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Update app.py

f1a4d7d verified over 2 years ago

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import streamlit as st
import pandas as pd
import plotly.express as px
import random
import plotly.graph_objects as go
import streamlit.components.v1 as components
import requests
import numpy as np
from streamlit_lottie import st_lottie

st.set_page_config(layout="wide")

def load_lottie_url(url: str):
    r = requests.get(url)
    if r.status_code != 200:
        return None
    return r.json()

# Chargement des données
@st.cache_data
# Fonction pour charger les différents datasets
def load_data():
    # Chemins des fichiers
    path_complete = r'cleaned_merged_data.csv'
    path_opinions = r'opinions.csv'

    # Chargement du dataset complet
    df_complete = pd.read_csv(path_complete)
    df_complete.dropna(inplace=True)  # Nettoyage des données, si nécessaire

    # Chargement du dataset des opinions
    df_opinions = pd.read_csv(path_opinions)
    df_opinions.dropna(inplace=True)  # Nettoyage des données, si nécessaire


    return df_complete, df_opinions

# Chargement des trois instances de dataframe
df, df_opinions = load_data()

# CSS personnalisé
custom_css = """
<style>
/* Personnaliser les titres */
.block-container {
                    padding-top: 1rem;
                    padding-bottom: 0rem;
                    padding-left: 5rem;
                    padding-right: 5rem;
                }
h1 {
    color: #002c4d;
}
[data-testid="stDecoration"] {
		display: none;
	}
h2 {
    color: #002c4d;
    border-bottom: 2px solid #002c4d;
}
/* Personnaliser le bouton */
.stButton button {
    background-color: #002c4d !important;
    color: white !important;
    border-radius: 5px !important;
    border: none !important;
    padding: 8px 15px !important;
    font-size: 16px !important;
}
/* Personnaliser le fond */
body {
    background-color: white !important;
}
/* Ajouter plus de padding-top aux paragraphes */
.custom-paragraph {
    padding-top: 20px;
}
</style>
"""

# Injecter le CSS personnalisé dans l'application
st.markdown(custom_css, unsafe_allow_html=True)

coll1,coll2,coll3= st.columns([1,4,1])
with coll2:
    col1,col2= st.columns([2,5])
    with col1:
        lottie_animation_1 = "https://assets1.lottiefiles.com/packages/lf20_5mhyg2hz.json"
        lottie_anime_json = load_lottie_url(lottie_animation_1)
        st_lottie(lottie_anime_json, key="logo")
    with col2:
        for _ in range(3):  # Ajustez le nombre de lignes selon le besoin
            st.write("")
        st.title('Explore Our Datasets')



# Titre principal et introduction
st.header('Description of our Data')
st.write(" ")
st.write("""
The data presented here were used in our TER project, focused on analyzing the controversy on Twitter. They allowed us to perform various analyses such as:
- Sentiment and emotion analysis: to understand user reactions.
- Social network analysis: to capture the links between hashtags and tweet clusters based on LDA analysis.
- Statistical analysis: which allowed us to connect our previous work through the controversy index, which attempts to quantify the level of controversy of a tweet.""")

# Affichage des datasets
# Vous pouvez remplacer 'df', 'df_opinions', et 'df_controversy_index' par vos variables de dataframe réelles
st.header('Check our Datasets !')

# Ajouter un sélecteur pour le dataset à afficher
dataset_choice = st.selectbox('Select the dataset you want to view:', 
                              ['Complete Dataset', 'Opinions Dataset'])

if dataset_choice == 'Complete Dataset':
    st.subheader('Complete Dataset')
    st.dataframe(df)  
elif dataset_choice == 'Opinions Dataset':
    st.subheader('Opinions Dataset')
    st.dataframe(df_opinions)  

	import streamlit as st
	import pandas as pd
	import plotly.express as px
	import random
	import plotly.graph_objects as go
	import streamlit.components.v1 as components
	import requests
	import numpy as np
	from streamlit_lottie import st_lottie

	st.set_page_config(layout="wide")

	def load_lottie_url(url: str):
	r = requests.get(url)
	if r.status_code != 200:
	return None
	return r.json()

	# Chargement des données
	@st.cache_data
	# Fonction pour charger les différents datasets
	def load_data():
	# Chemins des fichiers
	path_complete = r'cleaned_merged_data.csv'
	path_opinions = r'opinions.csv'

	# Chargement du dataset complet
	df_complete = pd.read_csv(path_complete)
	df_complete.dropna(inplace=True) # Nettoyage des données, si nécessaire

	# Chargement du dataset des opinions
	df_opinions = pd.read_csv(path_opinions)
	df_opinions.dropna(inplace=True) # Nettoyage des données, si nécessaire


	return df_complete, df_opinions

	# Chargement des trois instances de dataframe
	df, df_opinions = load_data()

	# CSS personnalisé
	custom_css = """
	<style>
	/* Personnaliser les titres */
	.block-container {
	padding-top: 1rem;
	padding-bottom: 0rem;
	padding-left: 5rem;
	padding-right: 5rem;
	}
	h1 {
	color: #002c4d;
	}
	[data-testid="stDecoration"] {
	display: none;
	}
	h2 {
	color: #002c4d;
	border-bottom: 2px solid #002c4d;
	}

	/* Personnaliser le bouton */
	.stButton button {
	background-color: #002c4d !important;
	color: white !important;
	border-radius: 5px !important;
	border: none !important;
	padding: 8px 15px !important;
	font-size: 16px !important;
	}

	/* Personnaliser le fond */
	body {
	background-color: white !important;
	}

	/* Ajouter plus de padding-top aux paragraphes */
	.custom-paragraph {
	padding-top: 20px;
	}
	</style>
	"""

	# Injecter le CSS personnalisé dans l'application
	st.markdown(custom_css, unsafe_allow_html=True)

	coll1,coll2,coll3= st.columns([1,4,1])
	with coll2:
	col1,col2= st.columns([2,5])
	with col1:
	lottie_animation_1 = "https://assets1.lottiefiles.com/packages/lf20_5mhyg2hz.json"
	lottie_anime_json = load_lottie_url(lottie_animation_1)
	st_lottie(lottie_anime_json, key="logo")
	with col2:
	for _ in range(3): # Ajustez le nombre de lignes selon le besoin
	st.write("")
	st.title('Explore Our Datasets')



	# Titre principal et introduction
	st.header('Description of our Data')
	st.write(" ")
	st.write("""
	The data presented here were used in our TER project, focused on analyzing the controversy on Twitter. They allowed us to perform various analyses such as:
	- Sentiment and emotion analysis: to understand user reactions.
	- Social network analysis: to capture the links between hashtags and tweet clusters based on LDA analysis.
	- Statistical analysis: which allowed us to connect our previous work through the controversy index, which attempts to quantify the level of controversy of a tweet.""")

	# Affichage des datasets
	# Vous pouvez remplacer 'df', 'df_opinions', et 'df_controversy_index' par vos variables de dataframe réelles
	st.header('Check our Datasets !')

	# Ajouter un sélecteur pour le dataset à afficher
	dataset_choice = st.selectbox('Select the dataset you want to view:',
	['Complete Dataset', 'Opinions Dataset'])

	if dataset_choice == 'Complete Dataset':
	st.subheader('Complete Dataset')
	st.dataframe(df)
	elif dataset_choice == 'Opinions Dataset':
	st.subheader('Opinions Dataset')
	st.dataframe(df_opinions)