponding page when a button is clicked
-if st.session_state.button_clicked:
-    if st.session_state.button_clicked == "python":
-        st.session_state.redirect_to = "python"
-    elif st.session_state.button_clicked == "machine_learning":
-        st.session_state.redirect_to = "machine_learning"
-    elif st.session_state.button_clicked == "deep_learning":
-        st.session_state.redirect_to = "deep_learning"
-    elif st.session_state.button_clicked == "statistics":
-        st.session_state.redirect_to = "statistics"
-    elif st.session_state.button_clicked == "excel":
-        st.session_state.redirect_to = "excel"
-    elif st.session_state.button_clicked == "sql":
-        st.session_state.redirect_to = "sql"
-
-# Redirect to the corresponding page
-if "redirect_to" in st.session_state:
-    if st.session_state.redirect_to == "python":
-        python()
-    elif st.session_state.redirect_to == "machine_learning":
-        machine_learning()
-    elif st.session_state.redirect_to == "deep_learning":
-        deep_learning()
-    elif st.session_state.redirect_to == "statistics":
-        statistics()
-    elif st.session_state.redirect_to == "excel":
-        excel()
-    elif st.session_state.redirect_to == "sql":
-        sql()
-
-# Load the models
-python_model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased')
-machine_learning_model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased')
-deep_learning_model = AutoModelForSequenceClassification.from_pretrained('roberta-base')
-statistics_model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased')
-excel_model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased')
-sql_model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased')
-
-# Define the main functions for each module
-def python():
-    st.write("Python Module")
-
-def machine_learning():
-    st.write("Machine Learning Module")
+# Set page config
+st.set_page_config(page_title="Innomatics Online Trainer Bot", layout="centered")
 
-def deep_learning():
-    st.write("Deep Learning Module")
-
-def statistics():
-    st.write("Statistics Module")
-
-def excel():
-    st.write("Excel Module")
-
-def sql():
-    st.write("SQL Module")
-
-# Run the main function
-python()
-```
-
-However, the above code is still not ideal because it's not using the Hugging Face library to load the models in a more efficient way. Here's a revised version of the code that uses the Hugging Face library to load the models in a more efficient way:
-
-```python
-import streamlit as st
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
-import pandas as pd
-import numpy as np
-
-# Set the background color of the dashboard
-st.set_page_config(layout="wide")
+# Background color using HTML/CSS
 st.markdown(
     """
-    # Innomatics Online Trainer Bot
-    Welcome to Innomatics Online Trainer Bot. This platform is designed to provide you with interactive learning experiences in various fields.
-    """
+    <style>
+    .main {
+        background-color: #8B0000; /* Brownish red */
+    }
+    h1, h3, p {
+        color: white;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
 )
 
-# Introduction
-st.write("")
-
-# Question
-st.write("In which module do you have doubt?")
-
-# Create a multi-column layout for the buttons
-with st.expander("Select a module"):
-    columns = st.columns(6)
-    for i, col in enumerate(columns):
-        if i < 3:
-            col.button("Python", key="python")
-        elif i < 6:
-            col.button("Machine Learning", key="machine_learning")
-        else:
-            col.button("Deep Learning", key="deep_learning")
-        if i == 0:
-            col.button("Statistics", key="statistics")
-        elif i == 1:
-            col.button("Excel", key="excel")
-        else:
-            col.button("SQL", key="sql")
-
-# Redirect to the corresponding page when a button is clicked
-if st.session_state.button_clicked:
-    if st.session_state.button_clicked == "python":
-        st.session_state.redirect_to = "python"
-    elif st.session_state.button_clicked == "machine_learning":
-        st.session_state.redirect_to = "machine_learning"
-    elif st.session_state.button_clicked == "deep_learning":
-        st.session_state.redirect_to = "deep_learning"
-    elif st.session_state.button_clicked == "statistics":
-        st.session_state.redirect_to = "statistics"
-    elif st.session_state.button_clicked == "excel":
-        st.session_state.redirect_to = "excel"
-    elif st.session_state.button_clicked == "sql":
-        st.session_state.redirect_to = "sql"
-
-# Redirect to the corresponding page
-if "redirect_to" in st.session_state:
-    if st.session_state.redirect_to == "python":
-        python()
-    elif st.session_state.redirect_to == "machine_learning":
-        machine_learning()
-    elif st.session_state.redirect_to == "deep_learning":
-        deep_learning()
-    elif st.session_state.redirect_to == "statistics":
-        statistics()
-    elif st.session_state.redirect_to == "excel":
-        excel()
-    elif st.session_state.redirect_to == "sql":
-        sql()
-
-# Load the models
-python_model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased')
-machine_learning_model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased')
-deep_learning_model = AutoModelForSequenceClassification.from_pretrained('roberta-base')
-statistics_model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased')
-excel_model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased')
-sql_model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased')
-
-# Define the main functions for each module
-def python():
-    st.write("Python Module")
-
-def machine_learning():
-    st.write("Machine Learning Module")
-
-def deep_learning():
-    st.write("Deep Learning Module")
-
-def statistics():
-    st.write("Statistics Module")
-
-def excel():
-    st.write("Excel Module")
-
-def sql():
-    st.write("SQL Module")
-
-# Run the main function
-python()
-```
-
-However, the above code is still not ideal because it's not using the Hugging Face library to load the models in a more efficient way. Here's a revised version of the code that uses the Hugging Face library to load the models in a more efficient way:
-
-```python
-import streamlit as st
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
-import pandas as pd
-import numpy as np
-
-# Set the background color of the dashboard
-st.set_page_config(layout="wide")
-st.markdown(
-    """
-    # Innomatics Online Trainer Bot
-    Welcome to Innomatics Online Trainer Bot. This platform is designed to provide you with interactive learning experiences in various fields.
-    """
-)
+# Title
+st.title("Innomatics Online Trainer Bot")
 
 # Introduction
-st.write("")
-
-# Question
-st.write("In which module do you have doubt?")
-
-# Create a multi-column layout for the buttons
-with st.expander("Select a module"):
-    columns = st.columns(6)
-    for i, col in enumerate(columns):
-        if i < 3:
-            col.button("Python", key="python")
-        elif i < 6:
-            col.button("Machine Learning", key="machine_learning")
-        else:
-            col.button("Deep Learning", key="deep_learning")
-        if i == 0:
-            col.button("Statistics", key="statistics")
-        elif i == 1:
-            col.button("Excel", key="excel")
-        else:
-            col.button("SQL", key="sql")
-
-# Redirect to the corresponding page when a button is clicked
-if st.session_state.button_clicked:
-    if st.session_state.button_clicked == "python":
-        st.session_state.redirect_to = "python"
-    elif st.session_state.button_clicked == "machine_learning":
-        st.session_state.redirect_to = "machine_learning"
-    elif st.session_state.button_clicked == "deep_learning":
-        st.session_state.redirect_to = "deep_learning"
-    elif st.session_state.button_clicked == "statistics":
-        st.session_state.redirect_to = "statistics"
-    elif st.session_state.button_clicked == "excel":
-        st.session_state.redirect_to = "excel"
-    elif st.session_state.button_clicked == "sql":
-        st.session_state.redirect_to = "sql"
-
-# Redirect to the corresponding page
-if "redirect_to" in st.session_state:
-    if st.session_state.redirect_to == "python":
-        python()
-    elif st.session_state.redirect_to == "machine_learning":
-        machine_learning()
-    elif st.session_state.redirect_to == "deep_learning":
-        deep_learning()
-    elif st.session_state.redirect_to == "statistics":
-        statistics()
-    elif st.session_state.redirect_to == "excel":
-        excel()
-    elif st.session_state.redirect_to == "sql":
-        sql()
-
-# Load the models
-python_model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased')
-machine_learning_model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased')
-deep_learning_model = AutoModelForSequenceClassification.from_pretrained('roberta-base')
-statistics_model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased')
-excel_model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased')
-sql_model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased')
-
-# Define the main functions for each module
-def python():
-    st.write("Python Module")
-
-def machine_learning():
-    st.write("Machine Learning Module")
-
-def deep_learning():
-    st.write("Deep Learning Module")
-
-def statistics():
-    st.write("Statistics Module")
-
-def excel():
-    st.write("Excel Module")
-
-def sql():
-    st.write("SQL Module")
-
-# Run the main function
-python()
-```
-
-However, the above code is still not ideal because it's not using the Hugging Face library to load the models in a more efficient way. Here's a revised version of the code that uses the Hugging Face library to load the models in a more efficient way:
-
-```python
-import streamlit as st
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
-import pandas as pd
-import numpy as np
-
-# Set the background color of the dashboard
-st.set_page_config(layout="wide")
-st.markdown(
-    """
-    # Innomatics Online Trainer Bot
-    Welcome to Innomatics Online Trainer Bot. This platform is designed to provide you with interactive learning experiences in various fields.
-    """
-)
-
-# Introduction
-st.write("")
-
-# Question
-st.write("In which module do you have doubt?")
-
-# Create a multi-column layout for the buttons
-with st.expander("Select a module"):
-    columns = st.columns(6)
-    for i, col in enumerate(columns):
-        if i < 3:
-            col.button("Python", key="python")
-        elif i < 6:
-            col.button("Machine Learning", key="machine_learning")
-        else:
-            col.button("Deep Learning", key="deep_learning")
-        if i == 0:
-            col.button("Statistics", key="statistics")
-        elif i == 1:
-            col.button("Excel", key="excel")
-        else:
-            col.button("SQL", key="sql")
-
-# Redirect to the corresponding page when a button is clicked
-if st.session_state.button_clicked:
-    if st.session_state.button_clicked == "python":
-        st.session_state.redirect_to = "python"
-    elif st.session_state.button_clicked == "machine_learning":
-        st.session_state.redirect_to = "machine_learning"
-    elif st.session_state.button_clicked == "deep_learning":
-        st.session_state.redirect_to = "deep_learning"
-    elif st.session_state.button_clicked == "statistics":
-        st.session_state.redirect_to = "deep_learning"
+st.markdown("### 👋 Welcome to the Innomatics Online Trainer Bot!")
+st.markdown("This dashboard will guide you through your doubts in various modules.")
+
+# Prompt
+st.markdown("## In which module do you have doubt?")
+
+# Create buttons in a 2-column layout
+col1, col2 = st.columns(2)
+with col1:
+    if st.button("Python"):
+        st.switch_page("python.py")
+with col2:
+    if st.button("Machine Learning"):
+        st.switch_page("machine_learning.py")
+
+col3, col4 = st.columns(2)
+with col3:
+    if st.button("Deep Learning"):
+        st.switch_page("deep_learning.py")
+with col4:
+    if st.button("Statistics"):
+        st.switch_page("statistics.py")
+
+col5, col6 = st.columns(2)
+with col5:
+    if st.button("Excel"):
+        st.switch_page("excel.py")
+with col6:
+    if st.button("SQL"):
+        st.switch_page("sql.py")