Update pages/✨first.py

pages/✨first.py

@@ -1,112 +1,48 @@
+rom sklearn.feature_extraction.text import CountVectorizer
+from sklearn.linear_model import LogisticRegression
+import re
+import string
+import pickle
 import streamlit as st
-import base64
-import streamlit as st
-import plotly.express as px
-
-df = px.data.iris()
-
-@st.cache_data
-def get_img_as_base64(file):
-    with open(file, "rb") as f:
-        data = f.read()
-    return base64.b64encode(data).decode()
-
-
-page_bg_img = f"""
-<style>
-[data-testid="stAppViewContainer"] > .main {{
-background-image: url("https://wallpapercave.com/wp/wp6480460.jpg");
-background-size: 115%;
-background-position: top left;
-background-repeat: no-repeat;
-background-attachment: local;
-}}
-
-[data-testid="stSidebar"] > div:first-child {{
-background-image: url("https://ibb.co/ZBkdJRg");
-background-size: 115%;
-background-position: center; 
-background-repeat: no-repeat;
-background-attachment: fixed;
-}}
-
-[data-testid="stHeader"] {{
-background: rgba(0,0,0,0);
-}}
-
-[data-testid="stToolbar"] {{
-right: 2rem;
-}}
-
-div.css-1n76uvr.e1tzin5v0 {{
-background-color: rgba(238, 238, 238, 0.5);
-border: 10px solid #EEEEEE;
-padding: 5% 5% 5% 10%;
-border-radius: 5px;
-}}
-
-</style>
-"""
-st.markdown(page_bg_img, unsafe_allow_html=True)
-
-import tensorflow as tf
-from tensorflow import keras
-import numpy as np
-import matplotlib.pyplot as plt
-
-################################################################################################
-#Тут нужно будет добаить модель. Ниже пример:
-
-# # Загрузка модели
-# model = keras.models.load_model('cgan_model.h5')
-
-# # Задание размерностей входных данных модели
-# latent_dim = 128
-# num_classes = 10
-
-# # Функция для генерации изображения
-# def generate_image(number):
-#     random_latent_vector = tf.random.normal(shape=(1, latent_dim))
-#     one_hot_label = tf.one_hot([number], num_classes)
-#     input_data = tf.concat([random_latent_vector, one_hot_label], axis=1)
-
-#     generated_image = model.predict(input_data)
-#     generated_image = generated_image.reshape(28, 28)
-#     generated_image = tf.image.resize(generated_image[None, ...], (28, 28))[0]  # Добавлено [None, ...] для добавления измерения
-#     return generated_image
-
-################################################################################################
-
-#Оформление
-
-col1, col2, col3 = st.columns([1,5,1])
-with col2:
-
-    st.title('Название модели')
-    
-col1, col2, col3 = st.columns([2,5,2])
-with col2:
-
-    number = st.slider('Выберите число:', 0, 9, step=1)
-
-################################################################################################  
-# Часть, отображаемая на странице
-
-#     number = st.slider('Выберите число:', 0, 9, step=1)
-
-
-#  #col1.subheader("Гистограмма total_bill:")
-
-#     # Генерация и отображение изображения
-#     generated_image = generate_image(number)
-#     generated_image_np = generated_image.numpy()  # Преобразование в массив NumPy
-#     fig, ax = plt.subplots()
-#     ax.scatter([1, 2], [1, 2], color='black')
-#     plt.imshow(generated_image_np, cmap='gray')
-#     plt.axis('off')
-#     fig.set_size_inches(3, 3)
-#     st.pyplot(fig)
 
-################################################################################################ 
-#st.markdown("<div style='text-align: center; font-size: 25px;'> ", unsafe_allow_html=True)
-#st.markdown("<div style='text-align: center; font-size: 25px;'> ", unsafe_allow_html=True)
+# Функция очистки текста
+def clean(text):
+    text = text.lower() # нижний регистр
+    text = re.sub(r'http\S+', " ", text) # удаляем ссылки
+    text = re.sub(r'@\w+',' ',text) # удаляем упоминания пользователей
+    text = re.sub(r'#\w+', ' ', text) # удаляем хэштеги
+    text = re.sub(r'\d+', ' ', text) # удаляем числа
+    text = text.translate(str.maketrans('', '', string.punctuation))
+    return text
+
+# Загрузка весов модели
+
+model_filename = '/home/nika/ds-phase-2/10-nlp/model_weights.pkl'
+with open(model_filename, 'rb') as file:
+    model = pickle.load(file)
+
+# Загрузка весов векторизатора
+vectorizer = CountVectorizer()
+vectorizer_filename = '/home/nika/ds-phase-2/10-nlp/vectorizer_weights.pkl'
+with open(vectorizer_filename, 'rb') as file:
+    vectorizer = pickle.load(file)
+
+# Само приложение  
+
+st.title("CritiSense")
+st.subheader("Movie Review Sentiment Analyzer")
+st.write("CritiSense is a powerful app that analyzes the sentiment of movie reviews.") 
+st.write("Whether you want to know if a review is positive or negative, CritiSense has got you covered.") 
+st.write("Just enter the review, and our app will provide you with instant sentiment analysis.")
+st.write("Make informed decisions about movies with CritiSense!")
+user_review = st.text_input("Enter your review:", "")
+user_review_clean = clean(user_review)
+user_features = vectorizer.transform([user_review_clean])
+prediction = model.predict(user_features)
+
+st.write("Review:", user_review)
+
+if prediction == 1:
+    st.markdown("<p style='color: green;'>Sentiment: Positive</p>", unsafe_allow_html=True)
+else:
+    st.markdown("<p style='color: red;'>Sentiment: Negative</p>", unsafe_allow_html=True)