Spaces:
Sleeping
Sleeping
File size: 2,625 Bytes
43ce8bf |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 |
import streamlit as st
import pandas as pd
import requests
# Helper functions to fetch data dynamically
def fetch_smartphones():
# Replace with a real API or dataset URL for smartphones
return pd.DataFrame([
{"Model": "iPhone 15", "Processor": "A17 Bionic", "RAM": "6 GB", "Battery": "3200 mAh"},
{"Model": "Samsung Galaxy S23", "Processor": "Snapdragon 8 Gen 2", "RAM": "8 GB", "Battery": "3900 mAh"}
])
def fetch_processors():
# Replace with a real API or dataset URL for processors
return pd.DataFrame([
{"Name": "Intel i9-13900K", "Cores": 24, "Threads": 32, "Base Clock": "3.0 GHz"},
{"Name": "AMD Ryzen 9 7950X", "Cores": 16, "Threads": 32, "Base Clock": "4.5 GHz"}
])
def fetch_gpus():
# Replace with a real API or dataset URL for GPUs
return pd.DataFrame([
{"Name": "NVIDIA RTX 4090", "VRAM": "24 GB", "Base Clock": "2.23 GHz"},
{"Name": "AMD RX 7900 XTX", "VRAM": "24 GB", "Base Clock": "2.3 GHz"}
])
def fetch_laptops():
# Replace with a real API or dataset URL for laptops
return pd.DataFrame([
{"Model": "MacBook Pro 16", "Processor": "M2 Max", "RAM": "32 GB", "Battery Life": "14 hours"},
{"Model": "Dell XPS 15", "Processor": "Intel i7-12700H", "RAM": "16 GB", "Battery Life": "10 hours"}
])
# Main App
def main():
st.title("Global Technology Comparisons")
st.sidebar.title("Choose a category")
# Define categories and their respective data-fetching functions
categories = {
"Smartphones": fetch_smartphones,
"Processors": fetch_processors,
"GPUs": fetch_gpus,
"Laptops": fetch_laptops
}
# User selects the category
category = st.sidebar.selectbox("Select a category:", list(categories.keys()))
# Fetch and display data dynamically
st.header(f"{category} Comparisons")
data = categories[category]()
st.table(data)
# Comparison tool
st.subheader(f"Compare {category}")
col1, col2 = st.columns(2)
# Dropdown for selecting items to compare
option1 = col1.selectbox("Select first item:", data.iloc[:, 0])
option2 = col2.selectbox("Select second item:", data.iloc[:, 0])
# Show comparison results
if option1 and option2:
st.write(f"### Comparison between {option1} and {option2}:")
item1 = data[data.iloc[:, 0] == option1].iloc[0]
item2 = data[data.iloc[:, 0] == option2].iloc[0]
comparison_df = pd.DataFrame({"Feature": data.columns, option1: item1.values, option2: item2.values})
st.table(comparison_df)
if __name__ == "__main__":
main()
|