Add feedback logging and UI components for analysis and paper matches

app.py

@@ -20,6 +20,12 @@ HF_TOKEN = os.getenv("HF_TOKEN")
 # Login to Hugging Face Hub
 hf_hub_login(token=HF_TOKEN, add_to_git_credential=True)
 
+# Check if using persistent storage
+persistent_storage = os.path.exists("/data")
+if persistent_storage:
+    # Use persistent storage
+    print("Using persistent storage")
+
 # Dataset details
 dataset_name = "nomadicsynth/arxiv-dataset-abstract-embeddings"
 dataset_revision = "v1.0.0"
@@ -488,6 +494,10 @@ latex_delimiters = [
 
 
 def create_interface():
+    # Create CSV loggers
+    analysis_logger = gr.CSVLogger()
+    paper_match_logger = gr.CSVLogger()
+
     with gr.Blocks(
         css="""
     .cell-menu-button {
@@ -549,21 +559,62 @@ def create_interface():
             with gr.Column(scale=1):
                 paper_details_output = gr.Markdown(
                     value="# Paper Details",
-                    label="Paper Details",
+                    label="Paper Details", 
                     latex_delimiters=latex_delimiters,
                     show_copy_button=True,
                     key="paper_details",
                 )
+                paper_feedback = gr.Radio(
+                    ["👍 Good Match", "👎 Poor Match"],
+                    label="Is this paper a good match?",
+                    visible=False
+                )
+                paper_comment = gr.Textbox(
+                    label="Additional feedback on this paper match (optional)", 
+                    visible=False
+                )
+                flag_paper_btn = gr.Button("Submit Paper Feedback", visible=False)
                 analyze_btn = gr.Button("Analyze Connection Potential", variant="primary", interactive=False)
             with gr.Column(scale=1):
-                # Analysis output
                 analysis_output = gr.Markdown(
                     value="# Synergy Analysis",
                     label="Synergy Analysis",
-                    latex_delimiters=latex_delimiters,
+                    latex_delimiters=latex_delimiters, 
                     show_copy_button=True,
                     key="analysis_output",
                 )
+                # Add thumbs up/down for analysis
+                analysis_feedback = gr.Radio(
+                    ["👍 Helpful", "👎 Not Helpful"],
+                    label="Was this analysis helpful?",
+                    visible=False
+                )
+                analysis_comment = gr.Textbox(
+                    label="Additional feedback on analysis (optional)",
+                    visible=False
+                )
+                flag_analysis_btn = gr.Button("Submit Analysis Feedback", visible=False)
+
+        # Set up loggers for analysis and paper match feedback
+        data_path = "/data" if persistent_storage else ""
+        analysis_logger.setup([paper_details_output, analysis_output, analysis_feedback, analysis_comment], data_path + "flagged_analyses")
+        paper_match_logger.setup([abstract_input, paper_details_output, paper_feedback, paper_comment], data_path + "flagged_paper_matches")
+
+        # Show analysis feedback components when analysis is displayed
+        def show_analysis_feedback():
+            return {
+                analysis_feedback: gr.update(visible=True),
+                analysis_comment: gr.update(visible=True),
+                flag_analysis_btn: gr.update(visible=True)
+            }
+
+        # Show paper feedback when paper is selected
+        def show_paper_feedback():
+            return {
+                paper_feedback: gr.update(visible=True),
+                paper_comment: gr.update(visible=True), 
+                flag_paper_btn: gr.update(visible=True)
+            }
 
         # Display paper details when row is selected
         def on_select(evt: gr.SelectData, papers, query):
@@ -610,6 +661,8 @@ def create_interface():
             lambda: gr.update(interactive=True),  # Enable analyze button when paper selected
             outputs=[analyze_btn],
             api_name=False,
+        ).then(
+            show_paper_feedback  # Add feedback UI when paper selected
         )
 
         # Connect analyze button to run analysis
@@ -619,6 +672,19 @@ def create_interface():
             outputs=[analysis_output],
             show_progress_on=[paper_details_output, analysis_output],
             api_name=False,
+        ).then(show_analysis_feedback)
+
+        # Add flagging handlers
+        flag_analysis_btn.click(
+            lambda *args: analysis_logger.flag(list(args)),
+            inputs=[paper_details_output, analysis_output, analysis_feedback, analysis_comment],
+            preprocess=False
+        )
+
+        flag_paper_btn.click(
+            lambda *args: paper_match_logger.flag(list(args)),
+            inputs=[abstract_input, paper_details_output, paper_feedback, paper_comment],
+            preprocess=False
         )
 
     return demo