Create app.py

app.py

@@ -0,0 +1,49 @@
+import streamlit as st
+import joblib
+from sentence_transformers import SentenceTransformer
+
+# Load the pre-trained embedding model
+@st.cache_resource  # Cache the embedding model to save loading time
+def load_embedding_model():
+    return SentenceTransformer('neuml/pubmedbert-base-embeddings')
+
+# Load the MLP model
+@st.cache_resource  # Cache the loaded model
+def load_mlp_model():
+    with open("MLP.pkl", "rb") as file:
+        return joblib.load(file)
+
+# Embed text
+def get_embeddings(title, abstract, embedding_model):
+    # Concatenate title and abstract
+    combined_text = title + " " + abstract
+    return embedding_model.encode(combined_text)
+
+# Main Streamlit app
+def main():
+    st.title("MLP Predictor for Titles and Abstracts")
+    
+    # Input fields
+    title = st.text_input("Enter the Title:")
+    abstract = st.text_area("Enter the Abstract:")
+    
+    # Load models
+    embedding_model = load_embedding_model()
+    mlp_model = load_mlp_model()
+    
+    # Predict button
+    if st.button("Predict Label"):
+        if title.strip() == "" or abstract.strip() == "":
+            st.error("Both Title and Abstract are required!")
+        else:
+            # Get embeddings
+            embeddings = get_embeddings(title, abstract, embedding_model)
+            
+            # Make prediction
+            prediction = mlp_model.predict([embeddings])[0]  # Input should be a 2D array
+            
+            # Display result
+            st.success(f"The predicted label is: {prediction}")
+
+if __name__ == "__main__":
+    main()