update app.py

app.py

@@ -18,6 +18,7 @@ from tempfile import mkdtemp
 from urllib.parse import quote
 
 import gradio as gr
+import pandas as pd
 import uvicorn
 from fastapi import Depends, FastAPI, HTTPException, Query, status
 from fastapi.responses import FileResponse
@@ -135,6 +136,26 @@ def _download_markdown(file_path: Path, label: str) -> str:
     return f"[{label}]({_download_href(file_path)})"
 
 
+def _screening_verdict_counts(screened_excel_path: Path) -> dict[str, int]:
+    df = pd.read_excel(screened_excel_path, engine="openpyxl")
+    if "LLM_verdict" not in df.columns:
+        raise KeyError("Expected 'LLM_verdict' column was not found in screening output.")
+
+    verdict_counts = (
+        df["LLM_verdict"]
+        .astype(str)
+        .str.strip()
+        .str.lower()
+        .value_counts()
+        .to_dict()
+    )
+    return {
+        "include": int(verdict_counts.get("include", 0)),
+        "exclude": int(verdict_counts.get("exclude", 0)),
+        "unclear": int(verdict_counts.get("unclear", 0)),
+    }
+
+
 def _auth_credentials() -> tuple[str, str]:
     username = (os.getenv(APP_USERNAME_ENV) or "").strip()
     password = (os.getenv(APP_PASSWORD_ENV) or "").strip()
@@ -338,8 +359,19 @@ def screen_excel(
         return
 
     progress(1, desc="Screening complete.")
+    try:
+        verdict_counts = _screening_verdict_counts(screened_output)
+        completed_status = (
+            "Screening complete: "
+            f"Included {verdict_counts['include']} | "
+            f"Excluded {verdict_counts['exclude']} | "
+            f"Unclear {verdict_counts['unclear']}"
+        )
+    except Exception:
+        completed_status = "Screening complete."
+
     yield (
-        f"Screening complete: {screened_output.name}",
+        completed_status,
         _download_markdown(screened_output, "Download Screened Excel"),
     )
 
@@ -353,9 +385,9 @@ def build_app() -> gr.Blocks:
     )
 
     with gr.Blocks(title="Reference Parser + Foundry Screener") as demo:
-        gr.Markdown("# Reference Parser and Screener")
+        gr.Markdown("# Reference Parsing, Deduplication and LLM-assisted Screening")
         gr.Markdown(
-            "Upload `.txt/.medline/.ris` files, parse into one Excel, then screen with Azure Foundry using Space secrets."
+            "Upload `.txt/.medline/.ris` files, parse into one Excel, then screen for inclusion/exclusion criteria."
         )
         gr.Markdown(f"**Secrets status:** {secrets_note}")
 
@@ -386,15 +418,17 @@ def build_app() -> gr.Blocks:
                 )
                 criteria_inclusion_text = gr.Textbox(
                     label="Inclusion Criteria (one per line)",
-                    lines=4,
+                    lines=2,
                 )
                 criteria_exclusion_text = gr.Textbox(
                     label="Exclusion Criteria (one per line)",
-                    lines=4,
+                    lines=2,
                 )
-                criteria_notes = gr.Textbox(label="Notes (optional)", lines=2)
-                title_column = gr.Textbox(label="Title Column", value="Title")
-                abstract_column = gr.Textbox(label="Abstract Column", value="Abstract")
+                criteria_notes = gr.Textbox(label="Notes (optional)", lines=1)
+                # title_column = gr.Textbox(label="Title Column", value="Title")
+                # abstract_column = gr.Textbox(label="Abstract Column", value="Abstract")
+                title_column = "Title"
+                abstract_column = "Abstract"
 
         parse_btn.click(
             fn=parse_files,