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app.py

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+import streamlit as st
+from transformers import pipeline
+
+# Load your fine-tuned model from Hugging Face
+MODEL_NAME = "Tryfonas/fine-tuned-bert-classifier-bds24"  # Update with your Hugging Face model name
+classifier = pipeline("text-classification", model=MODEL_NAME)
+
+# Streamlit UI
+st.title("BERT Text Classifier")
+st.write("Enter text below to classify:")
+
+# User input text
+user_input = st.text_area("Input Text", "Type here...")
+
+if st.button("Classify"):
+    if user_input.strip():
+        # Get model prediction
+        result = classifier(user_input)
+
+        # Extract label and confidence score
+        label = result[0]['label']  # Model output label
+        confidence = result[0]['score']  # Confidence score
+
+        # Convert model labels to "Positive" or "Negative"
+        if label == "LABEL_1":  # Adjust based on your model's labeling
+            sentiment = "Positive 😊"
+        elif label == "LABEL_0":
+            sentiment = "Negative 😞"
+        else:
+            sentiment = "Unknown 🤔"
+
+        # Display results
+        st.subheader("Prediction:")
+        st.write(f"**Sentiment:** {sentiment}")
+    else:
+        st.warning("Please enter some text.")

requirements.txt

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+streamlit
+transformers