Update app.py

app.py

@@ -175,10 +175,12 @@ def clean_response(text: str) -> str:
             cleaned.append(f"### {heading}\n" + "\n".join(findings))
 
     text = "\n\n".join(cleaned).strip()
+    logger.debug("Cleaned response length: %d chars", len(text))
     return text if text else ""
 
 def summarize_findings(combined_response: str) -> str:
     if not combined_response or all("No oversights identified" in chunk for chunk in combined_response.split("--- Analysis for Chunk")):
+        logger.info("No clinical oversights identified in analysis")
         return "### Summary of Clinical Oversights\nNo critical oversights identified in the provided records."
 
     sections = {}
@@ -205,9 +207,12 @@ def summarize_findings(combined_response: str) -> str:
             summary_lines.append(summary)
 
     if not summary_lines:
+        logger.info("No clinical oversights identified after summarization")
         return "### Summary of Clinical Oversights\nNo critical oversights identified."
 
-    return "### Summary of Clinical Oversights\n" + "\n".join(summary_lines)
+    summary = "### Summary of Clinical Oversights\n" + "\n".join(summary_lines)
+    logger.info("Summarized findings: %s", summary[:100])
+    return summary
 
 def init_agent():
     logger.info("Initializing model...")
@@ -252,10 +257,12 @@ Patient Record Excerpt (Chunk {0} of {1}):
         def analyze(message: str, history: List[dict], files: List, progress=gr.Progress()):
             history.append({"role": "user", "content": message})
             yield history, None, ""
+            logger.info("Starting analysis for message: %s", message[:100])
 
             extracted = ""
             file_hash_value = ""
             if files:
+                logger.info("Processing %d uploaded files", len(files))
                 def update_extraction_progress(current, total):
                     progress(current / total, desc=f"Extracting text... Page {current}/{total}")
                     return history, None, ""
@@ -265,11 +272,13 @@ Patient Record Excerpt (Chunk {0} of {1}):
                     results = [sanitize_utf8(f.result()) for f in as_completed(futures)]
                     extracted = "\n".join(results)
                     file_hash_value = file_hash(files[0].name) if files else ""
-
+                logger.info("Extraction complete for %d files", len(files))
                 history.append({"role": "assistant", "content": "✅ Text extraction complete."})
                 yield history, None, ""
-                logger.info("Extracted text length: %d chars", len(extracted))
+            else:
+                logger.warning("No files uploaded for analysis")
 
+            logger.info("Extracted text length: %d chars", len(extracted))
             chunk_size = 6000
             chunks = [extracted[i:i + chunk_size] for i in range(0, len(extracted), chunk_size)]
             logger.info("Created %d chunks", len(chunks))
@@ -304,6 +313,7 @@ Patient Record Excerpt (Chunk {0} of {1}):
                             batch_responses.append(chunk_response)
                             torch.cuda.empty_cache()
                             gc.collect()
+                            logger.debug("Processed chunk response length: %d chars", len(chunk_response))
 
                     for chunk_idx, chunk_response in enumerate(batch_responses, batch_idx + 1):
                         if chunk_response:
@@ -323,6 +333,7 @@ Patient Record Excerpt (Chunk {0} of {1}):
                 if report_path:
                     with open(report_path, "w", encoding="utf-8") as f:
                         f.write(combined_response + "\n\n" + summary)
+                logger.info("Analysis complete, report saved at: %s", report_path if report_path else "None")
                 yield history, report_path if report_path and os.path.exists(report_path) else None, summary
 
             except Exception as e: