Update app.py

app.py

@@ -4,27 +4,13 @@ import pandas as pd
 import pdfplumber
 import json
 import gradio as gr
-from typing import List, Dict, Any
+from typing import List
 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"
@@ -55,429 +41,169 @@ MEDICAL_KEYWORDS = {'diagnosis', 'assessment', 'plan', 'results', 'medications',
                     'allergies', 'summary', 'impression', 'findings', 'recommendations'}
 
 def sanitize_utf8(text: str) -> str:
-    """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 ""
+    return text.encode("utf-8", "ignore").decode("utf-8")
 
 def file_hash(path: str) -> str:
-    """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 ""
+    with open(path, "rb") as f:
+        return hashlib.md5(f.read()).hexdigest()
 
 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]):
-                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
+                text = page.extract_text() or ""
+                text_chunks.append(f"=== Page {i+1} ===\n{text.strip()}")
             for i, page in enumerate(pdf.pages[3:max_pages], start=4):
-                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)}")
-        
+                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()}")
         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:
-                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)}"})
+                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
     except Exception as e:
-        logger.error(f"Unexpected error in convert_file_to_json: {str(e)}")
-        return json.dumps({"error": f"Unexpected error processing file: {str(e)}"})
+        return json.dumps({"error": f"Error processing {os.path.basename(file_path)}: {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()
-        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}")
+        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}%")
     except Exception as e:
-        logger.error(f"System usage logging failed: {str(e)}")
+        print(f"[{tag}] GPU/CPU monitor failed: {e}")
 
 def init_agent():
-    """Initialize the TxAgent with proper configuration."""
-    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")
-    
-    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
-
-    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
+    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
 
-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:
+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:
 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:
-                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"
-        )
-    
+            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):
+                        for m in chunk:
+                            history.append({"role": m.role, "content": m.content})
+                            yield history, None
+                    elif isinstance(chunk, str):
+                        history[-1]["content"] += chunk
+                        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])
     return demo
 
 if __name__ == "__main__":
-    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
+    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
+    )