Add application file

app.py

@@ -0,0 +1,144 @@
+
+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.")