Update app.py

app.py

@@ -4,9 +4,8 @@ import pdfplumber
 import re
 import gradio as gr
 from typing import List, Dict
-from concurrent.futures import ThreadPoolExecutor, as_completed
+from concurrent.futures import ThreadPoolExecutor
 import hashlib
-import asyncio
 
 # Persistent directories
 persistent_dir = "/data/hf_cache"
@@ -37,7 +36,7 @@ def extract_all_pages(file_path: str) -> str:
     except Exception:
         return ""
 
-async def convert_file_to_text(file_path: str, file_type: str) -> str:
+def convert_file_to_text(file_path: str, file_type: str) -> str:
     """Convert supported file types to text, caching results."""
     try:
         h = file_hash(file_path)
@@ -88,14 +87,13 @@ def parse_analysis_response(raw_response: str) -> Dict[str, List[str]]:
     
     return sections
 
-async def analyze_medical_records(extracted_text: str) -> str:
-    """Analyze medical records and stream structured response."""
+def analyze_medical_records(extracted_text: str) -> str:
+    """Analyze medical records and return structured response."""
     # Split text into chunks to handle large inputs
     chunk_size = 10000
     chunks = [extracted_text[i:i + chunk_size] for i in range(0, len(extracted_text), chunk_size)]
     
     # Placeholder for analysis (replace with model or rule-based logic)
-    # Simulate chunked analysis with sample response
     raw_response_template = """
     Missed Diagnoses:
     - Undiagnosed hypertension despite elevated BP readings.
@@ -121,27 +119,12 @@ async def analyze_medical_records(extracted_text: str) -> str:
     
     for chunk_idx, chunk in enumerate(chunks, 1):
         # Simulate analysis per chunk (replace with real logic)
-        raw_response = raw_response_template  # In real use, analyze chunk
-        
-        # Parse chunk response
+        raw_response = raw_response_template
         parsed = parse_analysis_response(raw_response)
         for section, items in parsed.items():
             all_sections[section].update(items)
-        
-        # Stream partial results
-        response = [f"### Clinical Oversight Analysis (Chunk {chunk_idx}/{len(chunks)})\n"]
-        has_findings = False
-        for section, items in all_sections.items():
-            response.append(f"#### {section}")
-            if items:
-                response.extend(sorted(items))
-                has_findings = True
-            else:
-                response.append("- None identified.")
-            response.append("")
-        yield "\n".join(response)
     
-    # Final response
+    # Format final response
     response = ["### Clinical Oversight Analysis\n"]
     has_findings = False
     for section, items in all_sections.items():
@@ -159,12 +142,12 @@ async def analyze_medical_records(extracted_text: str) -> str:
               "No significant oversights identified. Continue monitoring."
     response.append(summary)
     
-    yield "\n".join(response)
+    return "\n".join(response)
 
-async def create_ui():
+def create_ui():
     """Create Gradio UI for clinical oversight analysis."""
-    async def analyze(message: str, history: List[dict], files: List):
-        """Handle analysis and stream results to UI."""
+    def analyze(message: str, history: List[dict], files: List):
+        """Handle analysis and return results."""
         history.append({"role": "user", "content": message})
         history.append({"role": "assistant", "content": "⏳ Analyzing..."})
         yield history, None
@@ -172,24 +155,21 @@ async def create_ui():
         extracted_text = ""
         file_hash_value = ""
         if files:
-            tasks = [convert_file_to_text(f.name, f.name.split(".")[-1].lower()) for f in files]
-            results = await asyncio.gather(*tasks, return_exceptions=True)
-            extracted_text = "\n".join(sanitize_utf8(r) for r in results if isinstance(r, str))
-            file_hash_value = file_hash(files[0].name) if files else ""
+            with ThreadPoolExecutor(max_workers=4) as executor:
+                futures = [executor.submit(convert_file_to_text, f.name, f.name.split(".")[-1].lower()) for f in files]
+                results = [f.result() for f in futures]
+                extracted_text = "\n".join(sanitize_utf8(r) for r in results if r)
+                file_hash_value = file_hash(files[0].name) if files else ""
 
         history.pop()  # Remove "Analyzing..."
         report_path = os.path.join(report_dir, f"{file_hash_value}_report.txt") if file_hash_value else None
-        full_response = []
 
         try:
-            async for partial_response in analyze_medical_records(extracted_text):
-                full_response = partial_response.splitlines()
-                history.append({"role": "assistant", "content": partial_response})
-                yield history, None
-
+            response = analyze_medical_records(extracted_text)
+            history.append({"role": "assistant", "content": response})
             if report_path:
                 with open(report_path, "w", encoding="utf-8") as f:
-                    f.write("\n".join(full_response))
+                    f.write(response)
             yield history, report_path if report_path and os.path.exists(report_path) else None
         except Exception as e:
             history.append({"role": "assistant", "content": f"❌ Error: {str(e)}"})
@@ -210,8 +190,8 @@ async def create_ui():
 if __name__ == "__main__":
     print("🚀 Launching app...")
     try:
-        demo = asyncio.run(create_ui())
-        demo.queue(api_open=False).launch(
+        demo = create_ui()
+        demo.launch(
             server_name="0.0.0.0",
             server_port=7860,
             show_error=True,