Update app.py

app.py

@@ -1,17 +1,15 @@
+import oneclass
+import Cosine_distance
 import gradio as gr
 import pandas as pd
 from io import StringIO
 
-# Import the OneClass class
-from oneclass import OneClass
-
-def predict_and_download(positive_csv_file, unlabelled_csv_file, n, Hyperparameter_nu):
-    # Create an instance of the OneClass class
-    oc = OneClass()
-    
-    # Call the select_top_n_papers method
-    selected_paper_info = oc.select_top_n_papers(n, positive_csv_file, unlabelled_csv_file, Hyperparameter_nu)
-    
+def predict_and_download(classifier_oneclass_or_Cosine_distance ,positive_csv_file, unlabelled_csv_file, n,Hyperparameter_nu):
+    if choice == "oneclass" :
+       selected_paper_info = oneclass.select_top_n_papers(n, positive_csv_file, unlabelled_csv_file,Hyperparameter_nu)
+    else :    
+       selected_paper_info = Cosine_distance.recommend_papers(positive_csv_file, positive_csv_file, n)
+        
     # Create a StringIO object to store CSV data
     csv_buffer = StringIO()
     
@@ -28,20 +26,10 @@ def predict_and_download(positive_csv_file, unlabelled_csv_file, n, Hyperparamet
 # Create the interface
 iface = gr.Interface(
     fn=predict_and_download,
-    inputs=[
-        gr.inputs.File(type="csv", label="Positive CSV File"),
-        gr.inputs.File(type="csv", label="Unlabelled CSV File"),
-        gr.inputs.Number(label="Number of Papers to Select", default=10),
-        gr.inputs.Number(label="Hyperparameter nu", default=0.5)
-    ],
-    outputs=[
-        gr.outputs.Dataframe(label="Selected Papers", formats=["csv", "json"]),
-        gr.outputs.DownloadButton(label="Download CSV")
-    ],
-    title="Paper Prediction",
-    description="Enter the number of papers to select and upload CSV files for labelled and unlabelled data.",
-    article="This interface uses the OneClass algorithm to select the top N papers based on the input CSV files. The Hyperparameter nu controls the sensitivity of the algorithm.",
-    theme="default",
+    inputs=["text","file", "file", "number","number"],
+    outputs=[gr.DataFrame(label="Recommended Papers"), gr.DownloadButton(label="Download CSV")],
+    title="Personalized Arxiv Feed",
+    description="Enter text and upload CSV files for labelled and unlabelled data.",
     allow_flagging='never'  # Disable flagging feature
 )