app.py

import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt
from datasets import load_dataset

# ClimaticHouse Overview
st.write("# ClimaticHouse Overview")
st.write("ClimaticHouse emerges as a pioneering initiative at the intersection of technology and environmental stewardship, dedicated to providing accurate, comprehensive, and accessible climate data through the power of open-source hardware and software. Our mission is to leverage cutting-edge technology to monitor and analyze environmental conditions, enhancing our understanding of climate dynamics and their impact on various ecosystems.")

# Datasets Description
st.write("## Datasets Description")

## climatichouse/space
st.write("### climatichouse/space")
st.write("Description: This dataset is focused on private spaces such as homes, offices, hotels, restaurants, and other indoor environments. It collects data on temperature, humidity, air quality, and other parameters that affect the comfort and livability of these spaces.")

## climatichouse/animal
st.write("### climatichouse/animal")
st.write("Description: This dataset targets farms with animals, collecting information on environmental conditions, animal health, and management practices. It aims to optimize animal welfare and productivity through better environmental monitoring and control.")

## climatichouse/land
st.write("### climatichouse/land")
st.write("Description: This dataset is designed for agricultural farms, gathering data on soil conditions, crop health, weather patterns, and farming practices. The goal is to improve crop yield and sustainability by providing precise and actionable climate information.")

# Submitting Data to Datasets
st.write("## Submitting Data to Datasets")
st.write("To submit data to any of the ClimaticHouse datasets, follow these steps:")
st.write("- Prepare your data: Ensure your data is formatted correctly, including all required fields such as temperature, humidity, soil pH, plant health, etc.")
st.write("- Access the dataset: Navigate to the respective dataset on Hugging Face.")
st.write("- Upload your data: Use the provided interface on Hugging Face to upload your data. Follow the specific guidelines for each dataset to ensure your submission is accepted.")

# Analysis and Training Study
st.write("## Analysis and Training Study")
st.write("ClimaticHouse aims to identify what makes a space more enjoyable or comfortable by analyzing the collected data and training machine learning models. The study focuses on understanding the factors that contribute to the perceived pleasantness of different environments, whether they are private spaces, animal farms, or agricultural land.")

st.write("### Steps for Analysis:")
st.write("1. Data Collection: Gather extensive data from the respective datasets.")
st.write("2. Preprocessing: Clean and preprocess the data to handle missing values, normalize features, and prepare it for analysis.")
st.write("3. Feature Selection: Identify key features that influence the pleasantness of a space, such as temperature, humidity, noise levels, animal health indicators, and crop conditions.")
st.write("4. Model Training: Train machine learning models to predict the pleasantness of a space based on the selected features.")
st.write("5. Evaluation: Evaluate the model's performance using metrics like accuracy, precision, recall, and F1 score.")
st.write("6. Visualization: Generate graphs and visualizations to illustrate the findings and insights derived from the data.")

# Interactive Dashboard
st.write("## Interactive Dashboard")
dataset_option = st.selectbox(
    'Select a dataset',
    ('Space', 'Animal', 'Land')
)

# Load datasets from Hugging Face
datasets = {
    'Space': load_dataset('climatichouse/space'),
    'Animal': load_dataset('climatichouse/animal'),
    'Land': load_dataset('climatichouse/land')
}

# Display data and results based on selection
if dataset_option in datasets:
    st.write(f"### {dataset_option} Dataset")
    st.write(datasets[dataset_option].to_pandas().head())
    # Add your data analysis and visualization code here

# Example of data analysis and visualization
st.write('### Data Analysis and Visualization')
if dataset_option == 'Space':
    plt.figure(figsize=(10, 6))
    plt.hist(datasets['Space']['temperature'], bins=20, alpha=0.7, label='Temperature')
    plt.hist(datasets['Space']['humidity'], bins=20, alpha=0.7, label='Humidity')
    plt.xlabel('Value')
    plt.ylabel('Frequency')
    plt.title('Distribution of Temperature and Humidity in Space Dataset')
    plt.legend()
    st.pyplot(plt)

# Add more visualizations and analysis as needed