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updated the entire app.py
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sanyadureja committed Oct 26, 2024
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st.image('machine-learning.gif', caption='Your Hub for Predictive Insights', use_column_width=True)

st.markdown("""
## Explore Cutting-edge Machine Learning Models
## Explore Cutting-Edge Machine Learning Models
**Predictive Calc** offers a powerful suite of machine learning models designed to assist you in making informed decisions. Whether it's predicting house prices, determining loan eligibility, or evaluating health risks, we have you covered.
""")

# Why Choose Calc?
st.markdown("""
## Why Choose Predictive Calc?
- **Accurate Predictions**: Leverage state-of-the-art algorithms for highly accurate predictions.
- **User-friendly Interface**: Seamlessly interact with models tailored for real-world applications.
- **Comprehensive Calculators**: A collection of models designed for diverse decision-making needs.
- **Health & Financial Models**: Predict house prices, assess loan eligibility, and evaluate health risks such as Parkinson's and stress levels.
""")
## Why Choose Predictive Calc? """)
features = [
{
"title": "Accurate Predictions",
"icon": "🔍",
"description": "Harness cutting-edge machine learning algorithms that provide reliable and precise predictions."
},
{
"title": "User-Friendly Interface",
"icon": "💻",
"description": "Enjoy a seamless, intuitive experience with models designed for practical applications across various domains."
},
{
"title": "Comprehensive Calculators",
"icon": "📊",
"description": "Access a diverse set of models for financial analysis, health assessments, security checks, and more, all in one place."
},
{
"title": "Health & Financial Insights",
"icon": "🏥💰",
"description": "From estimating house prices and checking loan eligibility to evaluating health risks like Parkinson’s and stress levels, Predictive Calc offers essential tools for everyday decision-making."
},
{
"title": "Enhanced Document Analysis & Language Tools",
"icon": "📄🌐",
"description": "With a built-in text summarizer and translator, streamline your reading experience and break language barriers effortlessly. PDF Malware Detection also helps keep your documents safe."
}
]

st.markdown("""
---
**Ready to get started?** Select a calculator from the sidebar to begin your predictive journey!
""")
# Display features in a structured card format
for feature in features:
st.markdown(
f"""
<div style='
background-color: #f8f9fa;
border: 1px solid #e9ecef;
border-radius: 10px;
padding: 15px;
margin: 10px 0;
display: flex;
align-items: center;
color: black;
'>
<span style='font-size: 2em; margin-right: 15px;'>{feature["icon"]}</span>
<div>
<h4 style='margin: 0; color:black;'>{feature["title"]}</h4>
<p style='margin: 5px 0; color:black;'>{feature["description"]}</p>
</div>
</div>
""",
unsafe_allow_html=True
)

st.markdown("---")
st.markdown("## Available Calculators")

# Calculator information in a structured format
calculators = [
{
"name": "Income Estimator",
"description": "Estimate the annual income based on socio-economic and demographic information.",
"details": """
This calculator uses demographic and socio-economic variables to predict income level, providing insights into income patterns.
"""
},
{
"name": "Gold Price Predictor",
"description": "Predict future gold prices using various financial metrics.",
"details": """
### Introduction
The Gold Price Predictor leverages financial metrics and machine learning algorithms to forecast the price of gold (GLD). Gold prices are influenced by various economic factors, and this tool aims to provide accurate predictions based on historical data.
### Gold Price Dataset
The dataset used for this model contains daily financial data, including stock market indices, commodity prices, and currency exchange rates. The goal is to predict the gold price (GLD) using features such as the S&P 500 Index (SPX), crude oil price (USO), silver price (SLV), and the EUR/USD exchange rate.
### Additional Variable Information
- **SPX**: The S&P 500 index value, which tracks the performance of 500 large companies listed on stock exchanges in the United States.
- **USO**: The price of United States Oil Fund (USO), which reflects crude oil prices.
- **SLV**: The price of iShares Silver Trust (SLV), which reflects silver prices.
- **EUR/USD**: The Euro-to-U.S. Dollar exchange rate, which indicates the strength of the euro relative to the U.S. dollar.
- **GLD**: The price of SPDR Gold Shares (GLD), which is the target variable representing gold prices.
"""
},
{
"name": "House Price Estimator",
"description": "Predict the price of a house based on various features.",
"details": """
Using historical and current market data, this tool predicts the house price based on features like location, size, and amenities.
"""
},
{
"name": "Loan Eligibility",
"description": "Check your eligibility for various types of loans based on your financial profile.",
"details": """
This calculator assesses loan eligibility by analyzing credit scores, income, and other relevant financial details.
"""
},
{
"name": "Parkinson's Disease",
"description": "Assess your risk of Parkinson's Disease with advanced ML algorithms.",
"details": """
### Introduction
Parkinson's disease (PD) is a progressive neurodegenerative disorder that primarily affects movement. It often starts with subtle symptoms such as tremors, stiffness, and slow movement.
### Oxford Parkinson's Disease Detection Dataset (UCI ML Repository)
The dataset contains biomedical voice measurements from 31 people, 23 of whom have Parkinson's disease (PD). The main goal is to differentiate between healthy individuals and those with PD using the "status" column, where 0 indicates healthy and 1 indicates PD.
### Additional Variable Information
- **MDVP_Fo(Hz)**: Average vocal fundamental frequency.
- **MDVP_Fhi(Hz)**: Maximum vocal fundamental frequency.
- **MDVP_Flo(Hz)**: Minimum vocal fundamental frequency.
- **MDVP_Jitter(%)**, **MDVP_Jitter(Abs)**, **MDVP_RAP**, **MDVP_PPQ**, **Jitter_DDP**: Measures of variation in fundamental frequency.
- **MDVP_Shimmer**, **MDVP_Shimmer(dB)**, **Shimmer_APQ3**, **Shimmer_APQ5**, **MDVP_APQ**, **Shimmer_DDA**: Measures of variation in amplitude.
- **NHR**, **HNR**: Noise-to-tonal ratio measures in the voice.
- **status**: Health status of the subject (1 - Parkinson's, 0 - healthy).
- **RPDE**, **D2**: Nonlinear dynamical complexity measures.
- **DFA**: Signal fractal scaling exponent.
- **spread1**, **spread2**, **PPE**: Nonlinear measures of fundamental frequency variation.
"""
},
{
"name": "PDF Malware Detector",
"description": "Identify and alert users about potential malware in PDF files.",
"details": """
### Overview
The PDF Malware Detector scans uploaded PDF files for malicious content, ensuring user safety and data protection.
### Key Features
- **File Upload**: Simple drag-and-drop interface for easy file submission.
- **Malware Detection**: Comprehensive analysis to detect harmful elements within PDFs.
- **File Size Limit**: Supports files up to 200MB.
### Use Cases
Perfect for users needing to verify the integrity of PDF documents before opening or sharing.
"""
},
{
"name": "Stress Level Detector",
"description": "Analyze your mental stress levels based on social media interactions.",
"details": """
The model uses text analysis on social media data to identify signs of stress, helping users understand their mental health patterns.
"""
},
{
"name": "Text Summarizer",
"description": "Save time with concise, professional summaries of lengthy texts.",
"details": """
Generate quick and comprehensive summaries of lengthy documents, ideal for students, researchers, and professionals.
"""
},
{
"name": "Real-Time Language Translator",
"description": "Translate spoken language into other languages instantly for seamless communication.",
"details": """
### Overview
The Real-Time Language Translator uses advanced speech recognition and NLP to provide immediate translations between languages, enhancing communication in diverse settings.
### Key Features
- **Instant Translation**: Real-time spoken language translation.
- **Multiple Languages**: Supports a variety of source and target languages.
- **User-Friendly Interface**: Easy to navigate for all users.
### Use Cases
Ideal for travel, business meetings, and language learning, breaking down language barriers effortlessly.
"""
},
]

# Define shades of blue for calculators
blue_shades = [
"#D1E8E2", # Light Blue
"#A0D6E0", # Soft Blue
"#7FB3E8", # Sky Blue
]

# Display calculators in a table layout with two columns per row
for i in range(0, len(calculators), 2):
cols = st.columns(2)
for j, col in enumerate(cols):
if i + j < len(calculators):
calc = calculators[i + j]
# Use modulo to cycle through the blue shades
color_index = (i + j) % len(blue_shades)
color = blue_shades[color_index]
with col:
# Styled container for heading and description with different blue shades
st.markdown(
f"""
<div style='
background-color: {color};
border-radius: 10px;
padding: 15px;
margin-bottom: 10px;
'>
<h3 style='margin: 0; color: black;'>{calc['name']}</h3>
<p style='margin: 5px 0; color: black;'>{calc['description']}</p>
</div>
""",
unsafe_allow_html=True
)
# More Info expander
with st.expander("More Info"):
st.write(calc["details"])
st.markdown("---")

# Add a "Get Started" section at the bottom
st.markdown("## Get Started Today!")
st.markdown("Explore our calculators and take control of your predictive analytics journey!")

# List of available calculators
st.subheader("Available Calculators:")
st.write(
"- **Customer Income Estimation**: Estimate the annual income of a person based on socio-economic and demographic information."
)
st.write(
"- **House Price Prediction**: Estimate the price of a house based on various features."
)
st.write("- **Loan Eligibility**: Check your eligibility for different types of loans.")
st.write(
"- **Stress Level Detector**: Analyze your mental stress levels based on social media interactions."
)
st.write(
"- **Parkinson's Disease Detector**: Assess your risk of Parkinson's Disease with advanced machine learning algorithms."
)

# Parkinson's Disease Detector Section
with st.expander("Parkinson's Disease Detector - More Information"):
st.subheader("Introduction")
st.write(
"""
Parkinson's disease (PD) is a progressive neurodegenerative disorder that primarily affects movement. It often starts with subtle symptoms such as tremors, stiffness, and slow movement.
"""
)

# Dataset section
st.subheader("Oxford Parkinson's Disease Detection Dataset (UCI ML Repository)")
st.write(
"""
The dataset contains biomedical voice measurements from 31 people, 23 of whom have Parkinson's disease (PD). The main goal is to differentiate between healthy individuals and those with PD using the "status" column, where 0 indicates healthy and 1 indicates PD.
"""
)

# Input features section
st.subheader("Additional Variable Information")
st.write(
"""
- **MDVP_Fo(Hz)**: Average vocal fundamental frequency.
- **MDVP_Fhi(Hz)**: Maximum vocal fundamental frequency.
- **MDVP_Flo(Hz)**: Minimum vocal fundamental frequency.
- **MDVP_Jitter(%)**, **MDVP_Jitter(Abs)**, **MDVP_RAP**, **MDVP_PPQ**, **Jitter_DDP**: Measures of variation in fundamental frequency.
- **MDVP_Shimmer**, **MDVP_Shimmer(dB)**, **Shimmer_APQ3**, **Shimmer_APQ5**, **MDVP_APQ**, **Shimmer_DDA**: Measures of variation in amplitude.
- **NHR**, **HNR**: Noise-to-tonal ratio measures in the voice.
- **status**: Health status of the subject (1 - Parkinson's, 0 - healthy).
- **RPDE**, **D2**: Nonlinear dynamical complexity measures.
- **DFA**: Signal fractal scaling exponent.
- **spread1**, **spread2**, **PPE**: Nonlinear measures of fundamental frequency variation.
"""
)

st.write(
"- **Gold Price Predictor**: Predict future gold prices leverages financial metrics and machine learning algorithm."
)

# Gold Price Predictor Section
with st.expander("Gold Price Predictor - More Information"):
st.subheader("Introduction")
st.write(
"""
The Gold Price Predictor leverages financial metrics and machine learning algorithms to forecast the price of gold (GLD). Gold prices are influenced by various economic factors, and this tool aims to provide accurate predictions based on historical data.
"""
)

# Dataset section
st.subheader("Gold Price Dataset")
st.write(
"""
The dataset used for this model contains daily financial data, including stock market indices, commodity prices, and currency exchange rates. The goal is to predict the gold price (GLD) using features such as the S&P 500 Index (SPX), crude oil price (USO), silver price (SLV), and the EUR/USD exchange rate.
"""
)

# Input features section
st.subheader("Additional Variable Information")
st.write(
"""
- **SPX**: The S&P 500 index value, which tracks the performance of 500 large companies listed on stock exchanges in the United States.
- **USO**: The price of United States Oil Fund (USO), which reflects crude oil prices.
- **SLV**: The price of iShares Silver Trust (SLV), which reflects silver prices.
- **EUR/USD**: The Euro-to-U.S. Dollar exchange rate, which indicates the strength of the euro relative to the U.S. dollar.
- **GLD**: The price of SPDR Gold Shares (GLD), which is the target variable representing gold prices.
"""
)

#Text Summarization Section

st.write(
"- *Text Summarizer*: Save time with concise, professional summaries of lengthy texts—tailored to meet your needs and streamline your reading experience."
)
with st.expander("Text Summarizer - More Information"):
st.subheader("Introduction")
st.write(
"""
Many struggle with summarizing large texts or learning from lengthy materials. This model simplifies the process, offering concise summaries that enhance understanding and speed up learning—perfect for students and professionals alike.
"""
)
# Footer or additional information
st.markdown("## Legal Information")
st.write("© 2024 Yashasvini Sharma. All rights reserved.")
st.write("This project is licensed under the MIT License.")

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