Upload 3 files

app.py

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+import streamlit as st
+import joblib
+import pandas as pd
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.svm import OneClassSVM
+
+# Load the saved model
+model = joblib.load('one_class_svm_model.pkl')
+vectorizer = joblib.load('tfidf_vectorizer.pkl')  # Load the vectorizer used for training
+
+# Define a function for making predictions
+def predict(n=5, retrain=False, positive_labelled_file=None, unlabelled_labelled_file=None):
+    if retrain and positive_labelled_file is not None and unlabelled_labelled_file is not None:
+        # Load the positive labelled and unlabelled data
+        positive_labelled_info = pd.read_csv(positive_labelled_file)
+        unlabelled_labelled = pd.read_csv(unlabelled_labelled_file)
+        
+        # Combine title and abstract for both datasets
+        positive_labelled_info['text'] = positive_labelled_info['title'] + ' ' + positive_labelled_info['abstract']
+        unlabelled_labelled['text'] = unlabelled_labelled['title'] + ' ' + unlabelled_labelled['abstract']
+        
+        # Feature extraction for positive labelled data
+        X_pos = vectorizer.transform(positive_labelled_info['text'])
+        
+        # Fit the model on the new positive labelled data
+        model.fit(X_pos)
+    
+    # Predict the class of unlabelled data
+    X_unlabelled = vectorizer.transform(unlabelled_labelled['text'])
+    predictions = model.predict(X_unlabelled)
+    
+    # Return top n positive papers from unlabelled data
+    positive_indices = predictions == 1
+    top_n_positive_papers = unlabelled_labelled.loc[positive_indices].head(n)
+
+    # Return titles and IDs of selected papers
+    selected_paper_info = top_n_positive_papers[['id', 'title']]
+    return selected_paper_info
+
+# Define the input components
+n_input = st.slider("Top N papers to return:", min_value=1, max_value=20, value=5)
+retrain_input = st.checkbox("Retrain model?")
+positive_labelled_file = st.file_uploader("Upload Positive Labelled Data:", type=['csv'])
+unlabelled_labelled_file = st.file_uploader("Upload Unlabelled Labelled Data:", type=['csv'])
+
+# Check if the user has uploaded files and process them if available
+if positive_labelled_file is not None and unlabelled_labelled_file is not None:
+    # Call the predict function with uploaded data and display the result
+    result = predict(n_input, retrain_input, positive_labelled_file, unlabelled_labelled_file)
+    st.write(result)
+else:
+    st.info("Please upload the CSV files.")

oneclass.py

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+import pandas as pd
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.svm import OneClassSVM
+
+def select_top_n_papers(n, positive_csv_file, unlabelled_csv_file):
+    # Load the positive labelled and unlabelled data
+    positive_labelled_info = pd.read_csv(positive_csv_file)
+    unlabelled_labelled = pd.read_csv(unlabelled_csv_file)
+
+    # Combine title and abstract for both datasets
+    positive_labelled_info['text'] = positive_labelled_info['title'] + ' ' + positive_labelled_info['abstract']
+    unlabelled_labelled['text'] = unlabelled_labelled['title'] + ' ' + unlabelled_labelled['abstract']
+
+    # Train a TF-IDF vectorizer on the positive labelled data
+    vectorizer = TfidfVectorizer()
+    X_pos = vectorizer.fit_transform(positive_labelled_info['text'])
+
+    # Train a one-class SVM model
+    clf = OneClassSVM(kernel='rbf', nu=0.7)  # Adjust parameters as needed
+    clf.fit(X_pos)
+
+    # Transform unlabelled data using the same vectorizer
+    X_unlabelled = vectorizer.transform(unlabelled_labelled['text'])
+
+    # Predict the class of unlabelled data
+    predictions = clf.predict(X_unlabelled)
+
+    # Select the top n papers predicted to be positive
+    positive_indices = predictions == 1
+    top_n_positive_papers = unlabelled_labelled.loc[positive_indices].head(n)
+
+    # Return titles and IDs of selected papers
+    selected_paper_info = top_n_positive_papers[['id', 'title', 'date']]
+    return selected_paper_info

requirements.txt

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+joblib
+scikit-learn