Upload 3 files

app.py

@@ -0,0 +1,91 @@
+import streamlit as st
+import numpy as np
+import transformers
+import re
+import string
+import preprocessor as pre
+
+import torch
+from transformers import BertTokenizer, BertForSequenceClassification
+
+with open("style.css") as f:
+    st.markdown('<style>{}</style>'.format(f.read()), unsafe_allow_html=True) 
+
+# Preparation model and tokenizer
+model_path = "ninahf1503/SA-BERTchatgptapp"
+tokenizer = BertTokenizer.from_pretrained(model_path)
+model = BertForSequenceClassification.from_pretrained(model_path, ignore_mismatched_sizes=True )
+
+# Define the maximum sequence length
+seq_max_length = 55
+
+# Function to tokenizing input text
+def tokenizing_text(sentence):
+    sentence = preprocess_text(sentence)
+    encoded = tokenizer.encode_plus(
+        sentence,
+        add_special_tokens=True,
+        max_length=seq_max_length,
+        truncation=True,
+        padding='max_length',
+        return_tensors='pt'
+    )
+
+    input_ids = encoded['input_ids']
+    attention_mask = encoded['attention_mask']
+    return input_ids, attention_mask
+
+# Function to preprocessing input text
+def preprocess_text(sentence):
+    re_cleansing = "@\S+|https?:\S+|http?:\S|#[A-Za-z0-9]+|^RT[\s]+|(^|\W)\d+"
+    for punctuation in string.punctuation:
+        sentence = sentence.encode().decode('unicode_escape')
+        sentence = re.sub(r'\n', ' ', sentence)
+        sentence = pre.clean(sentence)
+        sentence = re.sub(r'[^\w\s]', ' ', sentence)
+        sentence = re.sub(r'[0-9]', ' ', sentence)
+        sentence = re.sub(re_cleansing, ' ', sentence).strip()
+        sentence = sentence.replace(punctuation, '')
+        sentence = sentence.lower()
+    return sentence
+
+# Function to predict sentiment
+def predict_sentiment(input_text):
+    input_ids, attention_mask = tokenizing_text(input_text)
+
+    with torch.no_grad():
+        outputs = model(input_ids, attention_mask)
+    
+    logits = outputs.logits
+    predict_class = torch.argmax(logits, dim=1).item()
+    
+    label_sentiment = {0: "Bad", 1: "Good", 2: "Neutral"}
+    predict_label = label_sentiment[predict_class]
+    
+    return predict_label
+
+
+
+# Streamlit web app
+def main():
+    st.title("Analisis Sentimen Aplikasi ChatGPT", anchor=False)
+    tweet_text = st.text_area(" ", placeholder="Enter the sentence you want to analyze", label_visibility="collapsed")
+    
+    if st.button("SUBMIT"):
+        if tweet_text.strip() == "":
+            st.title("Text Input Still Empty", anchor=False)
+            st.info("Please fill in the sentence you want to analyze")
+        else:
+            sentiment = predict_sentiment(tweet_text)
+            if sentiment == "Good":
+                st.title("Sentiment Analysis Results", anchor=False)
+                st.markdown('<div style="background-color: #5d9c59; padding: 16px; border-radius: 5px; font-weight: bold; color:white;">This sentence contains a positive sentiment</div>', unsafe_allow_html=True)
+            elif sentiment == "Bad":
+                st.title("Sentiment Analysis Results", anchor=False)
+                st.markdown('<div style="background-color: #df2e38; padding: 16px; border-radius: 5px; font-weight: bold; color:white;">This sentence contains a negative sentiment</div>', unsafe_allow_html=True)
+            else:
+                st.title("Sentiment Analysis Results", anchor=False)
+                st.markdown('<div style="background-color: #ffa500; padding: 16px; border-radius: 5px; font-weight: bold; color:white;">This sentence is neutral</div>', unsafe_allow_html=True)
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -0,0 +1,5 @@
+streamlit == 1.23.0
+torch 
+transformers == 4.30.2
+numpy == 1.23.0
+tweet-preprocessor == 0.6.0

style.css

@@ -0,0 +1,57 @@
+span {
+    font-size: 28px !important;
+    margin-bottom: 5px;
+  }
+  
+  .css-zt5igj span {
+    text-align: center;
+    color: black;
+  }
+  
+  .stTextArea .css-1om1ktf .st-bs {
+    background-color: #fff;
+    border: 1px #00381d;
+    border-radius: 8px;
+    padding: 8px 12px;
+  }
+  
+  .stButton button {
+    background-color: #33fb9a;
+    width: 150px;
+    height: 40px; 
+    color: white;
+    font-size: 16px; 
+    border: none;
+    border-radius: 8px; 
+    cursor: pointer;
+  }
+  
+  .stButton button p {
+    color: white;
+    font-weight: 600;
+    font-size: 18px !important;
+  }
+  
+  .stButton button:hover {
+    background-color: #1bcc76;
+    color: white;
+  }
+  
+  #text-input-still-empty span {
+    font-size: 30px !important;
+  }
+  
+  #sentiment-analysis-results span {
+    font-size: 30px !important;
+  }
+  
+  .stAlert .st-at {
+    background-color: #18d379;
+    border-radius: 5px;
+    color: white;
+  }
+  
+  .css-5rimss, .css-1w6rlcb p {
+    font-weight: bold;
+  }
+