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import streamlit as st | |
import pandas as pd | |
import base64 | |
import json | |
from st_pages import Page, Section, show_pages, add_page_title,add_indentation | |
st.set_page_config( | |
page_title="app", | |
page_icon="π", | |
) | |
st.markdown("<h1 style='text-align: center; color: black;'> Welcome to DataAI! π</h1>", unsafe_allow_html=True) | |
def get_base64_of_bin_file(png_file): | |
with open(png_file, "rb") as f: | |
data = f.read() | |
return base64.b64encode(data).decode() | |
def build_markup_for_logo( | |
png_file, | |
background_position="0% 0%", | |
margin_top="5%", | |
image_width="60%", | |
image_height="", | |
): | |
binary_string = get_base64_of_bin_file(png_file) | |
return """ | |
<style> | |
[data-testid="stSidebarNav"] { | |
background-image: url("data:image/png;base64,%s"); | |
background-repeat: no-repeat; | |
background-position: %s; | |
margin-top: %s; | |
background-size: %s %s; | |
} | |
</style> | |
""" % ( | |
binary_string, | |
background_position, | |
margin_top, | |
image_width, | |
image_height, | |
) | |
def add_logo(png_file): | |
logo_markup = build_markup_for_logo(png_file) | |
st.markdown( | |
logo_markup, | |
unsafe_allow_html=True, | |
) | |
add_logo("logoo.png") | |
paragraph = """ | |
<span style="color:black;">As a data AI team, our capabilities are demonstrated through the functionalities showcased on our demo site. | |
The team has developed a diverse set of applications that leverage artificial intelligence and machine learning techniques. | |
The "Home" page serves as a central hub, providing access to various sections. | |
Within the "GenAI" section, we showcase advanced technologies such as an AI Chatbot, auto code generation, report generation, and score generation. | |
The "Deep Learning" section features demonstrations in image analytics, video analytics, and speech recognition. | |
In the "Machine Learning" section, we exhibit our proficiency in regression, forecasting, clustering, and optimization. | |
Our team's expertise lies in delivering innovative solutions that harness the power of data and AI, addressing a wide range of challenges and applications.</span> | |
""" | |
# Display the paragraph with black color within a Streamlit HTML element | |
st.markdown(paragraph, unsafe_allow_html=True) | |
st.markdown("<h3 style='text-align: center; color: black;'>What is GenAI?!!! </h2>", unsafe_allow_html=True) | |
g_p = """"<span style="color:black;">GenAI" stands for "General Artificial Intelligence," which refers to the concept of developing intelligent systems capable of performing tasks across a broad spectrum of domains, similar to the versatility and adaptability observed in human intelligence. In a short summary, "GenAI" embodies the pursuit of creating AI systems that can comprehend, learn, and apply knowledge across various contexts, aiming for a more generalized and flexible form of artificial intelligence.</span>""" | |
st.markdown(g_p, unsafe_allow_html=True) | |
st.markdown("<h3 style='text-align: center; color: black;'>What is Deep Learning?!!! </h2>", unsafe_allow_html=True) | |
d_l = """<span style="color:black;">Deep learning is a subfield of machine learning that involves training artificial neural networks with multiple layers (deep neural networks) to perform tasks such as image and speech recognition, natural language processing, and other complex pattern recognition tasks. The depth of the network allows it to automatically learn hierarchical representations of data, extracting features at different levels of abstraction. Deep learning has shown remarkable success in various applications, especially when dealing with large amounts of data, enabling the development of sophisticated models for complex tasks.</span>""" | |
st.markdown(d_l, unsafe_allow_html=True) | |
st.markdown("<h3 style='text-align: center; color: black;'>What is Machine Learning?!!! </h2>", unsafe_allow_html=True) | |
m_l = """<span style="color:black;">Machine learning is a branch of artificial intelligence (AI) that focuses on developing algorithms and models that enable computers to learn patterns and make predictions or decisions without being explicitly programmed. It involves the use of data to train algorithms, allowing machines to improve their performance on a specific task over time. Machine learning is applied in various domains, including image and speech recognition, natural language processing, recommendation systems, and more, to extract insights, recognize patterns, and make predictions based on input data.</span>""" | |
st.markdown(m_l, unsafe_allow_html=True) | |
footer="""<style> | |
a:link , a:visited{ | |
color: blue; | |
background-color: transparent; | |
text-decoration: underline; | |
} | |
a:hover, a:active { | |
color: red; | |
background-color: transparent; | |
text-decoration: underline; | |
} | |
.footer { | |
position: fixed; | |
left: 0; | |
bottom: 0; | |
width: 100%; | |
background-color: white; | |
color: black; | |
text-align: center; | |
} | |
</style> | |
<div class="footer"> | |
<p>Developed with β€ by DataAi Team. Please contact us at +91 12-32130332</p> | |
</div> | |
""" | |
st.markdown(footer,unsafe_allow_html=True) | |
main_bg_ext = "jpg" | |
main_bg = "vally4.jpg" | |
st.markdown( | |
f""" | |
<style> | |
.stApp {{ | |
background: url(data:image/{main_bg_ext};base64,{base64.b64encode(open(main_bg, "rb").read()).decode()}); | |
background-size: cover | |
}} | |
</style> | |
""", | |
unsafe_allow_html=True | |
) | |
add_indentation() | |
# Specify what pages should be shown in the sidebar, and what their titles and icons | |
# should be | |
show_pages( | |
[ | |
Page("app.py", "Home", "π "), | |
Section("GenAI", icon="π€"), | |
Page("pages/AI_Chatbot.py", "AI Chatbot", "π",in_section=True), | |
# Pages after a section will be indented | |
Page("pages/cg.py", "Auto Code Generation", "π"), | |
Page("pages/Auto_Report_Generation.py", "Auto Report Generation", "π"), | |
Page("pages/Auto_Score_Generation.py", "Auto Score Generation", "π"), | |
Page("pages/core_risk.py", "Core Risk Classification", "π"), | |
Page("pages/jury_records.py", "Court Records", "π"), | |
Page("pages/topic_classification.py", "Topic Classification", "π"), | |
Section("Deep Learning", icon="π€"), | |
Page(path = "pages/deep_learning_demo.py", name = "Image Analytics", icon = "π"), | |
Page(path = "pages/deep_learning_demo2.py",name = "Video Analytics", icon ="π"), | |
Page(path = "pages/sp.py",name = "Speech Recognization", icon = "π"), | |
Section("Machine Learning", icon="π€"), | |
Page(path = "pages/machine_learning_demo.py",name = "Regression", icon = "π"), | |
Page(path = "pages/machine_learning_demo2.py",name = "Forecasting", icon = "π"), | |
Page(path = "pages/tc.py",name = "Clustering", icon = "π"), | |
Page(path = "pages/reg.py",name = "Optimization", icon ="π") | |
] | |
) |