Update app.py

app.py

@@ -16,19 +16,6 @@ def prediction_pop_model(year, population, pred_year):
     pred_population = slope * (pred_year) + intercept
     return pred_population
 
-def number_to_words(number):
-    # Simple conversion function for demonstration
-    ones = ['', 'one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine']
-    tens = ['', 'ten', 'twenty', 'thirty', 'forty', 'fifty', 'sixty', 'seventy', 'eighty', 'ninety']
-    teens = ['ten', 'eleven', 'twelve', 'thirteen', 'fourteen', 'fifteen', 'sixteen', 'seventeen', 'eighteen', 'nineteen']
-
-    if number < 10:
-        return ones[number]
-    elif number < 20:
-        return teens[number - 10]
-    else:
-        return tens[number // 10] + ' ' + ones[number % 10]
-
 # Data
 year = np.array([1952, 1957, 1962, 1967, 1972, 1977, 1982, 1987, 1992, 1997, 2002, 2007, 2009, 2011, 2013, 2015, 2017, 2019, 2021, 2023, 2024], dtype=np.float64)
 population = np.array([22223309, 25009741, 28173309, 31681188, 34807417, 38783863, 45681811, 52799062, 59402198, 66134291, 73312559, 75100000, 78300000, 81500000, 85800000, 89000000, 94800000, 98900000, 102800000, 106100000, 107300000], dtype=np.float64)
@@ -42,31 +29,30 @@ input_year = st.number_input("Enter the year you want to predict the population
 # Predict button
 if st.button("Predict"):
     predicted_population = prediction_pop_model(year, population, input_year)
-    population_words = number_to_words(int(predicted_population))
-    st.write(f"Predicted Population for {input_year}: {population_words}")
+    st.write(f"Predicted Population for {input_year}: {predicted_population:.2f} million")
 
-    # Plotting
-    fig = plt.figure(figsize=(10, 5))
-    ax = fig.add_subplot(111, projection='3d')
+    # Plotting 3D and 2D
+    fig = plt.figure(figsize=(15, 7))
+
+    # 3D plot
+    ax = fig.add_subplot(121, projection='3d')
     ax.scatter(year, population, zs=0, zdir='z', color='blue', label='Actual Population')
     ax.plot(year, population, zs=0, zdir='z', color='blue')
     ax.scatter([input_year], [predicted_population], zs=0, zdir='z', color='red', label='Predicted Population')
-    
     ax.set_xlabel('Year')
     ax.set_ylabel('Population')
     ax.set_zlabel('Values')
-    ax.set_title('Population Prediction')
+    ax.set_title('Population Prediction (3D)')
     ax.legend()
 
-    st.pyplot(fig)
-
-    # 2D Plot for comparison
-    plt.figure(figsize=(10, 5))
+    # 2D plot
+    plt.subplot(122)
     plt.scatter(year, population, color='blue', label='Actual Population')
     plt.plot(year, population, color='blue')
     plt.scatter(input_year, predicted_population, color='red', label='Predicted Population')
     plt.xlabel('Year')
     plt.ylabel('Population')
-    plt.title('Population Prediction')
+    plt.title('Population Prediction (2D)')
     plt.legend()
-    st.pyplot(plt.gcf())
+
+    st.pyplot(fig)