textcompare

requirements.txt

@@ -1,3 +1,2 @@
-altair
-pandas
 streamlit
+difflib

streamlit_app.py

@@ -1,40 +1,26 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+from difflib import SequenceMatcher
 import streamlit as st
 
-"""
-# Welcome to Streamlit!
+def calcular_similaridade(texto1, texto2):
+    # Criar um objeto SequenceMatcher com os textos dos arquivos
+    seq_matcher = SequenceMatcher(False, texto1, texto2)
 
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+    # Obter a similaridade normalizada entre 0 e 1
+    similaridade_ponderada = seq_matcher.ratio()
 
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
+    return similaridade_ponderada
 
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
+# Exemplo de uso
+st.title('Comparação de textos!')
+st.title(':blue[Isis] :sunglasses:')
+texto1 =  st.text_input(
+    "Entre com o primeiro texto 👇",
+)
+texto2 =  st.text_input(
+    "Entre com o segundo texto 👇",
+)
 
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
+similaridade = calcular_similaridade(texto1, texto2)
+st.text('Indice de similaridade entre os textos.')
+st.text(similaridade)
 
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))