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import streamlit as st
import pandas as pd
from transformers import AutoTokenizer, AutoModel,AutoModelForSequenceClassification
import torch
num_classes = 6
#tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
tokenizer = AutoTokenizer.from_pretrained("KhaldiAbderrhmane/bert-emotion",trust_remote_code=True)
model = AutoModelForSequenceClassification.from_pretrained("KhaldiAbderrhmane/bert-emotion",trust_remote_code=True)
def prediction_sentiment(review):
t= tokenizer(review, truncation=True, padding=True, max_length=128, return_tensors='pt')
inpt = t['input_ids']
mask = t['attention_mask']
outputs = model(inpt,mask)
outputs = outputs.logits
predicted= torch.max(outputs, 1).indices
if predicted == 0:
sentiment = "Sadness"
elif predicted == 1:
sentiment = "Joy"
elif predicted == 2:
sentiment = "Love"
elif predicted == 3:
sentiment = "Anger"
elif predicted == 4:
sentiment = "Fear"
else:
sentiment = "Surprise"
return sentiment
users = {"abdelmalek": [["this movie was so nice", "positive"], ["what the hell was that", "negative"], ["man this was good", "positive"]]}
columns = ["comment", "sentiment"]
user_name = st.text_input("User Name")
if user_name:
if user_name in users:
user_input = st.text_input("Enter your comment:")
if user_input:
sentiment = prediction_sentiment(user_input)
st.write('Your sentiment is:', sentiment)
users[user_name].append([user_input, sentiment])
else:
users[user_name] = []
st.write("Your user name has been added.")
user_input = st.text_input("Enter your comment:")
if user_input:
sentiment = prediction_sentiment(user_input)
st.write('Your sentiment is:', sentiment)
users[user_name].append([user_input, sentiment])
if st.button("Your comment:"):
if user_name in users:
df_t = pd.DataFrame(users[user_name], columns=columns)
card_css = """
<style>
.card {
background-color: #1A2E4D;
border-radius: 10px;
padding: 20px;
margin: 10px 0;
box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
}
.card-title {
font-size: 24px;
font-weight: bold;
color: #F4F6FF;
}
.card-content {
font-size: 18px;
color: #52709E;
}
.sentiment-circle {
width: 15px; /* Adjust size as needed */
height: 15px; /* Adjust size as needed */
border-radius: 50%;
display: inline-block;
position: absolute;
top: 50%;
right: 0;
transform: translateY(-50%);
margin-right: 10px; /* Space between circle and text */
}
.positive {
background-color: #82D853; /* Green background for positive sentiment */
}
.negative {
background-color: #D85353; /* Red background for negative sentiment */
}
</style>
"""
st.markdown(card_css, unsafe_allow_html=True)
for comment, sentiment in df_t.values:
sentiment_class = "positive" if sentiment == "positive" else "negative"
sentiment_circle = f'<div class="sentiment-circle {sentiment_class}" style="background-color: {"#82D853" if sentiment == "positive" else "#D85353"};"></div>'
border_color = "border: 2px solid #82D853;" if sentiment == "positive" else "border: 2px solid #D85353;"
card_content = f"""
<div class="card" style="{border_color}">
<div class="card-title">{user_name}</div>
<div class="card-content">
{comment}
{sentiment_circle}
</div>
</div>
"""
st.markdown(card_content, unsafe_allow_html=True)
else:
st.error("No history available for this user.")
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