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app.py

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+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import numpy as np
+from scipy.special import softmax
+
+
+# Add description and title
+st.write("""
+# Sentiment Analysis App
+""")
+
+
+# Add image
+image = st.image("images.png", width=200)
+
+
+# Get user input
+text = st.text_input("Type here:")
+button = st.button('Analyze')
+
+# Define the CSS style for the app
+st.markdown(
+"""
+<style>
+body {
+    background-color: #f5f5f5;
+}
+h1 {
+    color: #4e79a7;
+}
+</style>
+""",
+unsafe_allow_html=True
+)
+
+
+def preprocess(text):
+    new_text = []
+    for t in text.split(" "):
+        t = '@user' if t.startswith('@') and len(t) > 1 else t
+        t = 'http' if t.startswith('http') else t
+        new_text.append(t)
+    return " ".join(new_text)
+
+@st.cache_resource()
+def get_model():
+   # Load the model and tokenizer
+    tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
+    model = AutoModelForSequenceClassification.from_pretrained("MrDdz/bert-base-uncased")
+    return tokenizer,model
+tokenizer, model = get_model()
+
+if  button:
+    text_sample = tokenizer(text, padding = 'max_length',return_tensors = 'pt')
+    # print(text_sample)
+    output = model(**text_sample)
+    scores_ = output[0][0].detach().numpy()
+    scores_ = softmax(scores_)
+
+    labels = ['Negative','Neutral','Positive']
+    scores = {l:float(s) for (l,s) in zip(labels,scores_)}
+    st.write("Prediction :",scores)

requirements.txt

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+pandas
+transformers
+streamlit
+torch
+scikit-learn
+numpy