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fe2e7c5 435a527 fe2e7c5 435a527 fe2e7c5 98a9641 fe2e7c5 435a527 fe2e7c5 eff6a13 98a9641 f45e8da 435a527 49c978c 134c4b3 fe2e7c5 | 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 | import streamlit as st
import numpy as np
import pandas as pd
import string
from tensorflow.keras.models import load_model
# Load your trained model
loaded_model = load_model("Word_Correction.h5")
# Load your data
data = pd.read_csv("Word_label_dict.csv") # Make sure to replace "Word_label_dict.csv" with your dataset file
dataa = pd.read_csv("OPTED-Dictionary.csv")
# Create arrays for uppercase and lowercase letters
lowercase_list = np.array(list(string.ascii_lowercase))
uppercase_list = np.array(list(string.ascii_uppercase))
def mat(input_string):
lst = np.zeros(26, dtype=int) # Initialize a NumPy array filled with zeros
for char in input_string:
if char.isupper():
index = np.where(uppercase_list == char)[0] # Find the index of the uppercase letter
if len(index) > 0:
lst[index[0]] += 1
elif char.islower():
index = np.where(lowercase_list == char)[0] # Find the index of the lowercase letter
if len(index) > 0:
lst[index[0]] += 1
return pred(lst)
def pred(array):
y = loaded_model.predict(np.array([array])) # Pass array as a numpy array
top_classes = np.argsort(y, axis=1)[0][-3:][::-1] # Get indices of top three probabilities
top_probabilities = np.sort(y, axis=1)[0][-3:][::-1] # Get top three probabilities
return top_classes, top_probabilities
def get_definition(word):
definition = dataa[dataa['Word'] == word]['Definition'].values
if len(definition) > 0:
return definition[0]
else:
return None
def main():
st.title("**Smart Dictionary with Auto-Correction**")
input_text = st.text_input("Enter the Word")
if st.button("Check"):
top_classes, top_probabilities = mat(input_text)
for i, (class_, probability) in enumerate(zip(top_classes, top_probabilities)):
suggested_word = data[data.Label == class_].Word.values[0]
if st.button(f"Suggested Word: {suggested_word}"):
definition = get_definition(suggested_word)
if definition:
st.write(f"The dictionary meaning of '{suggested_word}' is: {definition}")
else:
st.write(f"No definition found for '{suggested_word}' in the dictionary.")
if __name__ == "__main__":
main()
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