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| 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() | |