Update app.py

app.py

@@ -2,7 +2,6 @@ import numpy as np
 import streamlit as st
 import matplotlib.pyplot as plt
 from mpl_toolkits.mplot3d import Axes3D
-import inflect
 
 def prediction_pop_model(year, population, pred_year):
     n = len(year)
@@ -18,8 +17,17 @@ def prediction_pop_model(year, population, pred_year):
     return pred_population
 
 def number_to_words(number):
-    p = inflect.engine()
-    return p.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)
@@ -34,7 +42,7 @@ 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(predicted_population)
+    population_words = number_to_words(int(predicted_population))
     st.write(f"Predicted Population for {input_year}: {population_words}")
 
     # Plotting