Update app.py

app.py

@@ -4,26 +4,13 @@ import pandas as pd
 import pdfplumber
 import json
 import gradio as gr
-from typing import List, Tuple
+from typing import List
 from concurrent.futures import ThreadPoolExecutor, as_completed
 import hashlib
 import shutil
 import re
 import psutil
 import subprocess
-import logging
-from datetime import datetime
-
-# Configure logging
-logging.basicConfig(
-    level=logging.INFO,
-    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
-    handlers=[
-        logging.StreamHandler(),
-        logging.FileHandler('clinical_oversight.log')
-    ]
-)
-logger = logging.getLogger(__name__)
 
 # Persistent directory
 persistent_dir = "/data/hf_cache"
@@ -73,7 +60,6 @@ def extract_priority_pages(file_path: str, max_pages: int = 20) -> str:
                     text_chunks.append(f"=== Page {i} ===\n{page_text.strip()}")
         return "\n\n".join(text_chunks)
     except Exception as e:
-        logger.error(f"Error extracting pages from PDF: {str(e)}")
         return f"PDF processing error: {str(e)}"
 
 def convert_file_to_json(file_path: str, file_type: str) -> str:
@@ -101,31 +87,29 @@ def convert_file_to_json(file_path: str, file_type: str) -> str:
             result = json.dumps({"filename": os.path.basename(file_path), "rows": content})
         else:
             result = json.dumps({"error": f"Unsupported file type: {file_type}"})
-        
         with open(cache_path, "w", encoding="utf-8") as f:
             f.write(result)
         return result
     except Exception as e:
-        logger.error(f"Error converting {file_type} file to JSON: {str(e)}")
         return json.dumps({"error": f"Error processing {os.path.basename(file_path)}: {str(e)}"})
 
 def log_system_usage(tag=""):
     try:
         cpu = psutil.cpu_percent(interval=1)
         mem = psutil.virtual_memory()
-        logger.info(f"[{tag}] CPU: {cpu}% | RAM: {mem.used // (1024**2)}MB / {mem.total // (1024**2)}MB")
+        print(f"[{tag}] CPU: {cpu}% | RAM: {mem.used // (1024**2)}MB / {mem.total // (1024**2)}MB")
         result = subprocess.run(
             ["nvidia-smi", "--query-gpu=memory.used,memory.total,utilization.gpu", "--format=csv,nounits,noheader"],
             capture_output=True, text=True
         )
         if result.returncode == 0:
             used, total, util = result.stdout.strip().split(", ")
-            logger.info(f"[{tag}] GPU: {used}MB / {total}MB | Utilization: {util}%")
+            print(f"[{tag}] GPU: {used}MB / {total}MB | Utilization: {util}%")
     except Exception as e:
-        logger.error(f"[{tag}] GPU/CPU monitor failed: {e}")
+        print(f"[{tag}] GPU/CPU monitor failed: {e}")
 
 def init_agent():
-    logger.info("🔁 Initializing model...")
+    print("🔁 Initializing model...")
     log_system_usage("Before Load")
     default_tool_path = os.path.abspath("data/new_tool.json")
     target_tool_path = os.path.join(tool_cache_dir, "new_tool.json")
@@ -144,63 +128,33 @@ def init_agent():
     )
     agent.init_model()
     log_system_usage("After Load")
-    logger.info("✅ Agent Ready")
+    print("✅ Agent Ready")
     return agent
 
-def format_response_for_ui(response: str) -> str:
-    """Formats the raw response for clean display in the UI"""
-    # Remove any tool call metadata
-    cleaned = response.split("[TOOL_CALLS]")[0].strip()
-    
-    # If we have a structured response, format it nicely
-    if "Potential missed diagnoses" in cleaned or "Flagged medication conflicts" in cleaned:
-        # Add markdown formatting for better readability
-        formatted = []
-        for line in cleaned.split("\n"):
-            if line.startswith("Potential missed diagnoses"):
-                formatted.append(f"### 🔍 Potential Missed Diagnoses")
-            elif line.startswith("Flagged medication conflicts"):
-                formatted.append(f"\n### ⚠️ Flagged Medication Conflicts")
-            elif line.startswith("Incomplete assessments"):
-                formatted.append(f"\n### 📋 Incomplete Assessments")
-            elif line.startswith("Highlighted abnormal results"):
-                formatted.append(f"\n### ❗ Abnormal Results Needing Follow-up")
-            else:
-                formatted.append(line)
-        return "\n".join(formatted)
-    return cleaned
-
-def analyze(message: str, history: List[Tuple[str, str]], files: list):
-    start_time = datetime.now()
-    logger.info(f"Starting analysis for message: {message[:100]}...")
-    if files:
-        logger.info(f"Processing {len(files)} uploaded files")
-
-    # Initialize chat history in the correct format if empty
-    if history is None:
-        history = []
-    
-    # Add user message to history
-    history.append([message, None])
-    yield history, None
-
-    extracted = ""
-    file_hash_value = ""
-    if files:
-        try:
-            with ThreadPoolExecutor(max_workers=4) as executor:
-                futures = [executor.submit(convert_file_to_json, f.name, f.name.split(".")[-1].lower()) for f in files]
-                results = [sanitize_utf8(f.result()) for f in as_completed(futures)]
-                extracted = "\n".join(results)
-                file_hash_value = file_hash(files[0].name)
-                logger.info(f"Processed {len(files)} files, extracted {len(extracted)} characters")
-        except Exception as e:
-            logger.error(f"Error processing files: {str(e)}")
-            history[-1][1] = f"❌ Error processing files: {str(e)}"
+def create_ui(agent):
+    with gr.Blocks(theme=gr.themes.Soft()) as demo:
+        gr.Markdown("<h1 style='text-align: center;'>🩺 Clinical Oversight Assistant</h1>")
+        chatbot = gr.Chatbot(label="Analysis", height=600, type="messages")
+        file_upload = gr.File(file_types=[".pdf", ".csv", ".xls", ".xlsx"], file_count="multiple")
+        msg_input = gr.Textbox(placeholder="Ask about potential oversights...", show_label=False)
+        send_btn = gr.Button("Analyze", variant="primary")
+        download_output = gr.File(label="Download Full Report")
+
+        def analyze(message: str, history: list, files: list):
+            history = history + [{"role": "user", "content": message},
+                                 {"role": "assistant", "content": "⏳ Analyzing records for potential oversights..."}]
             yield history, None
-            return
 
-    prompt = f"""Review these medical records and identify EXACTLY what might have been missed:
+            extracted = ""
+            file_hash_value = ""
+            if files:
+                with ThreadPoolExecutor(max_workers=4) as executor:
+                    futures = [executor.submit(convert_file_to_json, f.name, f.name.split(".")[-1].lower()) for f in files]
+                    results = [sanitize_utf8(f.result()) for f in as_completed(futures)]
+                    extracted = "\n".join(results)
+                    file_hash_value = file_hash(files[0].name)
+
+            prompt = f"""Review these medical records and identify EXACTLY what might have been missed:
 1. List potential missed diagnoses
 2. Flag any medication conflicts
 3. Note incomplete assessments
@@ -211,145 +165,54 @@ Medical Records:
 
 ### Potential Oversights:
 """
-    logger.info(f"Generated prompt with {len(prompt)} characters")
-
-    response_chunks = []
-    try:
-        logger.info("Starting model inference...")
-        for chunk in agent.run_gradio_chat(
-            message=prompt,
-            history=[],
-            temperature=0.2,
-            max_new_tokens=1024,
-            max_token=4096,
-            call_agent=False,
-            conversation=[]
-        ):
-            if not chunk:
-                continue
-            if isinstance(chunk, str):
-                response_chunks.append(chunk)
-            elif isinstance(chunk, list):
-                response_chunks.extend([c.content for c in chunk if hasattr(c, 'content')])
-            
-            partial_response = "".join(response_chunks)
-            formatted_partial = format_response_for_ui(partial_response)
-            
-            if formatted_partial:
-                history[-1][1] = formatted_partial
-                yield history, None
-
-        full_response = "".join(response_chunks)
-        logger.info(f"Full model response received: {full_response[:500]}...")
-        
-        final_output = format_response_for_ui(full_response)
-        if not final_output or len(final_output) < 20:  # Very short response
-            final_output = "No clear oversights identified. Recommend comprehensive review."
-            logger.info("No significant findings detected in analysis")
-        
-        history[-1][1] = final_output
-
-        # Save report
-        report_path = None
-        if file_hash_value:
-            report_path = os.path.join(report_dir, f"{file_hash_value}_report.txt")
+            response_chunks = []
             try:
-                with open(report_path, "w", encoding="utf-8") as f:
-                    f.write(final_output)
-                logger.info(f"Saved report to {report_path}")
+                for chunk in agent.run_gradio_chat(
+                    message=prompt,
+                    history=[],
+                    temperature=0.2,
+                    max_new_tokens=1024,
+                    max_token=4096,
+                    call_agent=False,
+                    conversation=[]
+                ):
+                    if not chunk:
+                        continue
+                    if isinstance(chunk, str):
+                        response_chunks.append(chunk)
+                    elif isinstance(chunk, list):
+                        response_chunks.extend([c.content for c in chunk if hasattr(c, 'content')])
+                    partial_response = "".join(response_chunks)
+                    cleaned_partial = partial_response.split("[TOOL_CALLS]")[0].strip()
+                    if cleaned_partial:
+                        history[-1] = {"role": "assistant", "content": cleaned_partial}
+                        yield history, None
             except Exception as e:
-                logger.error(f"Error saving report: {str(e)}")
-
-        elapsed = (datetime.now() - start_time).total_seconds()
-        logger.info(f"Analysis completed in {elapsed:.2f} seconds")
-        yield history, report_path if report_path and os.path.exists(report_path) else None
-
-    except Exception as e:
-        logger.error(f"Error during analysis: {str(e)}", exc_info=True)
-        history[-1][1] = f"❌ Error during analysis: {str(e)}"
-        yield history, None
-
-def create_ui(agent):
-    with gr.Blocks(theme=gr.themes.Soft(), title="Clinical Oversight Assistant") as demo:
-        gr.Markdown("<h1 style='text-align: center;'>🩺 Clinical Oversight Assistant</h1>")
-        gr.Markdown("""
-        <div style='text-align: center; margin-bottom: 20px;'>
-            Upload medical records and receive analysis of potential oversights, including:<br>
-            - Missed diagnoses - Medication conflicts - Incomplete assessments - Abnormal results needing follow-up
-        </div>
-        """)
-        
-        with gr.Row():
-            with gr.Column(scale=2):
-                file_upload = gr.File(
-                    label="Upload Medical Records",
-                    file_types=[".pdf", ".csv", ".xls", ".xlsx"],
-                    file_count="multiple",
-                    interactive=True
-                )
-                msg_input = gr.Textbox(
-                    placeholder="Ask about potential oversights...",
-                    show_label=False,
-                    lines=3,
-                    max_lines=5
-                )
-                send_btn = gr.Button("Analyze", variant="primary")
-            
-            with gr.Column(scale=3):
-                chatbot = gr.Chatbot(
-                    label="Analysis Results",
-                    height=600,
-                    bubble_full_width=False,
-                    show_copy_button=True
-                )
-                download_output = gr.File(
-                    label="Download Full Report",
-                    interactive=False
-                )
-
-        # Examples for quick testing
-        examples = gr.Examples(
-            examples=[
-                ["Are there any potential missed diagnoses in these records?"],
-                ["What medication conflicts should I be aware of?"],
-                ["Are there any incomplete assessments in this case?"]
-            ],
-            inputs=[msg_input],
-            label="Example Questions"
-        )
+                history[-1] = {"role": "assistant", "content": f"❌ Error: {str(e)}"}
+                yield history, None
+                return
 
-        send_btn.click(
-            analyze,
-            inputs=[msg_input, gr.State([]), file_upload],
-            outputs=[chatbot, download_output]
-        )
-        msg_input.submit(
-            analyze,
-            inputs=[msg_input, gr.State([]), file_upload],
-            outputs=[chatbot, download_output]
-        )
+            full_response = "".join(response_chunks)
+            final_output = full_response.split("[TOOL_CALLS]")[0].strip()
+            if not final_output:
+                final_output = "No clear oversights identified. Recommend comprehensive review."
+            history[-1] = {"role": "assistant", "content": final_output}
 
-        # Add some footer text
-        gr.Markdown("""
-        <div style='text-align: center; margin-top: 20px; color: #666; font-size: 0.9em;'>
-            Note: This tool provides preliminary analysis only. Always verify findings with complete clinical evaluation.
-        </div>
-        """)
+            report_path = os.path.join(report_dir, f"{file_hash_value}_report.txt") if file_hash_value else None
+            yield history, report_path if report_path and os.path.exists(report_path) else None
 
+        send_btn.click(analyze, inputs=[msg_input, gr.State([]), file_upload], outputs=[chatbot, download_output])
+        msg_input.submit(analyze, inputs=[msg_input, gr.State([]), file_upload], outputs=[chatbot, download_output])
     return demo
 
 if __name__ == "__main__":
-    logger.info("🚀 Launching Clinical Oversight Assistant...")
-    try:
-        agent = init_agent()
-        demo = create_ui(agent)
-        demo.queue(api_open=False).launch(
-            server_name="0.0.0.0",
-            server_port=7860,
-            show_error=True,
-            allowed_paths=[report_dir],
-            share=False
-        )
-    except Exception as e:
-        logger.error(f"Failed to launch application: {str(e)}", exc_info=True)
-        raise
+    print("🚀 Launching app...")
+    agent = init_agent()
+    demo = create_ui(agent)
+    demo.queue(api_open=False).launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        show_error=True,
+        allowed_paths=[report_dir],
+        share=False
+    )