app.py

import sys
import os
import pandas as pd
import pdfplumber
import json
import gradio as gr
from typing import List, Optional
from concurrent.futures import ThreadPoolExecutor, as_completed
import hashlib
import shutil
import time
from functools import lru_cache
from threading import Thread
import re

# Environment setup
current_dir = os.path.dirname(os.path.abspath(__file__))
src_path = os.path.abspath(os.path.join(current_dir, "src"))
sys.path.insert(0, src_path)

# Cache directories
base_dir = "/data"
os.makedirs(base_dir, exist_ok=True)
model_cache_dir = os.path.join(base_dir, "txagent_models")
tool_cache_dir = os.path.join(base_dir, "tool_cache")
file_cache_dir = os.path.join(base_dir, "cache")

os.makedirs(model_cache_dir, exist_ok=True)
os.makedirs(tool_cache_dir, exist_ok=True)
os.makedirs(file_cache_dir, exist_ok=True)

os.environ.update({
    "TRANSFORMERS_CACHE": model_cache_dir,
    "HF_HOME": model_cache_dir,
    "TOKENIZERS_PARALLELISM": "false",
    "CUDA_LAUNCH_BLOCKING": "1"
})

from txagent.txagent import TxAgent

# Medical keywords for priority detection
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")

def file_hash(path: str) -> str:
    with open(path, "rb") as f:
        return hashlib.md5(f.read()).hexdigest()

def extract_priority_pages(file_path: str, max_pages: int = 20) -> str:
    """Fast extraction of first pages and medically relevant sections"""
    try:
        text_chunks = []
        with pdfplumber.open(file_path) as pdf:
            # Always process first 3 pages
            for i, page in enumerate(pdf.pages[:3]):
                text_chunks.append(f"=== Page {i+1} ===\n{(page.extract_text() or '').strip()}")
            
            # Scan subsequent pages for 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()}")
        
        return "\n\n".join(text_chunks)
    except Exception as e:
        return f"PDF processing error: {str(e)}"

def convert_file_to_json(file_path: str, file_type: str) -> str:
    """Optimized file conversion with medical focus"""
    try:
        h = file_hash(file_path)
        cache_path = os.path.join(file_cache_dir, f"{h}.json")
        
        if os.path.exists(cache_path):
            return open(cache_path, "r", encoding="utf-8").read()

        if file_type == "pdf":
            # Fast initial processing
            text = extract_priority_pages(file_path)
            result = json.dumps({
                "filename": os.path.basename(file_path),
                "content": text,
                "status": "initial"
            })
            
            # Start background full processing
            Thread(target=full_pdf_processing, args=(file_path, h)).start()
            
        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:
                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:
            return 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:
        return json.dumps({"error": f"Error processing {os.path.basename(file_path)}: {str(e)}"})

def full_pdf_processing(file_path: str, file_hash: str):
    """Background full PDF processing"""
    try:
        cache_path = os.path.join(file_cache_dir, f"{file_hash}_full.json")
        if os.path.exists(cache_path):
            return

        with pdfplumber.open(file_path) as pdf:
            full_text = "\n".join([f"=== Page {i+1} ===\n{(page.extract_text() or '').strip()}" 
                                 for i, page in enumerate(pdf.pages)])
            
        result = json.dumps({
            "filename": os.path.basename(file_path),
            "content": full_text,
            "status": "complete"
        })
        
        with open(cache_path, "w", encoding="utf-8") as f:
            f.write(result)
    except Exception as e:
        print(f"Background processing failed: {str(e)}")

def init_agent():
    """Initialize TxAgent with medical analysis focus"""
    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()
    return agent

def create_ui(agent: TxAgent):
    with gr.Blocks(theme=gr.themes.Soft()) as demo:
        gr.Markdown("<h1 style='text-align: center;'>🩺 Clinical Oversight Assistant</h1>")
        gr.Markdown("<h3 style='text-align: center;'>Identify potential oversights in patient care</h3>")

        chatbot = gr.Chatbot(label="Analysis", height=600)
        file_upload = gr.File(
            label="Upload Medical Records",
            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")
        conversation_state = gr.State([])

        def analyze_potential_oversights(message: str, history: list, conversation: list, files: list):
            start_time = time.time()
            try:
                history.append((message, "Analyzing records for potential oversights..."))
                yield history
                
                # Process files
                extracted_data = ""
                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 if hasattr(f, 'name')]
                        extracted_data = "\n".join([sanitize_utf8(f.result()) for f in as_completed(futures)])

                # Medical oversight analysis prompt
                analysis_prompt = """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:
{records}

Provide ONLY the potential oversights in this format:

### Potential Oversights:
1. [Missed diagnosis] - [Evidence from records]
2. [Medication issue] - [Supporting data]
3. [Assessment gap] - [Relevant findings]""".format(records=extracted_data[:15000])  # Limit input size

                # Generate analysis
                response = []
                for chunk in agent.run_gradio_chat(
                    message=analysis_prompt,
                    history=[],
                    temperature=0.2,  # More deterministic
                    max_new_tokens=1024,
                    max_token=4096,
                    call_agent=False,
                    conversation=conversation
                ):
                    if isinstance(chunk, str):
                        response.append(chunk)
                    elif isinstance(chunk, list):
                        response.extend([c.content for c in chunk if hasattr(c, 'content')])
                    
                    if len(response) % 3 == 0:  # Update every 3 chunks
                        history[-1] = (message, "".join(response).strip())
                        yield history

                # Finalize output
                final_output = "".join(response).strip()
                if not final_output:
                    final_output = "No clear oversights identified. Recommend comprehensive review."

                # Format as bullet points if not already
                if not final_output.startswith(("1.", "-", "*", "#")):
                    final_output = "• " + final_output.replace("\n", "\n• ")

                history[-1] = (message, f"### Potential Clinical Oversights:\n{final_output}")
                print(f"Analysis completed in {time.time()-start_time:.2f}s")
                yield history

            except Exception as e:
                history.append((message, f"❌ Analysis failed: {str(e)}"))
                yield history

        # UI event handlers
        inputs = [msg_input, chatbot, conversation_state, file_upload]
        send_btn.click(analyze_potential_oversights, inputs=inputs, outputs=chatbot)
        msg_input.submit(analyze_potential_oversights, inputs=inputs, outputs=chatbot)

        gr.Examples([
            ["What might have been missed in this patient's treatment?"],
            ["Are there any medication conflicts in these records?"],
            ["What abnormal results require follow-up?"]
        ], inputs=msg_input)

    return demo

if __name__ == "__main__":
    print("Initializing medical analysis agent...")
    agent = init_agent()
    
    print("Launching interface...")
    demo = create_ui(agent)
    demo.queue(concurrency_count=2).launch(
        server_name="0.0.0.0",
        server_port=7860,
        show_error=True,
        share=True
    )