Update app.py

app.py

@@ -1,53 +1,53 @@
 import streamlit as st
 import requests
 import matplotlib.pyplot as plt
-import pycountry
 
-# World Bank API URL for GDP & Military Expenditure
-WB_API_URL = "https://api.worldbank.org/v2/country/{}/indicator/{}?format=json"
+# API URLs
+REST_COUNTRIES_API = "https://restcountries.com/v3.1/name/{}"
+WORLD_BANK_API = "https://api.worldbank.org/v2/country/{}/indicator/{}?format=json"
 
-# Military Personnel API (Custom dataset or external source needed)
-MILITARY_PERSONNEL_API = "https://example.com/api/military_personnel/{}"  # Placeholder API (replace with a real one)
-
-# Function to fetch ISO country code
-def get_country_code(country_name):
-    try:
-        country = pycountry.countries.lookup(country_name)
-        return country.alpha_3
-    except LookupError:
-        return None
-
-# Function to fetch data from World Bank API
+# Function to get World Bank data
 def get_world_bank_data(country_code, indicator):
     try:
-        response = requests.get(WB_API_URL.format(country_code, indicator))
+        response = requests.get(WORLD_BANK_API.format(country_code, indicator))
         data = response.json()
         if isinstance(data, list) and len(data) > 1 and data[1]:
-            return data[1][0]['value'] if data[1][0]['value'] else "N/A"
+            return data[1][0].get('value', "N/A")
     except:
         pass
     return "N/A"
 
-# Function to fetch active military personnel
-def get_military_personnel(country_code):
+# Function to get country info (Area & Population)
+def get_country_info(country_name):
     try:
-        response = requests.get(MILITARY_PERSONNEL_API.format(country_code))
+        response = requests.get(REST_COUNTRIES_API.format(country_name))
         data = response.json()
-        return data.get("active_military", "N/A")  # Adjust based on API response
+        if isinstance(data, list) and len(data) > 0:
+            country_data = data[0]
+            return {
+                "Area (sq km)": country_data.get("area", "N/A"),
+                "Population": country_data.get("population", "N/A"),
+                "Country Code": country_data.get("cca2", "").lower()  # Get country code for World Bank API
+            }
     except:
-        return "N/A"
+        pass
+    return None
 
-# Function to fetch all military data
+# Function to fetch all military-related data
 def get_military_data(country_name):
-    country_code = get_country_code(country_name)
-    if not country_code:
-        return None  # Invalid country name
+    country_info = get_country_info(country_name)
+    if not country_info:
+        return None
+    
+    country_code = country_info["Country Code"]
     
     return {
         "Country": country_name,
+        "Population": country_info["Population"],
+        "Area (sq km)": country_info["Area (sq km)"],
         "GDP (in USD)": get_world_bank_data(country_code, "NY.GDP.MKTP.CD"),
         "Military Expenditure (% of GDP)": get_world_bank_data(country_code, "MS.MIL.XPND.GD.ZS"),
-        "Active Military Personnel": get_military_personnel(country_code),
+        "Active Military Personnel": "N/A"  # No global API for this, needs a separate dataset
     }
 
 # Streamlit UI
@@ -62,12 +62,25 @@ if option == "Single Country Data":
         if data:
             st.json(data)
             
-            # Bar Chart Visualization
-            fig, ax = plt.subplots(figsize=(6, 4))
-            labels = ["GDP", "Military Expenditure (% of GDP)", "Active Military Personnel"]
-            values = [data["GDP (in USD)"], data["Military Expenditure (% of GDP)"], data["Active Military Personnel"]]
-            
-            ax.bar(labels, values, color=['blue', 'red', 'green'])
+            # Ensure numeric values for plotting (replace "N/A" with 0)
+            def safe_float(value):
+                try:
+                    return float(value)
+                except:
+                    return 0
+
+            values = [
+                safe_float(data["GDP (in USD)"]),
+                safe_float(data["Military Expenditure (% of GDP)"]),
+                safe_float(data["Active Military Personnel"]),
+                safe_float(data["Area (sq km)"]),
+                safe_float(data["Population"])
+            ]
+
+            # Visualization: Bar Chart
+            fig, ax = plt.subplots(figsize=(7, 5))
+            labels = ["GDP", "Military Expenditure (% of GDP)", "Active Military", "Area", "Population"]
+            ax.bar(labels, values, color=['blue', 'red', 'green', 'purple', 'orange'])
             ax.set_ylabel("Value")
             ax.set_title(f"Military Data for {country}")
             st.pyplot(fig)
@@ -87,24 +100,37 @@ elif option == "Compare Two Countries":
         data2 = get_military_data(country2)
 
         if data1 and data2:
-            st.subheader(f"Comparison: {data1['Country']} vs {data2['Country']}")
+            st.subheader(f"📊 Comparison: {data1['Country']} vs {data2['Country']}")
             st.table([
                 ["Metric", data1['Country'], data2['Country']],
+                ["Population", data1['Population'], data2['Population']],
+                ["Area (sq km)", data1['Area (sq km)'], data2['Area (sq km)']],
                 ["GDP (in USD)", data1['GDP (in USD)'], data2['GDP (in USD)']],
                 ["Military Expenditure (% of GDP)", data1['Military Expenditure (% of GDP)'], data2['Military Expenditure (% of GDP)']],
                 ["Active Military Personnel", data1['Active Military Personnel'], data2['Active Military Personnel']],
             ])
 
+            # Ensure numeric values for plotting (replace "N/A" with 0)
+            def safe_float(value):
+                try:
+                    return float(value)
+                except:
+                    return 0
+
+            values1 = [safe_float(data1["GDP (in USD)"]), safe_float(data1["Military Expenditure (% of GDP)"]),
+                       safe_float(data1["Active Military Personnel"]), safe_float(data1["Area (sq km)"]), safe_float(data1["Population"])]
+
+            values2 = [safe_float(data2["GDP (in USD)"]), safe_float(data2["Military Expenditure (% of GDP)"]),
+                       safe_float(data2["Active Military Personnel"]), safe_float(data2["Area (sq km)"]), safe_float(data2["Population"])]
+
             # Visualization: Comparison Bar Chart
-            fig, ax = plt.subplots(figsize=(6, 4))
-            labels = ["GDP", "Military Expenditure (% of GDP)", "Active Military Personnel"]
-            values1 = [data1["GDP (in USD)"], data1["Military Expenditure (% of GDP)"], data1["Active Military Personnel"]]
-            values2 = [data2["GDP (in USD)"], data2["Military Expenditure (% of GDP)"], data2["Active Military Personnel"]]
-            
+            fig, ax = plt.subplots(figsize=(7, 5))
+            labels = ["GDP", "Military Expenditure (% of GDP)", "Active Military", "Area", "Population"]
             x = range(len(labels))
+
             ax.bar(x, values1, width=0.4, label=data1["Country"], color='blue', align='center')
             ax.bar([i + 0.4 for i in x], values2, width=0.4, label=data2["Country"], color='red', align='center')
-            
+
             ax.set_xticks([i + 0.2 for i in x])
             ax.set_xticklabels(labels)
             ax.set_ylabel("Value")