app.py

 import ThreadPoolExecutor, as_completed
import hashlib
import shutil
import time
from threading import Thread, Lock
import re
import tempfile
import threading

# ---------------------------------------------------------------------------------------
# Setup persistent directories for Hugging Face Spaces
# ---------------------------------------------------------------------------------------
# Use a persistent cache directory (adjust the path as needed based on your HF Space settings)
persistent_dir = "/data/hf_cache"
os.makedirs(persistent_dir, exist_ok=True)

model_cache_dir = os.path.join(persistent_dir, "txagent_models")
tool_cache_dir = os.path.join(persistent_dir, "tool_cache")
file_cache_dir = os.path.join(persistent_dir, "cache")
report_dir = os.path.join(persistent_dir, "reports")
vllm_cache_dir = os.path.join(persistent_dir, "vllm_cache")

for directory in [model_cache_dir, tool_cache_dir, file_cache_dir, report_dir, vllm_cache_dir]:
    os.makedirs(directory, exist_ok=True)

# Set environment variables so that model and transformers caches point to persistent storage.
os.environ["HF_HOME"] = model_cache_dir
os.environ["TRANSFORMERS_CACHE"] = model_cache_dir
os.environ["VLLM_CACHE_DIR"] = vllm_cache_dir
os.environ["TOKENIZERS_PARALLELISM"] = "false"
os.environ["CUDA_LAUNCH_BLOCKING"] = "1"

# Append the local source path if needed
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)

# ---------------------------------------------------------------------------------------
# Import the TxAgent from your tool package
# ---------------------------------------------------------------------------------------
from txagent.txagent import TxAgent

# ---------------------------------------------------------------------------------------
# Define constants and helper functions
# ---------------------------------------------------------------------------------------
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:
            # Process first three pages always
            for i, page in enumerate(pdf.pages[:3]):
                text = page.extract_text() or ""
                text_chunks.append(f"=== Page {i+1} ===\n{text.strip()}")
            # Process subsequent pages only if they contain key 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:
    try:
        h = file_hash(file_path)
        cache_path = os.path.join(file_cache_dir, f"{h}.json")
        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"})
            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 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:
        return json.dumps({"error": f"Error processing {os.path.basename(file_path)}: {str(e)}"})

def full_pdf_processing(file_path: str, file_hash_value: str):
    try:
        cache_path = os.path.join(file_cache_dir, f"{file_hash_value}_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_value}_report.txt"), "w", encoding="utf-8") as out:
            out.write(full_text)
    except Exception as e:
        print(f"Background processing failed: {str(e)}")

# ---------------------------------------------------------------------------------------
# Global agent variable and thread-safe lock for background model loading
# ---------------------------------------------------------------------------------------
agent = None
agent_lock = Lock()

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)
    new_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=[],
    )
    new_agent.init_model()
    return new_agent

def load_agent_in_background():
    global agent
    with agent_lock:
        if agent is None:
            print("Initializing agent in background (this may take a while)...")
            agent = init_agent()
            print("Agent initialization complete.")

# Start background agent loading at startup
threading.Thread(target=load_agent_in_background, daemon=True).start()

# ---------------------------------------------------------------------------------------
# Define the Gradio UI
# ---------------------------------------------------------------------------------------
def create_ui():
    with gr.Blocks(theme=gr.themes.Soft()) as demo:
        gr.Markdown("""
        <h1 style='text-align: center;'>🩺 Clinical Oversight Assistant</h1>
        <h3 style='text-align: center;'>Identify potential oversights in patient care</h3>
        """)
        chatbot = gr.Chatbot(label="Analysis", height=600, type="messages")
        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")
        download_output = gr.File(label="Download Full Report")

        def analyze_potential_oversights(message: str, history: list, files: list):
            global agent
            # Append user and interim assistant message
            history = history + [
                {"role": "user", "content": message},
                {"role": "assistant", "content": "⏳ Analyzing records for potential oversights..."}
            ]
            yield history, None

            if agent is None:
                history.append({"role": "assistant", 
                                "content": "🕒 The model is still loading. Please wait a moment and try again."})
                yield history, None
                return

            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')
                    ]
                    results = []
                    for future in as_completed(futures):
                        results.append(sanitize_utf8(future.result()))
                    extracted_data = "\n".join(results)
                    file_hash_value = file_hash(files[0].name) if hasattr(files[0], 'name') else ""

            # Truncate extracted data to avoid token overflow
            max_extracted_chars = 12000
            truncated_data = extracted_data[:max_extracted_chars]

            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:
{truncated_data}

### Potential Oversights:
"""

            response = ""
            try:
                # Stream agent responses and update the last message in the conversation with each chunk.
                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=[]
                ):
                    if chunk is None:
                        continue
                    if isinstance(chunk, str):
                        response += chunk
                    elif isinstance(chunk, list):
                        response += "".join([c.content for c in chunk if hasattr(c, 'content')])
                    cleaned = response.replace("[TOOL_CALLS]", "").strip()
                    # Update the assistant message (last item in history) with the latest accumulated answer
                    history[-1] = {"role": "assistant", "content": cleaned}
                    yield history, None
            except Exception as agent_error:
                history[-1] = {"role": "assistant", "content": f"❌ Analysis failed during processing: {str(agent_error)}"}
                yield history, None
                return

            final_output = response.replace("[TOOL_CALLS]", "").strip()
            if not final_output:
                final_output = "No clear oversights identified. Recommend comprehensive review."

            # Update the assistant's message with the final output
            history[-1] = {"role": "assistant", "content": final_output}

            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

            yield history, report_path

        send_btn.click(analyze_potential_oversights,
                       inputs=[msg_input, gr.State([]), file_upload],
                       outputs=[chatbot, download_output])
        msg_input.submit(analyze_potential_oversights,
                         inputs=[msg_input, gr.State([]), file_upload],
                         outputs=[chatbot, download_output])
        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("Launching interface...")
    demo = create_ui()
    demo.queue(api_open=False).launch(
        server_name="0.0.0.0",
        server_port=7860,
        show_error=True,
        allowed_paths=[report_dir],
        share=False
    )