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app.py

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+import streamlit as st
+import transformers
+import torch
+
+# Load the model and tokenizer
+model = transformers.AutoModelForSequenceClassification.from_pretrained("ikoghoemmanuell/finetuned_sentiment_model")
+tokenizer = transformers.AutoTokenizer.from_pretrained("ikoghoemmanuell/finetuned_sentiment_tokenizer")
+
+# Define the function for sentiment analysis
+@st.cache_resource
+def predict_sentiment(text):
+    # Tokenize the text
+    encoded_input = tokenizer(text, truncation=True, padding=True, return_tensors='pt')
+    
+    # Forward pass through the model
+    output = model(**encoded_input)
+    logits = output.logits
+    
+    # Compute probabilities and predicted label
+    probabilities = torch.softmax(logits, dim=1)
+    predicted_label = torch.argmax(probabilities, dim=1)
+    
+    # Get sentiment label and score
+    sentiment_label = tokenizer.decode(predicted_label.squeeze().item())
+    sentiment_score = probabilities[0, predicted_label].item()
+    
+    return sentiment_label, sentiment_score
+
+# Setting the page configurations
+st.set_page_config(
+    page_title="Sentiment Analysis App",
+    page_icon=":smile:",
+    layout="wide",
+    initial_sidebar_state="auto",
+)
+
+# Add description and title
+st.write("""
+# How Positive or Negative is your Text?
+Enter some text and we'll tell you if it has a positive, negative, or neutral sentiment!
+""")
+
+
+# Add image
+image = st.image("https://i0.wp.com/thedatascientist.com/wp-content/uploads/2018/10/sentiment-analysis.png", width=400)
+
+# Get user input
+text = st.text_input("Enter some text here:")
+
+# Define the CSS style for the app
+st.markdown(
+"""
+<style>
+body {
+    background-color: #f5f5f5;
+}
+h1 {
+    color: #4e79a7;
+}
+</style>
+""",
+unsafe_allow_html=True
+)
+
+# Show sentiment output
+if text:
+    sentiment, score = predict_sentiment(text)
+    if sentiment == "Positive":
+        st.success(f"The sentiment is {sentiment} with a score of {score*100:.2f}%!")
+    elif sentiment == "Negative":
+        st.error(f"The sentiment is {sentiment} with a score of {score*100:.2f}%!")
+    else:
+        st.warning(f"The sentiment is {sentiment} with a score of {score*100:.2f}%!")

requirements.txt

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+torch
+transformers