Streamlit UI

app.py

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+import streamlit as st
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+# Load the fine-tuned model and tokenizer at startup
+model_name = "EbukaGaus/EbukaMBert"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Define the label mapping
+label_mapping = {0: 'Neutral', 1: 'Negative', 2: 'Positive'}
+
+# Define a function to predict sentiment
+def predict_sentiment(text: str):
+    # Tokenise the input text
+    inputs = tokenizer(text, return_tensors="pt")
+
+    # Run inference without tracking gradients
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    # Apply softmax to get probabilities
+    probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
+
+    # Get the most likely class
+    predicted_class = torch.argmax(probabilities, dim=1).item()
+
+    # Map the predicted class to the sentiment label
+    predicted_label = label_mapping[predicted_class]
+
+    # Retrieve the confidence score
+    confidence = probabilities[0][predicted_class].item()
+
+    return predicted_label, confidence
+
+def main():
+    st.title("Simple Sentiment Analysis App")
+
+    # Text input
+    text_input = st.text_area("Enter your text here", "")
+
+    # Predict sentiment when the button is clicked
+    if st.button("Predict Sentiment"):
+        if text_input.strip() == "":
+            st.warning("Please enter some text first.")
+        else:
+            sentiment, confidence = predict_sentiment(text_input)
+            st.write(f"**Sentiment:** {sentiment}")
+            st.write(f"**Confidence:** {confidence:.2f}")
+
+if __name__ == "__main__":
+    main()