Upload 9 files

main.py

@@ -0,0 +1,58 @@
+import streamlit as st
+
+st.set_page_config(layout="wide")
+
+
+video_html = """
+		<style>
+
+		#myVideo {
+		  position: fixed;
+		  right: 0;
+		  bottom: 0;
+		  min-width: 100%; 
+		  min-height: 100%;
+		}
+
+		.content {
+		  position: relative; /* Изменено на position: relative; */
+		  bottom: 0;
+		  background: rgba(0, 0, 0, 0.5);
+		  color: #f1f1f1;
+		  width: 100%;
+		  padding: 20px;
+		}
+
+		[data-testid="stToolbar"] {
+			right: 2rem;
+		}
+
+		div.css-d6uc01.e1tzin5v0 {
+			background-color: rgba(238, 238, 238, 0.5);
+			border: 10px solid #EEEEEE;
+			padding: 5% 5% 5% 10%;
+			border-radius: 5px;
+		}
+
+		</style>	
+		<video autoplay muted loop id="myVideo">
+		  <source src="https://rr4---sn-gvnuxaxjvh-c35z.googlevideo.com/videoplayback?expire=1686269230&ei=zhiCZJXvB-P7zLUP3K6SeA&ip=5.182.110.165&id=o-AOMElUbQqPajhZX5aErHwivnMyjp6i6a4uDOp4L6B3c8&itag=399&aitags=133%2C134%2C135%2C136%2C137%2C160%2C242%2C243%2C244%2C247%2C248%2C278%2C394%2C395%2C396%2C397%2C398%2C399&source=youtube&requiressl=yes&spc=qEK7B7Pbxq1VUrzvbs7fHjl6u6WKTaCFFP3qRYtBLA&vprv=1&svpuc=1&mime=video%2Fmp4&ns=NXLw4J-BddEHxlAHSqJEAZAN&gir=yes&clen=198203522&dur=3609.999&lmt=1630799721330343&keepalive=yes&fexp=24007246,24350018,51000011&beids=24350018&c=WEB&txp=5432434&n=qTHryvAvH9uy7A&sparams=expire%2Cei%2Cip%2Cid%2Caitags%2Csource%2Crequiressl%2Cspc%2Cvprv%2Csvpuc%2Cmime%2Cns%2Cgir%2Cclen%2Cdur%2Clmt&sig=AOq0QJ8wRQIgMOr2k93xnKlQJTWwD400i4MLLRM7EjtE5MegwNmDkqECIQDOrlXOzYVXJZHHUG4jKkur9BYeQZWVRvj8kF0zse7jEg%3D%3D&redirect_counter=1&rm=sn-vgqee776&req_id=9fc7062719efa3ee&cms_redirect=yes&cmsv=e&ipbypass=yes&mh=D8&mip=5.228.41.215&mm=31&mn=sn-gvnuxaxjvh-c35z&ms=au&mt=1686247277&mv=m&mvi=4&pcm2cms=yes&pl=21&lsparams=ipbypass,mh,mip,mm,mn,ms,mv,mvi,pcm2cms,pl&lsig=AG3C_xAwRQIgTj1QLulfeB4eyrjf3vVnn4YAhZgSSDckaKESfmGwLh0CIQCDhcJaLoz9B45N-6cBAS52R3k9vbyeQsDTjhIAAtWA0w%3D%3D")>
+		  Your browser does not support HTML5 video.
+		</video>
+"""
+
+st.markdown(video_html, unsafe_allow_html=True)
+
+col1, col2, col3 = st.columns([3,5,2])
+
+with col2:
+    st.title('✨NLP Project by GPT-Team✨')
+
+col1, col2, col3 = st.columns([2,5,2])
+
+with col2:
+    st.markdown("<div style='text-align: center; font-size: 30px;'>Team members:</div>", unsafe_allow_html=True)
+    st.markdown("<div style='text-align: center; font-size: 25px;'>✨ Maria K.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;✨ Osana B.</div>", unsafe_allow_html=True)
+    st.markdown("<div style='text-align: center; font-size: 25px;'>✨ Veronika K.&nbsp;&nbsp;&nbsp;&nbsp;✨ Anna S.</div>", unsafe_allow_html=True)
+    st.markdown("<div style='text-align: center; font-size: 25px;'></div>", unsafe_allow_html=True)
+    st.markdown("<div style='text-align: center; font-size: 25px;'></div>", unsafe_allow_html=True)

pages/.ipynb_checkpoints/Без названия-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

pages/.ipynb_checkpoints/✨BERT-checkpoint.py

@@ -0,0 +1,119 @@
+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://i.ibb.co/kH8bcr4/1368432.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
+
+################################################################################################
+import torch
+import numpy as np
+import transformers 
+import pickle
+from sklearn.linear_model import LogisticRegression
+
+#def load_model():
+model = transformers.DistilBertModel.from_pretrained(
+"distilbert-base-uncased",
+output_attentions = False,
+output_hidden_states = False
+)
+#model.load_state_dict(torch.load('model/modelbert.pt', map_location=torch.device('cpu')))
+tokenizer = transformers.DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
+    #return model_finetuned, tokenizer
+
+def preprocess_text(text_input, max_len, tokenizer):
+    input_tokens = tokenizer(
+        text_input, 
+        return_tensors='pt', 
+        padding=True, 
+        max_length=max_len,
+        truncation = True
+        )
+    return input_tokens
+
+def predict_sentiment(model, input_tokens):
+    #st.write(input_tokens)
+    ans = {0: "NEGATIVE", 1: "POSITIVE"}
+    last_hidden_states = model(**input_tokens)
+    #st.write('last_hidden_states ok')
+    vectors = last_hidden_states[0][:,0,:].detach().cpu().numpy()
+    #st.write('vectors ok')
+    #output = model(**input_tokens).last_hidden_states.detach().numpy()
+    with open('model/modelbert1.pkl', 'rb') as file:
+        cls = pickle.load(file)
+    result = ans[cls.predict(vectors)[0]]
+    return result
+
+col1, col2, col3 = st.columns([1,5,1])
+with col2:
+
+    st.title('Text sentiment analysis')
+
+col1, col2, col3 = st.columns([2,5,2])
+with col2:
+    
+    max_len = st.slider('Maximum word length', 0, 500, 250)
+
+    text_input = st.text_input("Enter some text")
+    #model, tokenizer = load_model()
+
+    if text_input:
+        input_tokens = preprocess_text(text_input, max_len, tokenizer)
+        output = predict_sentiment(model, input_tokens)
+        st.write(output)
+
+
+
+################################################################################################ 
+#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)

pages/.ipynb_checkpoints/✨first-checkpoint.py

@@ -0,0 +1,127 @@
+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)
+
+# Вставка кода с кнопками
+container = st.container()
+
+# Настройка стиля контейнера с помощью CSS
+container.markdown(
+    """
+    <style>
+    .container-style {
+        display: flex;
+        justify-content: center;
+        align-items: center;
+        margin-top: 20px;
+        margin-bottom: 20px;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+# Расположение кнопок внутри контейнера
+with container:
+    col1, col2, col3 = st.columns(3)
+
+    # Кнопки для перехода на другие страницы
+    if col1.button('Страница 1'):
+        # Действия при нажатии кнопки для перехода на Страницу 1
+        st.experimental_set_query_params(page='?page=1')
+
+    if col2.button('Страница 2'):
+        # Действия при нажатии кнопки для перехода на Страницу 2
+        st.experimental_set_query_params(page='2')
+
+    if col3.button('Страница 3'):
+        # Действия при нажатии кнопки для перехода на Страницу 3
+        st.experimental_set_query_params(page='third.py')

pages/.ipynb_checkpoints/✨third-checkpoint.py

@@ -0,0 +1,114 @@
+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()
+
+
+#img = get_img_as_base64("https://catherineasquithgallery.com/uploads/posts/2021-02/1612739741_65-p-goluboi-fon-tsifri-110.jpg")
+
+page_bg_img = f"""
+<style>
+[data-testid="stAppViewContainer"] > .main {{
+background-image: url("https://wallpapercave.com/wp/wp11966930.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)

pages/Без названия.ipynb

@@ -0,0 +1,371 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "99cbbaf3-6005-4e37-8855-f1d675211128",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "absl-py==1.4.0\n",
+      "aiofiles==23.1.0\n",
+      "aiogram==2.25.1\n",
+      "aiohttp==3.8.4\n",
+      "aiosignal==1.3.1\n",
+      "altair==4.2.2\n",
+      "anyio @ file:///home/conda/feedstock_root/build_artifacts/anyio_1666191106763/work/dist\n",
+      "appdirs==1.4.4\n",
+      "argon2-cffi @ file:///home/conda/feedstock_root/build_artifacts/argon2-cffi_1640817743617/work\n",
+      "argon2-cffi-bindings @ file:///home/conda/feedstock_root/build_artifacts/argon2-cffi-bindings_1666850768662/work\n",
+      "asttokens==2.2.1\n",
+      "astunparse==1.6.3\n",
+      "async-timeout==4.0.2\n",
+      "attrs @ file:///home/conda/feedstock_root/build_artifacts/attrs_1671632566681/work\n",
+      "Babel==2.9.1\n",
+      "backcall==0.2.0\n",
+      "backports.functools-lru-cache @ file:///home/conda/feedstock_root/build_artifacts/backports.functools_lru_cache_1618230623929/work\n",
+      "beautifulsoup4 @ file:///home/conda/feedstock_root/build_artifacts/beautifulsoup4_1679322162244/work\n",
+      "bleach @ file:///home/conda/feedstock_root/build_artifacts/bleach_1674535352125/work\n",
+      "blinker==1.6.1\n",
+      "brotlipy @ file:///home/conda/feedstock_root/build_artifacts/brotlipy_1666764671472/work\n",
+      "cachetools==5.3.0\n",
+      "catboost==1.2\n",
+      "certifi==2022.12.7\n",
+      "cffi @ file:///home/conda/feedstock_root/build_artifacts/cffi_1671179353105/work\n",
+      "charset-normalizer @ file:///home/conda/feedstock_root/build_artifacts/charset-normalizer_1661170624537/work\n",
+      "click==8.1.3\n",
+      "cloudpickle==2.2.1\n",
+      "cmake==3.25.0\n",
+      "colorama @ file:///home/conda/feedstock_root/build_artifacts/colorama_1666700638685/work\n",
+      "comm==0.1.3\n",
+      "conda==23.1.0\n",
+      "conda-package-handling @ file:///home/conda/feedstock_root/build_artifacts/conda-package-handling_1669907009957/work\n",
+      "conda_package_streaming @ file:///home/conda/feedstock_root/build_artifacts/conda-package-streaming_1669733752472/work\n",
+      "contourpy==1.0.7\n",
+      "cryptography @ file:///home/conda/feedstock_root/build_artifacts/cryptography-split_1675828607645/work\n",
+      "cycler==0.11.0\n",
+      "Cython==0.29.34\n",
+      "debugpy==1.6.6\n",
+      "decorator==5.1.1\n",
+      "defusedxml @ file:///home/conda/feedstock_root/build_artifacts/defusedxml_1615232257335/work\n",
+      "Deprecated==1.2.13\n",
+      "entrypoints @ file:///home/conda/feedstock_root/build_artifacts/entrypoints_1643888246732/work\n",
+      "executing==1.2.0\n",
+      "fastapi==0.95.2\n",
+      "fastjsonschema @ file:///home/conda/feedstock_root/build_artifacts/python-fastjsonschema_1677336799617/work/dist\n",
+      "ffmpy==0.3.0\n",
+      "filelock==3.9.0\n",
+      "flatbuffers==23.5.26\n",
+      "flit_core @ file:///home/conda/feedstock_root/build_artifacts/flit-core_1667734568827/work/source/flit_core\n",
+      "fonttools==4.39.3\n",
+      "frozendict==2.3.7\n",
+      "frozenlist==1.3.3\n",
+      "fsspec==2023.5.0\n",
+      "gast==0.4.0\n",
+      "gitdb==4.0.10\n",
+      "GitPython==3.1.31\n",
+      "google-auth==2.19.1\n",
+      "google-auth-oauthlib==1.0.0\n",
+      "google-pasta==0.2.0\n",
+      "gradio==3.32.0\n",
+      "gradio_client==0.2.5\n",
+      "graphviz==0.20.1\n",
+      "grpcio==1.54.2\n",
+      "h11==0.14.0\n",
+      "h5py==3.8.0\n",
+      "html5lib==1.1\n",
+      "httpcore==0.17.2\n",
+      "httpx==0.24.1\n",
+      "huggingface-hub==0.14.1\n",
+      "humanize==4.6.0\n",
+      "idna @ file:///home/conda/feedstock_root/build_artifacts/idna_1663625384323/work\n",
+      "importlib-metadata @ file:///home/conda/feedstock_root/build_artifacts/importlib-metadata_1679167925176/work\n",
+      "importlib-resources @ file:///home/conda/feedstock_root/build_artifacts/importlib_resources_1676919000169/work\n",
+      "ipykernel==6.22.0\n",
+      "ipython==8.12.0\n",
+      "ipython-genutils==0.2.0\n",
+      "jax==0.4.11\n",
+      "jedi==0.18.2\n",
+      "Jinja2 @ file:///home/conda/feedstock_root/build_artifacts/jinja2_1654302431367/work\n",
+      "joblib==1.2.0\n",
+      "json5 @ file:///home/conda/feedstock_root/build_artifacts/json5_1600692310011/work\n",
+      "jsonschema @ file:///home/conda/feedstock_root/build_artifacts/jsonschema-meta_1669810440410/work\n",
+      "jupyter-events @ file:///home/conda/feedstock_root/build_artifacts/jupyter_events_1673559782596/work\n",
+      "jupyter_client==8.1.0\n",
+      "jupyter_core==5.3.0\n",
+      "jupyter_server @ file:///home/conda/feedstock_root/build_artifacts/jupyter_server_1679073341944/work\n",
+      "jupyter_server_terminals @ file:///home/conda/feedstock_root/build_artifacts/jupyter_server_terminals_1673491454549/work\n",
+      "jupyterlab @ file:///home/conda/feedstock_root/build_artifacts/jupyterlab_1666613090338/work\n",
+      "jupyterlab-pygments @ file:///home/conda/feedstock_root/build_artifacts/jupyterlab_pygments_1649936611996/work\n",
+      "jupyterlab_server @ file:///home/conda/feedstock_root/build_artifacts/jupyterlab_server_1679528718717/work\n",
+      "keras==2.12.0\n",
+      "kiwisolver==1.4.4\n",
+      "libclang==16.0.0\n",
+      "linkify-it-py==2.0.2\n",
+      "lit==15.0.7\n",
+      "llvmlite==0.40.0\n",
+      "lxml==4.9.2\n",
+      "magic-filter==1.0.9\n",
+      "Markdown==3.4.3\n",
+      "markdown-it-py==2.2.0\n",
+      "MarkupSafe @ file:///home/conda/feedstock_root/build_artifacts/markupsafe_1674135787083/work\n",
+      "matplotlib==3.7.1\n",
+      "matplotlib-inline==0.1.6\n",
+      "mdit-py-plugins==0.3.3\n",
+      "mdurl==0.1.2\n",
+      "mistune @ file:///home/conda/feedstock_root/build_artifacts/mistune_1675771498296/work\n",
+      "ml-dtypes==0.1.0\n",
+      "mlxtend==0.22.0\n",
+      "mpmath==1.2.1\n",
+      "multidict==6.0.4\n",
+      "multitasking==0.0.11\n",
+      "nbclassic @ file:///home/conda/feedstock_root/build_artifacts/nbclassic_1678277563913/work\n",
+      "nbclient @ file:///home/conda/feedstock_root/build_artifacts/nbclient_1669795076334/work\n",
+      "nbconvert @ file:///home/conda/feedstock_root/build_artifacts/nbconvert-meta_1674590374792/work\n",
+      "nbformat @ file:///home/conda/feedstock_root/build_artifacts/nbformat_1679336765223/work\n",
+      "nest-asyncio==1.5.6\n",
+      "networkx==3.0\n",
+      "nibabel==5.1.0\n",
+      "nltk==3.8.1\n",
+      "notebook @ file:///home/conda/feedstock_root/build_artifacts/notebook_1678109761260/work\n",
+      "notebook_shim @ file:///home/conda/feedstock_root/build_artifacts/notebook-shim_1667478401171/work\n",
+      "numba==0.57.0\n",
+      "numpy==1.23.5\n",
+      "oauthlib==3.2.2\n",
+      "opt-einsum==3.3.0\n",
+      "orjson==3.8.14\n",
+      "packaging==23.0\n",
+      "pandas==1.5.3\n",
+      "pandocfilters @ file:///home/conda/feedstock_root/build_artifacts/pandocfilters_1631603243851/work\n",
+      "parso==0.8.3\n",
+      "patsy==0.5.3\n",
+      "pexpect @ file:///home/conda/feedstock_root/build_artifacts/pexpect_1667297516076/work\n",
+      "pickleshare==0.7.5\n",
+      "Pillow==9.5.0\n",
+      "pkgutil_resolve_name @ file:///home/conda/feedstock_root/build_artifacts/pkgutil-resolve-name_1633981968097/work\n",
+      "platformdirs==3.2.0\n",
+      "plotly==5.14.1\n",
+      "pluggy @ file:///home/conda/feedstock_root/build_artifacts/pluggy_1667232663820/work\n",
+      "pmdarima==2.0.3\n",
+      "prometheus-client @ file:///home/conda/feedstock_root/build_artifacts/prometheus_client_1674535637125/work\n",
+      "prompt-toolkit==3.0.38\n",
+      "protobuf==3.20.3\n",
+      "psutil==5.9.4\n",
+      "ptyprocess @ file:///home/conda/feedstock_root/build_artifacts/ptyprocess_1609419310487/work/dist/ptyprocess-0.7.0-py2.py3-none-any.whl\n",
+      "pure-eval==0.2.2\n",
+      "pyarrow==11.0.0\n",
+      "pyasn1==0.5.0\n",
+      "pyasn1-modules==0.3.0\n",
+      "pycosat @ file:///home/conda/feedstock_root/build_artifacts/pycosat_1666836542287/work\n",
+      "pycparser @ file:///home/conda/feedstock_root/build_artifacts/pycparser_1636257122734/work\n",
+      "pydantic==1.10.8\n",
+      "pydeck==0.8.0\n",
+      "pydub==0.25.1\n",
+      "pyenchant==3.2.2\n",
+      "Pygments==2.14.0\n",
+      "Pympler==1.0.1\n",
+      "pyOpenSSL @ file:///home/conda/feedstock_root/build_artifacts/pyopenssl_1672659226110/work\n",
+      "pyparsing==3.0.9\n",
+      "pyrsistent @ file:///home/conda/feedstock_root/build_artifacts/pyrsistent_1672681463845/work\n",
+      "PySocks @ file:///home/conda/feedstock_root/build_artifacts/pysocks_1661604839144/work\n",
+      "python-dateutil==2.8.2\n",
+      "python-json-logger @ file:///home/conda/feedstock_root/build_artifacts/python-json-logger_1677079630776/work\n",
+      "python-multipart==0.0.6\n",
+      "python-telegram-bot==20.3\n",
+      "pytz @ file:///home/conda/feedstock_root/build_artifacts/pytz_1679742222779/work\n",
+      "pytz-deprecation-shim==0.1.0.post0\n",
+      "PyYAML @ file:///home/conda/feedstock_root/build_artifacts/pyyaml_1666772395347/work\n",
+      "pyzmq==25.0.2\n",
+      "regex==2023.5.5\n",
+      "requests @ file:///home/conda/feedstock_root/build_artifacts/requests_1673863902341/work\n",
+      "requests-oauthlib==1.3.1\n",
+      "rfc3339-validator @ file:///home/conda/feedstock_root/build_artifacts/rfc3339-validator_1638811747357/work\n",
+      "rfc3986-validator @ file:///home/conda/feedstock_root/build_artifacts/rfc3986-validator_1598024191506/work\n",
+      "rich==13.3.3\n",
+      "rsa==4.9\n",
+      "ruamel.yaml @ file:///home/conda/feedstock_root/build_artifacts/ruamel.yaml_1666827327415/work\n",
+      "ruamel.yaml.clib @ file:///home/conda/feedstock_root/build_artifacts/ruamel.yaml.clib_1670412719074/work\n",
+      "safetensors==0.3.1\n",
+      "scikit-learn==1.2.2\n",
+      "scipy==1.10.1\n",
+      "seaborn==0.12.2\n",
+      "semantic-version==2.10.0\n",
+      "Send2Trash @ file:///home/conda/feedstock_root/build_artifacts/send2trash_1628511208346/work\n",
+      "shap==0.41.0\n",
+      "shellingham==1.5.0.post1\n",
+      "SimpleITK==2.2.1\n",
+      "six @ file:///home/conda/feedstock_root/build_artifacts/six_1620240208055/work\n",
+      "sklearn==0.0.post1\n",
+      "slicer==0.0.7\n",
+      "smmap==5.0.0\n",
+      "sniffio @ file:///home/conda/feedstock_root/build_artifacts/sniffio_1662051266223/work\n",
+      "soupsieve @ file:///home/conda/feedstock_root/build_artifacts/soupsieve_1658207591808/work\n",
+      "stack-data==0.6.2\n",
+      "starlette==0.27.0\n",
+      "statsmodels==0.14.0\n",
+      "streamlit==1.23.1\n",
+      "streamlit-nightly==1.23.2.dev20230607\n",
+      "sympy==1.11.1\n",
+      "tabulate==0.9.0\n",
+      "tenacity==8.2.2\n",
+      "tensorboard==2.12.3\n",
+      "tensorboard-data-server==0.7.0\n",
+      "tensorflow==2.12.0\n",
+      "tensorflow-estimator==2.12.0\n",
+      "tensorflow-io-gcs-filesystem==0.32.0\n",
+      "termcolor==2.3.0\n",
+      "terminado @ file:///home/conda/feedstock_root/build_artifacts/terminado_1670253674810/work\n",
+      "threadpoolctl==3.1.0\n",
+      "tinycss2 @ file:///home/conda/feedstock_root/build_artifacts/tinycss2_1666100256010/work\n",
+      "tokenizers==0.13.3\n",
+      "toml==0.10.2\n",
+      "tomli @ file:///home/conda/feedstock_root/build_artifacts/tomli_1644342247877/work\n",
+      "toolz @ file:///home/conda/feedstock_root/build_artifacts/toolz_1657485559105/work\n",
+      "torch==2.0.1+cu118\n",
+      "torchaudio==2.0.2+cu118\n",
+      "torchio==0.18.91\n",
+      "torchmetrics==0.11.4\n",
+      "torchutils==0.0.4\n",
+      "torchvision==0.15.2+cu118\n",
+      "tornado==6.2\n",
+      "tqdm @ file:///home/conda/feedstock_root/build_artifacts/tqdm_1662214488106/work\n",
+      "traitlets==5.9.0\n",
+      "transformers==4.30.0\n",
+      "translit==0.2a1\n",
+      "triton==2.0.0\n",
+      "typer==0.9.0\n",
+      "typing_extensions @ file:///home/conda/feedstock_root/build_artifacts/typing_extensions_1678559861143/work\n",
+      "tzdata==2023.3\n",
+      "tzlocal==4.3\n",
+      "uc-micro-py==1.0.2\n",
+      "urllib3 @ file:///home/conda/feedstock_root/build_artifacts/urllib3_1673452138552/work\n",
+      "uvicorn==0.22.0\n",
+      "validators==0.20.0\n",
+      "watchdog==3.0.0\n",
+      "wcwidth==0.2.6\n",
+      "webencodings==0.5.1\n",
+      "websocket-client @ file:///home/conda/feedstock_root/build_artifacts/websocket-client_1675567828044/work\n",
+      "websockets==11.0.3\n",
+      "Werkzeug==2.3.4\n",
+      "wrapt==1.14.1\n",
+      "xlrd==2.0.1\n",
+      "yarl==1.8.2\n",
+      "yellowbrick==1.5\n",
+      "yfinance==0.2.17\n",
+      "zipp @ file:///home/conda/feedstock_root/build_artifacts/zipp_1677313463193/work\n",
+      "zstandard==0.19.0\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip freeze"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "fba03be3-8dd0-48d4-898c-a7eb2fd5c292",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "fe9c5e5e-2bc8-4585-93e1-4b777ddeb7c4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'2.0.1+cu118'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "torch.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ce5f014d-d484-43a6-ad7f-9e0ba8ba46af",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting transformers\n",
+      "  Downloading transformers-4.30.0-py3-none-any.whl (7.2 MB)\n",
+      "\u001b[2K     \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.2/7.2 MB\u001b[0m \u001b[31m1.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0mm eta \u001b[36m0:00:01\u001b[0m[36m0:00:01\u001b[0m0m\n",
+      "\u001b[?25hRequirement already satisfied: filelock in /home/maria/miniforge3/lib/python3.10/site-packages (from transformers) (3.9.0)\n",
+      "Requirement already satisfied: huggingface-hub<1.0,>=0.14.1 in /home/maria/miniforge3/lib/python3.10/site-packages (from transformers) (0.14.1)\n",
+      "Requirement already satisfied: numpy>=1.17 in /home/maria/miniforge3/lib/python3.10/site-packages (from transformers) (1.23.5)\n",
+      "Requirement already satisfied: packaging>=20.0 in /home/maria/.local/lib/python3.10/site-packages (from transformers) (23.0)\n",
+      "Requirement already satisfied: pyyaml>=5.1 in /home/maria/miniforge3/lib/python3.10/site-packages (from transformers) (6.0)\n",
+      "Requirement already satisfied: regex!=2019.12.17 in /home/maria/miniforge3/lib/python3.10/site-packages (from transformers) (2023.5.5)\n",
+      "Requirement already satisfied: requests in /home/maria/miniforge3/lib/python3.10/site-packages (from transformers) (2.28.2)\n",
+      "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers)\n",
+      "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
+      "\u001b[2K     \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m1.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0mm eta \u001b[36m0:00:01\u001b[0m[36m0:00:01\u001b[0m\n",
+      "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers)\n",
+      "  Downloading safetensors-0.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
+      "\u001b[2K     \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m1.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0mm eta \u001b[36m0:00:01\u001b[0m0:01\u001b[0m:01\u001b[0mm\n",
+      "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /home/maria/miniforge3/lib/python3.10/site-packages (from transformers) (4.64.1)\n",
+      "Requirement already satisfied: fsspec in /home/maria/miniforge3/lib/python3.10/site-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (2023.5.0)\n",
+      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/maria/miniforge3/lib/python3.10/site-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (4.5.0)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /home/maria/miniforge3/lib/python3.10/site-packages (from requests->transformers) (2.1.1)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /home/maria/miniforge3/lib/python3.10/site-packages (from requests->transformers) (3.4)\n",
+      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/maria/miniforge3/lib/python3.10/site-packages (from requests->transformers) (1.26.14)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /home/maria/miniforge3/lib/python3.10/site-packages (from requests->transformers) (2022.12.7)\n",
+      "Installing collected packages: tokenizers, safetensors, transformers\n",
+      "Successfully installed safetensors-0.3.1 tokenizers-0.13.3 transformers-4.30.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install transformers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fc355448-1c49-4bda-8196-caec91650f40",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

pages/✨BERT.py

@@ -0,0 +1,119 @@
+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://i.ibb.co/kH8bcr4/1368432.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
+
+################################################################################################
+import torch
+import numpy as np
+import transformers 
+import pickle
+from sklearn.linear_model import LogisticRegression
+
+#def load_model():
+model = transformers.DistilBertModel.from_pretrained(
+"distilbert-base-uncased",
+output_attentions = False,
+output_hidden_states = False
+)
+#model.load_state_dict(torch.load('model/modelbert.pt', map_location=torch.device('cpu')))
+tokenizer = transformers.DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
+    #return model_finetuned, tokenizer
+
+def preprocess_text(text_input, max_len, tokenizer):
+    input_tokens = tokenizer(
+        text_input, 
+        return_tensors='pt', 
+        padding=True, 
+        max_length=max_len,
+        truncation = True
+        )
+    return input_tokens
+
+def predict_sentiment(model, input_tokens):
+    #st.write(input_tokens)
+    ans = {0: "NEGATIVE", 1: "POSITIVE"}
+    last_hidden_states = model(**input_tokens)
+    #st.write('last_hidden_states ok')
+    vectors = last_hidden_states[0][:,0,:].detach().cpu().numpy()
+    #st.write('vectors ok')
+    #output = model(**input_tokens).last_hidden_states.detach().numpy()
+    with open('model/modelbert1.pkl', 'rb') as file:
+        cls = pickle.load(file)
+    result = ans[cls.predict(vectors)[0]]
+    return result
+
+col1, col2, col3 = st.columns([1,5,1])
+with col2:
+
+    st.title('Text sentiment analysis')
+
+col1, col2, col3 = st.columns([2,5,2])
+with col2:
+    
+    max_len = st.slider('Maximum word length', 0, 500, 250)
+
+    text_input = st.text_input("Enter some text")
+    #model, tokenizer = load_model()
+
+    if text_input:
+        input_tokens = preprocess_text(text_input, max_len, tokenizer)
+        output = predict_sentiment(model, input_tokens)
+        st.write(output)
+
+
+
+################################################################################################ 
+#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)

pages/✨first.py

@@ -0,0 +1,127 @@
+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)
+
+# Вставка кода с кнопками
+container = st.container()
+
+# Настройка стиля контейнера с помощью CSS
+container.markdown(
+    """
+    <style>
+    .container-style {
+        display: flex;
+        justify-content: center;
+        align-items: center;
+        margin-top: 20px;
+        margin-bottom: 20px;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+# Расположение кнопок внутри контейнера
+with container:
+    col1, col2, col3 = st.columns(3)
+
+    # Кнопки для перехода на другие страницы
+    if col1.button('Страница 1'):
+        # Действия при нажатии кнопки для перехода на Страницу 1
+        st.experimental_set_query_params(page='?page=1')
+
+    if col2.button('Страница 2'):
+        # Действия при нажатии кнопки для перехода на Страницу 2
+        st.experimental_set_query_params(page='2')
+
+    if col3.button('Страница 3'):
+        # Действия при нажатии кнопки для перехода на Страницу 3
+        st.experimental_set_query_params(page='third.py')

pages/✨third.py

@@ -0,0 +1,114 @@
+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()
+
+
+#img = get_img_as_base64("https://catherineasquithgallery.com/uploads/posts/2021-02/1612739741_65-p-goluboi-fon-tsifri-110.jpg")
+
+page_bg_img = f"""
+<style>
+[data-testid="stAppViewContainer"] > .main {{
+background-image: url("https://wallpapercave.com/wp/wp11966930.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)