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
import tempfile

# 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")
report_dir = "/data/reports"
vllm_cache_dir = os.path.join(base_dir, "vllm_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.makedirs(report_dir, exist_ok=True)
os.makedirs(vllm_cache_dir, exist_ok=True)

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

from txagent.txagent import TxAgent

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:
    try:
        text_chunks = []
        with pdfplumber.open(file_path) as pdf:
            for i, page in enumerate(pdf.pages[:3]):
                text_chunks.append(f"=== Page {i+1} ===\n{(page.extract_text() or '').strip()}")
            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:
    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":
            text = extract_priority_pages(file_path)
            result = json.dumps({"filename": os.path.basename(file_path), "content": text, "status": "initial"})
            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):
    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)
        with open(os.path.join(report_dir, f"{file_hash}_report.txt"), "w", encoding="utf-8") as out:
            out.write(full_text)
    except Exception as e:
        print(f"Background processing failed: {str(e)}")

def init_agent():
    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 format_analysis_output(text: str) -> str:
    """Clean and format the analysis output for better display"""
    # Remove tool call artifacts
    text = text.replace("[TOOL_CALLS]", "").strip()
    if "[TOOL_CALLS]" in text:
        text = text.split("[TOOL_CALLS]")[0].strip()
    
    # Remove duplicate sections
    if "Based on the medical records provided" in text:
        parts = text.split("Based on the medical records provided")
        if len(parts) > 1:
            text = "Based on the medical records provided" + parts[-1]
    
    # Format sections with Markdown and emojis
    replacements = {
        "1. **Missed Diagnoses**:": "### 🔍 Missed Diagnoses",
        "2. **Medication Conflicts**:": "\n### 💊 Medication Conflicts",
        "3. **Incomplete Assessments**:": "\n### 📋 Incomplete Assessments",
        "4. **Abnormal Results Needing Follow-up**:": "\n### ⚠️ Abnormal Results Needing Follow-up",
        "Overall, the patient's medical records": "\n### 📝 Overall Assessment"
    }
    
    for old, new in replacements.items():
        text = text.replace(old, new)
    
    return text

def analyze_potential_oversights(message: str, history: list, conversation: list, files: list):
    start_time = time.time()
    try:
        history = history + [{"role": "user", "content": message}, {"role": "assistant", "content": "⏳ Analyzing records for potential oversights..."}]
        yield history, None

        extracted_data = ""
        file_hash_value = ""
        if files and isinstance(files, list):
            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)])
                file_hash_value = file_hash(files[0].name) if files else ""

        analysis_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:\n{extracted_data[:15000]}

### Potential Oversights:\n"""

        response = ""
        for chunk in agent.run_gradio_chat(
            message=analysis_prompt,
            history=[],
            temperature=0.2,
            max_new_tokens=1024,
            max_token=4096,
            call_agent=False,
            conversation=conversation
        ):
            if isinstance(chunk, str):
                response += chunk
            elif isinstance(chunk, list):
                response += "".join([c.content for c in chunk if hasattr(c, 'content')])

            # Format the partial response for display
            formatted = format_analysis_output(response)
            yield history[:-1] + [{"role": "assistant", "content": formatted}], None

        # Final formatting
        final_output = format_analysis_output(response)
        if not final_output:
            final_output = "No clear oversights identified. Recommend comprehensive review."

        report_path = None
        if file_hash_value:
            possible_report = os.path.join(report_dir, f"{file_hash_value}_report.txt")
            if os.path.exists(possible_report):
                report_path = possible_report

        history = history[:-1] + [{"role": "assistant", "content": final_output}]
        yield history, report_path

    except Exception as e:
        history.append({"role": "assistant", "content": f"❌ Analysis failed: {str(e)}"})
        yield history, None

def create_ui(agent: TxAgent):
    with gr.Blocks(theme=gr.themes.Soft(), css=".gradio-container {max-width: 900px !important}") as demo:
        gr.Markdown("""
        <div style='text-align: center;'>
            <h1>🩺 Clinical Oversight Assistant</h1>
            <h3>Identify potential oversights in patient care</h3>
            <p>Upload medical records to analyze for missed diagnoses, medication conflicts, incomplete assessments, and abnormal results needing follow-up.</p>
        </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",
                    height=100
                )
                msg_input = gr.Textbox(
                    placeholder="Ask about potential oversights...", 
                    show_label=False,
                    lines=3,
                    max_lines=6
                )
                send_btn = gr.Button("Analyze", variant="primary", size="lg")
                
                gr.Examples(
                    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?"],
                        ["Identify any incomplete assessments in these records"]
                    ],
                    inputs=msg_input,
                    label="Example Queries"
                )

            with gr.Column(scale=3):
                chatbot = gr.Chatbot(
                    label="Analysis Results", 
                    height=600,
                    bubble_full_width=False,
                    show_copy_button=True,
                    avatar_images=(
                        "assets/user.png", 
                        "assets/doctor.png"
                    )
                )
                download_output = gr.File(
                    label="Download Full Report",
                    visible=False
                )

        conversation_state = gr.State([])

        inputs = [msg_input, chatbot, conversation_state, file_upload]
        outputs = [chatbot, download_output]
        
        send_btn.click(
            analyze_potential_oversights, 
            inputs=inputs, 
            outputs=outputs
        )
        msg_input.submit(
            analyze_potential_oversights, 
            inputs=inputs, 
            outputs=outputs
        )

    return demo

if __name__ == "__main__":
    print("Initializing medical analysis agent...")
    agent = init_agent()

    print("Launching interface...")
    demo = create_ui(agent)
    demo.queue(
        concurrency_count=3,
        api_open=False
    ).launch(
        server_name="0.0.0.0",
        server_port=7860,
        show_error=True,
        allowed_paths=["/data/reports"],
        share=False
    )