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