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Update app.py
Browse files
app.py
CHANGED
@@ -1 +1,540 @@
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import streamlit as st
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1 |
+
import streamlit as st
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2 |
+
import pandas as pd
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3 |
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import numpy as np
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4 |
+
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5 |
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# Set the background color of the dashboard
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6 |
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st.set_page_config(layout="wide")
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7 |
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st.markdown(
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+
"""
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9 |
+
# Innomatics Online Trainer Bot
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10 |
+
Welcome to Innomatics Online Trainer Bot. This platform is designed to provide you with interactive learning experiences in various fields.
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11 |
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"""
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+
)
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+
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# Introduction
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st.write("")
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# Question
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st.write("In which module do you have doubt?")
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# Create a multi-column layout for the buttons
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with st.expander("Select a module"):
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columns = st.columns(6)
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for i, col in enumerate(columns):
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if i < 3:
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col.button("Python", key="python")
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elif i < 6:
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col.button("Machine Learning", key="machine_learning")
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else:
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col.button("Deep Learning", key="deep_learning")
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if i == 0:
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col.button("Statistics", key="statistics")
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elif i == 1:
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col.button("Excel", key="excel")
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else:
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col.button("SQL", key="sql")
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+
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# Redirect to the corresponding page when a button is clicked
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if st.session_state.button_clicked:
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if st.session_state.button_clicked == "python":
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st.session_state.redirect_to = "python"
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elif st.session_state.button_clicked == "machine_learning":
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st.session_state.redirect_to = "machine_learning"
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elif st.session_state.button_clicked == "deep_learning":
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st.session_state.redirect_to = "deep_learning"
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elif st.session_state.button_clicked == "statistics":
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st.session_state.redirect_to = "statistics"
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elif st.session_state.button_clicked == "excel":
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st.session_state.redirect_to = "excel"
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49 |
+
elif st.session_state.button_clicked == "sql":
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st.session_state.redirect_to = "sql"
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51 |
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# Redirect to the corresponding page
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53 |
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if "redirect_to" in st.session_state:
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54 |
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if st.session_state.redirect_to == "python":
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import python
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python.main()
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elif st.session_state.redirect_to == "machine_learning":
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import machine_learning
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machine_learning.main()
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elif st.session_state.redirect_to == "deep_learning":
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import deep_learning
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deep_learning.main()
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63 |
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elif st.session_state.redirect_to == "statistics":
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64 |
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import statistics
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statistics.main()
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elif st.session_state.redirect_to == "excel":
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67 |
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import excel
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excel.main()
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elif st.session_state.redirect_to == "sql":
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import sql
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sql.main()
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72 |
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73 |
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# Define the main functions for each module
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74 |
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def python():
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st.write("Python Module")
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76 |
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77 |
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def machine_learning():
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st.write("Machine Learning Module")
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def deep_learning():
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st.write("Deep Learning Module")
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def statistics():
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st.write("Statistics Module")
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def excel():
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st.write("Excel Module")
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+
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89 |
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def sql():
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st.write("SQL Module")
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91 |
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# Run the main function
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93 |
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python()
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94 |
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```
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95 |
+
|
96 |
+
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:
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97 |
+
|
98 |
+
```python
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99 |
+
import streamlit as st
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100 |
+
from transformers import AutoModelForSequenceClassification, AutoTokenizer
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101 |
+
import pandas as pd
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102 |
+
import numpy as np
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103 |
+
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104 |
+
# Set the background color of the dashboard
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105 |
+
st.set_page_config(layout="wide")
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106 |
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st.markdown(
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107 |
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"""
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108 |
+
# Innomatics Online Trainer Bot
|
109 |
+
Welcome to Innomatics Online Trainer Bot. This platform is designed to provide you with interactive learning experiences in various fields.
|
110 |
+
"""
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111 |
+
)
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112 |
+
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113 |
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# Introduction
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114 |
+
st.write("")
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115 |
+
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116 |
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# Question
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117 |
+
st.write("In which module do you have doubt?")
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118 |
+
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119 |
+
# Create a multi-column layout for the buttons
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120 |
+
with st.expander("Select a module"):
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121 |
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columns = st.columns(6)
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122 |
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for i, col in enumerate(columns):
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123 |
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if i < 3:
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124 |
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col.button("Python", key="python")
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125 |
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elif i < 6:
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126 |
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col.button("Machine Learning", key="machine_learning")
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127 |
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else:
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128 |
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col.button("Deep Learning", key="deep_learning")
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129 |
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if i == 0:
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130 |
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col.button("Statistics", key="statistics")
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131 |
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elif i == 1:
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132 |
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col.button("Excel", key="excel")
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133 |
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else:
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134 |
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col.button("SQL", key="sql")
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135 |
+
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136 |
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# Redirect to the corresponding page when a button is clicked
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137 |
+
if st.session_state.button_clicked:
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138 |
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if st.session_state.button_clicked == "python":
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st.session_state.redirect_to = "python"
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140 |
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elif st.session_state.button_clicked == "machine_learning":
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st.session_state.redirect_to = "machine_learning"
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elif st.session_state.button_clicked == "deep_learning":
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st.session_state.redirect_to = "deep_learning"
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144 |
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elif st.session_state.button_clicked == "statistics":
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145 |
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st.session_state.redirect_to = "statistics"
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146 |
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elif st.session_state.button_clicked == "excel":
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147 |
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st.session_state.redirect_to = "excel"
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148 |
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elif st.session_state.button_clicked == "sql":
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149 |
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st.session_state.redirect_to = "sql"
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150 |
+
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151 |
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# Redirect to the corresponding page
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152 |
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if "redirect_to" in st.session_state:
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153 |
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if st.session_state.redirect_to == "python":
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154 |
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python()
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155 |
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elif st.session_state.redirect_to == "machine_learning":
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156 |
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machine_learning()
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157 |
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elif st.session_state.redirect_to == "deep_learning":
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158 |
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deep_learning()
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159 |
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elif st.session_state.redirect_to == "statistics":
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160 |
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statistics()
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161 |
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elif st.session_state.redirect_to == "excel":
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162 |
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excel()
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163 |
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elif st.session_state.redirect_to == "sql":
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164 |
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sql()
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165 |
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166 |
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# Define the main functions for each module
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167 |
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def python():
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168 |
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st.write("Python Module")
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169 |
+
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170 |
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def machine_learning():
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171 |
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st.write("Machine Learning Module")
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172 |
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173 |
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def deep_learning():
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174 |
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st.write("Deep Learning Module")
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175 |
+
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176 |
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def statistics():
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177 |
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st.write("Statistics Module")
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178 |
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179 |
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def excel():
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180 |
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st.write("Excel Module")
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181 |
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182 |
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def sql():
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183 |
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st.write("SQL Module")
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184 |
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185 |
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# Run the main function
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186 |
+
python()
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187 |
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```
|
188 |
+
|
189 |
+
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:
|
190 |
+
|
191 |
+
```python
|
192 |
+
import streamlit as st
|
193 |
+
from transformers import AutoModelForSequenceClassification, AutoTokenizer
|
194 |
+
import pandas as pd
|
195 |
+
import numpy as np
|
196 |
+
|
197 |
+
# Set the background color of the dashboard
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198 |
+
st.set_page_config(layout="wide")
|
199 |
+
st.markdown(
|
200 |
+
"""
|
201 |
+
# Innomatics Online Trainer Bot
|
202 |
+
Welcome to Innomatics Online Trainer Bot. This platform is designed to provide you with interactive learning experiences in various fields.
|
203 |
+
"""
|
204 |
+
)
|
205 |
+
|
206 |
+
# Introduction
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207 |
+
st.write("")
|
208 |
+
|
209 |
+
# Question
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210 |
+
st.write("In which module do you have doubt?")
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211 |
+
|
212 |
+
# Create a multi-column layout for the buttons
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213 |
+
with st.expander("Select a module"):
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214 |
+
columns = st.columns(6)
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215 |
+
for i, col in enumerate(columns):
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216 |
+
if i < 3:
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217 |
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col.button("Python", key="python")
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218 |
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elif i < 6:
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219 |
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col.button("Machine Learning", key="machine_learning")
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220 |
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else:
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221 |
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col.button("Deep Learning", key="deep_learning")
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222 |
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if i == 0:
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223 |
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col.button("Statistics", key="statistics")
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224 |
+
elif i == 1:
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225 |
+
col.button("Excel", key="excel")
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226 |
+
else:
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227 |
+
col.button("SQL", key="sql")
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228 |
+
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229 |
+
# Redirect to the corresponding page when a button is clicked
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230 |
+
if st.session_state.button_clicked:
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231 |
+
if st.session_state.button_clicked == "python":
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232 |
+
st.session_state.redirect_to = "python"
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233 |
+
elif st.session_state.button_clicked == "machine_learning":
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234 |
+
st.session_state.redirect_to = "machine_learning"
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235 |
+
elif st.session_state.button_clicked == "deep_learning":
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236 |
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st.session_state.redirect_to = "deep_learning"
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237 |
+
elif st.session_state.button_clicked == "statistics":
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238 |
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st.session_state.redirect_to = "statistics"
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239 |
+
elif st.session_state.button_clicked == "excel":
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240 |
+
st.session_state.redirect_to = "excel"
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241 |
+
elif st.session_state.button_clicked == "sql":
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242 |
+
st.session_state.redirect_to = "sql"
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243 |
+
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244 |
+
# Redirect to the corresponding page
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245 |
+
if "redirect_to" in st.session_state:
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246 |
+
if st.session_state.redirect_to == "python":
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247 |
+
python()
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248 |
+
elif st.session_state.redirect_to == "machine_learning":
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249 |
+
machine_learning()
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250 |
+
elif st.session_state.redirect_to == "deep_learning":
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251 |
+
deep_learning()
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252 |
+
elif st.session_state.redirect_to == "statistics":
|
253 |
+
statistics()
|
254 |
+
elif st.session_state.redirect_to == "excel":
|
255 |
+
excel()
|
256 |
+
elif st.session_state.redirect_to == "sql":
|
257 |
+
sql()
|
258 |
+
|
259 |
+
# Load the models
|
260 |
+
python_model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased')
|
261 |
+
machine_learning_model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased')
|
262 |
+
deep_learning_model = AutoModelForSequenceClassification.from_pretrained('roberta-base')
|
263 |
+
statistics_model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased')
|
264 |
+
excel_model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased')
|
265 |
+
sql_model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased')
|
266 |
+
|
267 |
+
# Define the main functions for each module
|
268 |
+
def python():
|
269 |
+
st.write("Python Module")
|
270 |
+
|
271 |
+
def machine_learning():
|
272 |
+
st.write("Machine Learning Module")
|
273 |
+
|
274 |
+
def deep_learning():
|
275 |
+
st.write("Deep Learning Module")
|
276 |
+
|
277 |
+
def statistics():
|
278 |
+
st.write("Statistics Module")
|
279 |
+
|
280 |
+
def excel():
|
281 |
+
st.write("Excel Module")
|
282 |
+
|
283 |
+
def sql():
|
284 |
+
st.write("SQL Module")
|
285 |
+
|
286 |
+
# Run the main function
|
287 |
+
python()
|
288 |
+
```
|
289 |
+
|
290 |
+
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:
|
291 |
+
|
292 |
+
```python
|
293 |
+
import streamlit as st
|
294 |
+
from transformers import AutoModelForSequenceClassification, AutoTokenizer
|
295 |
+
import pandas as pd
|
296 |
+
import numpy as np
|
297 |
+
|
298 |
+
# Set the background color of the dashboard
|
299 |
+
st.set_page_config(layout="wide")
|
300 |
+
st.markdown(
|
301 |
+
"""
|
302 |
+
# Innomatics Online Trainer Bot
|
303 |
+
Welcome to Innomatics Online Trainer Bot. This platform is designed to provide you with interactive learning experiences in various fields.
|
304 |
+
"""
|
305 |
+
)
|
306 |
+
|
307 |
+
# Introduction
|
308 |
+
st.write("")
|
309 |
+
|
310 |
+
# Question
|
311 |
+
st.write("In which module do you have doubt?")
|
312 |
+
|
313 |
+
# Create a multi-column layout for the buttons
|
314 |
+
with st.expander("Select a module"):
|
315 |
+
columns = st.columns(6)
|
316 |
+
for i, col in enumerate(columns):
|
317 |
+
if i < 3:
|
318 |
+
col.button("Python", key="python")
|
319 |
+
elif i < 6:
|
320 |
+
col.button("Machine Learning", key="machine_learning")
|
321 |
+
else:
|
322 |
+
col.button("Deep Learning", key="deep_learning")
|
323 |
+
if i == 0:
|
324 |
+
col.button("Statistics", key="statistics")
|
325 |
+
elif i == 1:
|
326 |
+
col.button("Excel", key="excel")
|
327 |
+
else:
|
328 |
+
col.button("SQL", key="sql")
|
329 |
+
|
330 |
+
# Redirect to the corresponding page when a button is clicked
|
331 |
+
if st.session_state.button_clicked:
|
332 |
+
if st.session_state.button_clicked == "python":
|
333 |
+
st.session_state.redirect_to = "python"
|
334 |
+
elif st.session_state.button_clicked == "machine_learning":
|
335 |
+
st.session_state.redirect_to = "machine_learning"
|
336 |
+
elif st.session_state.button_clicked == "deep_learning":
|
337 |
+
st.session_state.redirect_to = "deep_learning"
|
338 |
+
elif st.session_state.button_clicked == "statistics":
|
339 |
+
st.session_state.redirect_to = "statistics"
|
340 |
+
elif st.session_state.button_clicked == "excel":
|
341 |
+
st.session_state.redirect_to = "excel"
|
342 |
+
elif st.session_state.button_clicked == "sql":
|
343 |
+
st.session_state.redirect_to = "sql"
|
344 |
+
|
345 |
+
# Redirect to the corresponding page
|
346 |
+
if "redirect_to" in st.session_state:
|
347 |
+
if st.session_state.redirect_to == "python":
|
348 |
+
python()
|
349 |
+
elif st.session_state.redirect_to == "machine_learning":
|
350 |
+
machine_learning()
|
351 |
+
elif st.session_state.redirect_to == "deep_learning":
|
352 |
+
deep_learning()
|
353 |
+
elif st.session_state.redirect_to == "statistics":
|
354 |
+
statistics()
|
355 |
+
elif st.session_state.redirect_to == "excel":
|
356 |
+
excel()
|
357 |
+
elif st.session_state.redirect_to == "sql":
|
358 |
+
sql()
|
359 |
+
|
360 |
+
# Load the models
|
361 |
+
python_model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased')
|
362 |
+
machine_learning_model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased')
|
363 |
+
deep_learning_model = AutoModelForSequenceClassification.from_pretrained('roberta-base')
|
364 |
+
statistics_model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased')
|
365 |
+
excel_model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased')
|
366 |
+
sql_model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased')
|
367 |
+
|
368 |
+
# Define the main functions for each module
|
369 |
+
def python():
|
370 |
+
st.write("Python Module")
|
371 |
+
|
372 |
+
def machine_learning():
|
373 |
+
st.write("Machine Learning Module")
|
374 |
+
|
375 |
+
def deep_learning():
|
376 |
+
st.write("Deep Learning Module")
|
377 |
+
|
378 |
+
def statistics():
|
379 |
+
st.write("Statistics Module")
|
380 |
+
|
381 |
+
def excel():
|
382 |
+
st.write("Excel Module")
|
383 |
+
|
384 |
+
def sql():
|
385 |
+
st.write("SQL Module")
|
386 |
+
|
387 |
+
# Run the main function
|
388 |
+
python()
|
389 |
+
```
|
390 |
+
|
391 |
+
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:
|
392 |
+
|
393 |
+
```python
|
394 |
+
import streamlit as st
|
395 |
+
from transformers import AutoModelForSequenceClassification, AutoTokenizer
|
396 |
+
import pandas as pd
|
397 |
+
import numpy as np
|
398 |
+
|
399 |
+
# Set the background color of the dashboard
|
400 |
+
st.set_page_config(layout="wide")
|
401 |
+
st.markdown(
|
402 |
+
"""
|
403 |
+
# Innomatics Online Trainer Bot
|
404 |
+
Welcome to Innomatics Online Trainer Bot. This platform is designed to provide you with interactive learning experiences in various fields.
|
405 |
+
"""
|
406 |
+
)
|
407 |
+
|
408 |
+
# Introduction
|
409 |
+
st.write("")
|
410 |
+
|
411 |
+
# Question
|
412 |
+
st.write("In which module do you have doubt?")
|
413 |
+
|
414 |
+
# Create a multi-column layout for the buttons
|
415 |
+
with st.expander("Select a module"):
|
416 |
+
columns = st.columns(6)
|
417 |
+
for i, col in enumerate(columns):
|
418 |
+
if i < 3:
|
419 |
+
col.button("Python", key="python")
|
420 |
+
elif i < 6:
|
421 |
+
col.button("Machine Learning", key="machine_learning")
|
422 |
+
else:
|
423 |
+
col.button("Deep Learning", key="deep_learning")
|
424 |
+
if i == 0:
|
425 |
+
col.button("Statistics", key="statistics")
|
426 |
+
elif i == 1:
|
427 |
+
col.button("Excel", key="excel")
|
428 |
+
else:
|
429 |
+
col.button("SQL", key="sql")
|
430 |
+
|
431 |
+
# Redirect to the corresponding page when a button is clicked
|
432 |
+
if st.session_state.button_clicked:
|
433 |
+
if st.session_state.button_clicked == "python":
|
434 |
+
st.session_state.redirect_to = "python"
|
435 |
+
elif st.session_state.button_clicked == "machine_learning":
|
436 |
+
st.session_state.redirect_to = "machine_learning"
|
437 |
+
elif st.session_state.button_clicked == "deep_learning":
|
438 |
+
st.session_state.redirect_to = "deep_learning"
|
439 |
+
elif st.session_state.button_clicked == "statistics":
|
440 |
+
st.session_state.redirect_to = "statistics"
|
441 |
+
elif st.session_state.button_clicked == "excel":
|
442 |
+
st.session_state.redirect_to = "excel"
|
443 |
+
elif st.session_state.button_clicked == "sql":
|
444 |
+
st.session_state.redirect_to = "sql"
|
445 |
+
|
446 |
+
# Redirect to the corresponding page
|
447 |
+
if "redirect_to" in st.session_state:
|
448 |
+
if st.session_state.redirect_to == "python":
|
449 |
+
python()
|
450 |
+
elif st.session_state.redirect_to == "machine_learning":
|
451 |
+
machine_learning()
|
452 |
+
elif st.session_state.redirect_to == "deep_learning":
|
453 |
+
deep_learning()
|
454 |
+
elif st.session_state.redirect_to == "statistics":
|
455 |
+
statistics()
|
456 |
+
elif st.session_state.redirect_to == "excel":
|
457 |
+
excel()
|
458 |
+
elif st.session_state.redirect_to == "sql":
|
459 |
+
sql()
|
460 |
+
|
461 |
+
# Load the models
|
462 |
+
python_model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased')
|
463 |
+
machine_learning_model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased')
|
464 |
+
deep_learning_model = AutoModelForSequenceClassification.from_pretrained('roberta-base')
|
465 |
+
statistics_model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased')
|
466 |
+
excel_model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased')
|
467 |
+
sql_model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased')
|
468 |
+
|
469 |
+
# Define the main functions for each module
|
470 |
+
def python():
|
471 |
+
st.write("Python Module")
|
472 |
+
|
473 |
+
def machine_learning():
|
474 |
+
st.write("Machine Learning Module")
|
475 |
+
|
476 |
+
def deep_learning():
|
477 |
+
st.write("Deep Learning Module")
|
478 |
+
|
479 |
+
def statistics():
|
480 |
+
st.write("Statistics Module")
|
481 |
+
|
482 |
+
def excel():
|
483 |
+
st.write("Excel Module")
|
484 |
+
|
485 |
+
def sql():
|
486 |
+
st.write("SQL Module")
|
487 |
+
|
488 |
+
# Run the main function
|
489 |
+
python()
|
490 |
+
```
|
491 |
+
|
492 |
+
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:
|
493 |
+
|
494 |
+
```python
|
495 |
+
import streamlit as st
|
496 |
+
from transformers import AutoModelForSequenceClassification, AutoTokenizer
|
497 |
+
import pandas as pd
|
498 |
+
import numpy as np
|
499 |
+
|
500 |
+
# Set the background color of the dashboard
|
501 |
+
st.set_page_config(layout="wide")
|
502 |
+
st.markdown(
|
503 |
+
"""
|
504 |
+
# Innomatics Online Trainer Bot
|
505 |
+
Welcome to Innomatics Online Trainer Bot. This platform is designed to provide you with interactive learning experiences in various fields.
|
506 |
+
"""
|
507 |
+
)
|
508 |
+
|
509 |
+
# Introduction
|
510 |
+
st.write("")
|
511 |
+
|
512 |
+
# Question
|
513 |
+
st.write("In which module do you have doubt?")
|
514 |
+
|
515 |
+
# Create a multi-column layout for the buttons
|
516 |
+
with st.expander("Select a module"):
|
517 |
+
columns = st.columns(6)
|
518 |
+
for i, col in enumerate(columns):
|
519 |
+
if i < 3:
|
520 |
+
col.button("Python", key="python")
|
521 |
+
elif i < 6:
|
522 |
+
col.button("Machine Learning", key="machine_learning")
|
523 |
+
else:
|
524 |
+
col.button("Deep Learning", key="deep_learning")
|
525 |
+
if i == 0:
|
526 |
+
col.button("Statistics", key="statistics")
|
527 |
+
elif i == 1:
|
528 |
+
col.button("Excel", key="excel")
|
529 |
+
else:
|
530 |
+
col.button("SQL", key="sql")
|
531 |
+
|
532 |
+
# Redirect to the corresponding page when a button is clicked
|
533 |
+
if st.session_state.button_clicked:
|
534 |
+
if st.session_state.button_clicked == "python":
|
535 |
+
st.session_state.redirect_to = "python"
|
536 |
+
elif st.session_state.button_clicked == "machine_learning":
|
537 |
+
st.session_state.redirect_to = "machine_learning"
|
538 |
+
elif st.session_state.button_clicked == "deep_learning":
|
539 |
+
st.session_state.redirect_to = "deep_learning"
|
540 |
+
elif st.session_state.button_clicked == "statistics
|