Create app.py

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 input text
+    inputs = tokenizer(text, return_tensors="pt")
+    # Pass the tokenized input through the model
+    outputs = model(**inputs)
+    # Get the predicted class and return the corresponding sentiment
+    predicted_class = torch.argmax(outputs.logits, dim=-1).item()
+    if predicted_class == 0:
+        return "Negative"
+    elif predicted_class == 1:
+        return "Neutral"
+    else:
+        return "Positive"
+
+# 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 = predict_sentiment(text)
+    if sentiment == "Positive":
+        st.success(f"The sentiment is {sentiment}!")
+    elif sentiment == "Negative":
+        st.error(f"The sentiment is {sentiment}.")
+    else:
+        st.warning(f"The sentiment is {sentiment}.")