Update app.py

app.py

@@ -55,7 +55,6 @@ TARGET_CHUNK_TOKENS = 1200
 PROMPT_RESERVE = 100
 MEDICAL_SECTION_HEADER = "=== MEDICAL SECTION ==="
 
-
 def log_system_usage(tag=""):
     try:
         cpu = psutil.cpu_percent(interval=1)
@@ -71,7 +70,6 @@ def log_system_usage(tag=""):
     except Exception as e:
         print(f"[{tag}] GPU/CPU monitor failed: {e}")
 
-
 def sanitize_utf8(text: str) -> str:
     return text.encode("utf-8", "ignore").decode("utf-8")
 
@@ -83,7 +81,6 @@ def count_tokens(text: str) -> int:
     encoding = tiktoken.get_encoding(TOKENIZER)
     return len(encoding.encode(text))
 
-
 def extract_all_pages_with_token_count(file_path: str) -> Tuple[str, int, int]:
     try:
         text_chunks = []
@@ -103,7 +100,6 @@ def extract_all_pages_with_token_count(file_path: str) -> Tuple[str, int, int]:
     except Exception as e:
         return f"PDF processing error: {str(e)}", 0, 0
 
-
 def convert_file_to_json(file_path: str, file_type: str) -> str:
     try:
         h = file_hash(file_path)
@@ -151,18 +147,18 @@ def convert_file_to_json(file_path: str, file_type: str) -> str:
     except Exception as e:
         return json.dumps({"error": f"Error processing {os.path.basename(file_path)}: {str(e)}"})
 
-
 def clean_response(text: str) -> str:
     text = sanitize_utf8(text)
     patterns = [
-        r"\[TOOL_CALLS\].*", r"\['get_[^\]]+\']\n?", r"\{'meta':\s*\{.*?\}\s*,\s*'results':\s*\[.*?\]\}\n?",
-        r"To analyze the medical records for clinical oversights.*?\n"  # remove generic prompt
+        r"\[TOOL_CALLS\].*",
+        r"\['get_[^\]]+\']\n?",
+        r"\{'meta':\s*\{.*?\}\s*,\s*'results':\s*\[.*?\]\}\n?",
+        r"To analyze the medical records for clinical oversights.*?\n"
     ]
     for pat in patterns:
         text = re.sub(pat, "", text, flags=re.DOTALL)
     return re.sub(r"\n{3,}", "\n\n", text).strip()
 
-
 def format_final_report(analysis_results: List[str], filename: str) -> str:
     report = [
         "COMPREHENSIVE CLINICAL OVERSIGHT ANALYSIS",
@@ -197,7 +193,6 @@ def format_final_report(analysis_results: List[str], filename: str) -> str:
     report.append("END OF REPORT")
     return "\n".join(report)
 
-
 def split_content_by_tokens(content: str, max_tokens: int) -> List[str]:
     paragraphs = re.split(r"\n\s*\n", content)
     chunks, current, curr_toks = [], [], 0
@@ -222,7 +217,6 @@ def split_content_by_tokens(content: str, max_tokens: int) -> List[str]:
         chunks.append("\n\n".join(current))
     return chunks
 
-
 def init_agent():
     print("🔁 Initializing model...")
     log_system_usage("Before Load")
@@ -240,12 +234,11 @@ def init_agent():
         seed=100,
         additional_default_tools=[]
     )
-    agent.init_model(max_model_len=MAX_MODEL_LEN)
+    agent.init_model()
     log_system_usage("After Load")
     print("✅ Agent Ready")
     return agent
 
-
 def analyze_complete_document(content: str, filename: str, agent: TxAgent, temperature: float = 0.3) -> str:
     base_prompt = (
         "Analyze for:\n1. Critical\n2. Missed DX\n3. Med issues\n4. Gaps\n5. Follow-up\n\nContent:\n"
@@ -279,7 +272,6 @@ def analyze_complete_document(content: str, filename: str, agent: TxAgent, tempe
             print(f"Error processing chunk {i}: {e}")
     return format_final_report(results, filename)
 
-
 def create_ui(agent):
     with gr.Blocks(title="Clinical Oversight Assistant") as demo:
         gr.Markdown("""
@@ -303,7 +295,6 @@ def create_ui(agent):
                 yield "", None, "⚠️ Please upload files.", None
                 return
             yield "", None, "⏳ Processing...", None
-            # convert files
             previews = []
             contents = []
             for f in files:
@@ -328,7 +319,12 @@ if __name__ == "__main__":
     try:
         import tiktoken
     except ImportError:
-        subprocess.run([sys.executable, "-m", "pip", "install", "tiktoken"] )
+        subprocess.run([sys.executable, "-m", "pip", "install", "tiktoken"])
     agent = init_agent()
     demo = create_ui(agent)
-    demo.queue(api_open=False, max_size=20).launch(server_name="0.0.0.0", server_port=7860, show_error=True, share=False)
+    demo.queue(api_open=False, max_size=20).launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        show_error=True,
+        share=False
+    )