Update app.py

app.py

@@ -4,13 +4,27 @@ import pandas as pd
 import pdfplumber
 import json
 import gradio as gr
-from typing import List
+from typing import List, Dict, Any
 from concurrent.futures import ThreadPoolExecutor, as_completed
 import hashlib
 import shutil
 import re
 import psutil
 import subprocess
+import logging
+import traceback
+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"
@@ -41,172 +55,429 @@ MEDICAL_KEYWORDS = {'diagnosis', 'assessment', 'plan', 'results', 'medications',
                     'allergies', 'summary', 'impression', 'findings', 'recommendations'}
 
 def sanitize_utf8(text: str) -> str:
-    return text.encode("utf-8", "ignore").decode("utf-8")
+    """Ensure text is UTF-8 encoded and clean."""
+    try:
+        return text.encode("utf-8", "ignore").decode("utf-8")
+    except Exception as e:
+        logger.error(f"UTF-8 sanitization failed: {str(e)}")
+        return ""
 
 def file_hash(path: str) -> str:
-    with open(path, "rb") as f:
-        return hashlib.md5(f.read()).hexdigest()
+    """Generate MD5 hash of file content."""
+    try:
+        with open(path, "rb") as f:
+            return hashlib.md5(f.read()).hexdigest()
+    except Exception as e:
+        logger.error(f"File hash generation failed for {path}: {str(e)}")
+        return ""
 
 def extract_priority_pages(file_path: str, max_pages: int = 20) -> str:
+    """Extract pages from PDF with priority given to pages containing medical keywords."""
     try:
         text_chunks = []
+        logger.info(f"Extracting pages from {file_path}")
+        
         with pdfplumber.open(file_path) as pdf:
+            # Always extract first 3 pages
             for i, page in enumerate(pdf.pages[:3]):
-                text = page.extract_text() or ""
-                text_chunks.append(f"=== Page {i+1} ===\n{text.strip()}")
+                try:
+                    text = page.extract_text() or ""
+                    text_chunks.append(f"=== Page {i+1} ===\n{text.strip()}")
+                except Exception as page_error:
+                    logger.warning(f"Error processing page {i+1}: {str(page_error)}")
+                    text_chunks.append(f"=== Page {i+1} ===\n[Error extracting content]")
+            
+            # Extract remaining pages that contain medical keywords
             for i, page in enumerate(pdf.pages[3:max_pages], start=4):
-                page_text = page.extract_text() or ""
-                if any(re.search(rf'\b{kw}\b', page_text.lower()) for kw in MEDICAL_KEYWORDS):
-                    text_chunks.append(f"=== Page {i} ===\n{page_text.strip()}")
+                try:
+                    page_text = page.extract_text() or ""
+                    if any(re.search(rf'\b{kw}\b', page_text.lower()) for kw in MEDICAL_KEYWORDS):
+                        text_chunks.append(f"=== Page {i} ===\n{page_text.strip()}")
+                except Exception as page_error:
+                    logger.warning(f"Error processing page {i}: {str(page_error)}")
+        
         return "\n\n".join(text_chunks)
     except Exception as e:
+        logger.error(f"PDF processing error for {file_path}: {str(e)}")
         return f"PDF processing error: {str(e)}"
 
 def convert_file_to_json(file_path: str, file_type: str) -> str:
+    """Convert different file types to JSON format with caching."""
     try:
         h = file_hash(file_path)
+        if not h:
+            return json.dumps({"error": "Could not generate file hash"})
+            
         cache_path = os.path.join(file_cache_dir, f"{h}.json")
+        
+        # Check cache first
         if os.path.exists(cache_path):
-            with open(cache_path, "r", encoding="utf-8") as f:
-                return f.read()
-
-        if file_type == "pdf":
-            text = extract_priority_pages(file_path)
-            result = json.dumps({"filename": os.path.basename(file_path), "content": text, "status": "initial"})
-        elif file_type == "csv":
-            df = pd.read_csv(file_path, encoding_errors="replace", header=None, dtype=str,
-                             skip_blank_lines=False, on_bad_lines="skip")
-            content = df.fillna("").astype(str).values.tolist()
-            result = json.dumps({"filename": os.path.basename(file_path), "rows": content})
-        elif file_type in ["xls", "xlsx"]:
             try:
-                df = pd.read_excel(file_path, engine="openpyxl", header=None, dtype=str)
-            except Exception:
-                df = pd.read_excel(file_path, engine="xlrd", header=None, dtype=str)
-            content = df.fillna("").astype(str).values.tolist()
-            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
+                with open(cache_path, "r", encoding="utf-8") as f:
+                    return f.read()
+            except Exception as cache_error:
+                logger.error(f"Cache read error for {file_path}: {str(cache_error)}")
+        
+        result = {}
+        try:
+            if file_type == "pdf":
+                text = extract_priority_pages(file_path)
+                result = {
+                    "filename": os.path.basename(file_path),
+                    "content": text,
+                    "status": "initial",
+                    "file_type": "pdf"
+                }
+            elif file_type == "csv":
+                df = pd.read_csv(
+                    file_path,
+                    encoding_errors="replace",
+                    header=None,
+                    dtype=str,
+                    skip_blank_lines=False,
+                    on_bad_lines="skip"
+                )
+                content = df.fillna("").astype(str).values.tolist()
+                result = {
+                    "filename": os.path.basename(file_path),
+                    "rows": content,
+                    "file_type": "csv"
+                }
+            elif file_type in ["xls", "xlsx"]:
+                try:
+                    df = pd.read_excel(file_path, engine="openpyxl", header=None, dtype=str)
+                except Exception:
+                    try:
+                        df = pd.read_excel(file_path, engine="xlrd", header=None, dtype=str)
+                    except Exception as excel_error:
+                        logger.error(f"Excel read error for {file_path}: {str(excel_error)}")
+                        raise
+                content = df.fillna("").astype(str).values.tolist()
+                result = {
+                    "filename": os.path.basename(file_path),
+                    "rows": content,
+                    "file_type": "excel"
+                }
+            else:
+                result = {"error": f"Unsupported file type: {file_type}"}
+            
+            json_result = json.dumps(result)
+            
+            # Save to cache
+            try:
+                with open(cache_path, "w", encoding="utf-8") as f:
+                    f.write(json_result)
+            except Exception as cache_write_error:
+                logger.error(f"Cache write error for {file_path}: {str(cache_write_error)}")
+            
+            return json_result
+        except Exception as processing_error:
+            logger.error(f"Error processing {file_path}: {str(processing_error)}")
+            return json.dumps({"error": f"Error processing {os.path.basename(file_path)}: {str(processing_error)}"})
     except Exception as e:
-        return json.dumps({"error": f"Error processing {os.path.basename(file_path)}: {str(e)}"})
+        logger.error(f"Unexpected error in convert_file_to_json: {str(e)}")
+        return json.dumps({"error": f"Unexpected error processing file: {str(e)}"})
 
 def log_system_usage(tag=""):
+    """Log system resource usage including CPU, RAM, and GPU."""
     try:
         cpu = psutil.cpu_percent(interval=1)
         mem = psutil.virtual_memory()
-        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(", ")
-            print(f"[{tag}] GPU: {used}MB / {total}MB | Utilization: {util}%")
+        logger.info(f"[{tag}] CPU: {cpu}% | RAM: {mem.used // (1024**2)}MB / {mem.total // (1024**2)}MB")
+        
+        try:
+            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}%")
+        except Exception as gpu_error:
+            logger.warning(f"[{tag}] GPU monitor failed: {gpu_error}")
     except Exception as e:
-        print(f"[{tag}] GPU/CPU monitor failed: {e}")
+        logger.error(f"System usage logging failed: {str(e)}")
 
 def init_agent():
-    print("🔁 Initializing model...")
+    """Initialize the TxAgent with proper configuration."""
+    logger.info("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")
-    if not os.path.exists(target_tool_path):
-        shutil.copy(default_tool_path, target_tool_path)
-
-    agent = TxAgent(
-        model_name="mims-harvard/TxAgent-T1-Llama-3.1-8B",
-        rag_model_name="mims-harvard/ToolRAG-T1-GTE-Qwen2-1.5B",
-        tool_files_dict={"new_tool": target_tool_path},
-        force_finish=True,
-        enable_checker=True,
-        step_rag_num=8,
-        seed=100,
-        additional_default_tools=[],
-    )
-    agent.init_model()
-    log_system_usage("After Load")
-    print("✅ Agent Ready")
-    return agent
+    
+    try:
+        if not os.path.exists(target_tool_path):
+            shutil.copy(default_tool_path, target_tool_path)
+            logger.info("Copied default tool configuration")
+    except Exception as e:
+        logger.error(f"Tool configuration copy failed: {str(e)}")
+        raise
 
-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}]
-            history.append({"role": "assistant", "content": "⏳ Analyzing records for potential oversights..."})
-            yield history, None
-
-            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:
+    try:
+        agent = TxAgent(
+            model_name="mims-harvard/TxAgent-T1-Llama-3.1-8B",
+            rag_model_name="mims-harvard/ToolRAG-T1-GTE-Qwen2-1.5B",
+            tool_files_dict={"new_tool": target_tool_path},
+            force_finish=True,
+            enable_checker=True,
+            step_rag_num=8,
+            seed=100,
+            additional_default_tools=[],
+        )
+        agent.init_model()
+        log_system_usage("After Load")
+        logger.info("Agent initialization successful")
+        return agent
+    except Exception as e:
+        logger.error(f"Agent initialization failed: {str(e)}")
+        raise
+
+def save_report(content: str, file_hash_value: str = "") -> str:
+    """Save analysis report to file and return path."""
+    try:
+        if not file_hash_value:
+            file_hash_value = hashlib.md5(content.encode()).hexdigest()
+        
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        report_filename = f"report_{timestamp}_{file_hash_value[:8]}.txt"
+        report_path = os.path.join(report_dir, report_filename)
+        
+        with open(report_path, "w", encoding="utf-8") as f:
+            f.write(content)
+        
+        logger.info(f"Report saved to {report_path}")
+        return report_path
+    except Exception as e:
+        logger.error(f"Failed to save report: {str(e)}")
+        return ""
+
+def clean_response(content: str) -> str:
+    """Clean up model response by removing tool call artifacts."""
+    if not content:
+        return "⚠️ No content generated."
+    
+    try:
+        # Remove tool call artifacts
+        cleaned = re.sub(r"\[TOOL_CALLS\].*?(?=(\[|\Z))", "", content, flags=re.DOTALL).strip()
+        # Remove excessive whitespace
+        cleaned = re.sub(r"\n{3,}", "\n\n", cleaned)
+        return cleaned or "⚠️ Empty response after cleaning."
+    except Exception as e:
+        logger.error(f"Response cleaning failed: {str(e)}")
+        return content
+
+def process_model_response(chunk: Any, history: List[Dict[str, str]]) -> List[Dict[str, str]]:
+    """Process model response chunk and update chat history."""
+    try:
+        if chunk is None:
+            return history
+            
+        if isinstance(chunk, list) and all(hasattr(m, 'role') and hasattr(m, 'content') for m in chunk):
+            for m in chunk:
+                cleaned_content = clean_response(m.content)
+                history.append({"role": m.role, "content": cleaned_content})
+        elif isinstance(chunk, str):
+            cleaned_chunk = clean_response(chunk)
+            if history and history[-1]["role"] == "assistant":
+                history[-1]["content"] += cleaned_chunk
+            else:
+                history.append({"role": "assistant", "content": cleaned_chunk})
+        else:
+            logger.warning(f"Unexpected response type: {type(chunk)}")
+            
+        return history
+    except Exception as e:
+        logger.error(f"Error processing model response: {str(e)}")
+        history.append({"role": "assistant", "content": f"⚠️ Error processing response: {str(e)}"})
+        return history
+
+def analyze(message: str, history: list, files: list):
+    """Main analysis function that processes files and generates responses."""
+    try:
+        # Initial response
+        new_history = history.copy()
+        new_history.append({"role": "user", "content": message})
+        new_history.append({"role": "assistant", "content": "⏳ Analyzing records for potential oversights..."})
+        yield new_history, None
+        
+        # Process files
+        extracted = ""
+        file_hash_value = ""
+        if files:
+            logger.info(f"Processing {len(files)} files...")
+            with ThreadPoolExecutor(max_workers=4) as executor:
+                futures = []
+                for f in files:
+                    try:
+                        file_type = f.name.split(".")[-1].lower()
+                        futures.append(executor.submit(convert_file_to_json, f.name, file_type))
+                    except Exception as e:
+                        logger.error(f"Error submitting file {f.name} for processing: {str(e)}")
+                        new_history.append({"role": "system", "content": f"⚠️ Error processing {f.name}: {str(e)}"})
+                
+                results = []
+                for f in as_completed(futures):
+                    try:
+                        results.append(sanitize_utf8(f.result()))
+                    except Exception as e:
+                        logger.error(f"Error getting file processing result: {str(e)}")
+                        results.append(json.dumps({"error": "File processing failed"}))
+                
+                extracted = "\n".join(results)
+                try:
+                    file_hash_value = file_hash(files[0].name) if files else ""
+                except Exception as e:
+                    logger.error(f"Error generating file hash: {str(e)}")
+                    file_hash_value = ""
+        
+        # Prepare prompt
+        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
 4. Highlight abnormal results needing follow-up
+
 Medical Records:
 {extracted[:12000]}
+
 ### Potential Oversights:
 """
-
+        logger.info(f"Prompt length: {len(prompt)} characters")
+        
+        # Initialize agent response
+        agent = init_agent()
+        response_content = ""
+        report_path = ""
+        
+        # Process agent response
+        for chunk in agent.run_gradio_chat(
+            message=prompt,
+            history=[],
+            temperature=0.2,
+            max_new_tokens=2048,
+            max_token=4096,
+            call_agent=False,
+            conversation=[],
+        ):
             try:
-                for chunk in agent.run_gradio_chat(
-                    message=prompt,
-                    history=[],
-                    temperature=0.2,
-                    max_new_tokens=2048,
-                    max_token=4096,
-                    call_agent=False,
-                    conversation=[],
-                ):
-                    if chunk is None:
-                        continue
-
-                    if isinstance(chunk, list) and all(hasattr(m, 'role') and hasattr(m, 'content') for m in chunk):
-                        for m in chunk:
-                            cleaned_content = re.sub(r"\[TOOL_CALLS\].*?(?=(\[|\Z))", "", m.content, flags=re.DOTALL).strip()
-                            history.append({"role": m.role, "content": cleaned_content or "⚠️ No answer returned."})
-                            yield history, None
-                    elif isinstance(chunk, str):
-                        cleaned_chunk = re.sub(r"\[TOOL_CALLS\].*?(?=(\[|\Z))", "", chunk, flags=re.DOTALL).strip()
-                        history[-1]["content"] += cleaned_chunk or "⚠️ No content generated."
-                        yield history, None
-
-                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
-
-            except Exception as e:
-                print("🚨 ERROR:", e)
-                history.append({"role": "assistant", "content": f"❌ Error occurred: {str(e)}"})
-                yield history, 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])
+                new_history = process_model_response(chunk, new_history)
+                if isinstance(chunk, str):
+                    response_content += clean_response(chunk)
+                
+                yield new_history, None
+            except Exception as chunk_error:
+                logger.error(f"Error processing chunk: {str(chunk_error)}")
+                new_history.append({"role": "assistant", "content": f"⚠️ Error processing response chunk: {str(chunk_error)}"})
+                yield new_history, None
+        
+        # Save final report
+        if response_content:
+            try:
+                report_path = save_report(response_content, file_hash_value)
+            except Exception as report_error:
+                logger.error(f"Error saving report: {str(report_error)}")
+                new_history.append({"role": "system", "content": "⚠️ Failed to save full report"})
+        
+        yield new_history, report_path if report_path and os.path.exists(report_path) else None
+        
+    except Exception as e:
+        logger.error(f"Analysis error: {str(e)}\n{traceback.format_exc()}")
+        error_history = history.copy()
+        error_history.append({"role": "assistant", "content": f"❌ Critical error occurred: {str(e)}"})
+        yield error_history, None
+
+def create_ui(agent):
+    """Create Gradio UI interface."""
+    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 ask about potential oversights or missed diagnoses.
+        </div>
+        """)
+        
+        with gr.Row():
+            with gr.Column(scale=2):
+                chatbot = gr.Chatbot(
+                    label="Analysis Conversation",
+                    height=600,
+                    bubble_full_width=False,
+                    show_copy_button=True
+                )
+                msg_input = gr.Textbox(
+                    placeholder="Ask about potential oversights...",
+                    show_label=False,
+                    container=False
+                )
+                with gr.Row():
+                    send_btn = gr.Button("Analyze", variant="primary")
+                    clear_btn = gr.Button("Clear")
+                
+            with gr.Column(scale=1):
+                file_upload = gr.File(
+                    file_types=[".pdf", ".csv", ".xls", ".xlsx"],
+                    file_count="multiple",
+                    label="Upload Medical Records"
+                )
+                download_output = gr.File(
+                    label="Download Full Report",
+                    interactive=False
+                )
+                gr.Markdown("""
+                <div style='margin-top: 20px; font-size: 0.9em; color: #666;'>
+                    <b>Note:</b> The system analyzes PDFs, CSVs, and Excel files for potential clinical oversights.
+                </div>
+                """)
+        
+        # Event handlers
+        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]
+        )
+        
+        clear_btn.click(
+            lambda: ([], None),
+            inputs=[],
+            outputs=[chatbot, download_output]
+        )
+        
+        # Add some examples
+        gr.Examples(
+            examples=[
+                ["What potential diagnoses might have been missed in these records?"],
+                ["Are there any medication conflicts I should be aware of?"],
+                ["What abnormal results need follow-up in these reports?"]
+            ],
+            inputs=msg_input,
+            label="Example Questions"
+        )
+    
     return demo
 
 if __name__ == "__main__":
-    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
-    )
+    try:
+        logger.info("🚀 Launching Clinical Oversight Assistant...")
+        agent = init_agent()
+        demo = create_ui(agent)
+        
+        demo.queue(
+            api_open=False,
+            concurrency_count=2
+        ).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"Application failed to start: {str(e)}\n{traceback.format_exc()}")
+        raise