app.py

#import sys, os, json, gradio as gr, pandas as pd, pdfplumber, hashlib, shutil, re, time
from concurrent.futures import ThreadPoolExecutor, as_completed
from threading import Thread

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

base_dir = "/data"
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 = os.path.join(base_dir, "reports")
vllm_cache_dir = os.path.join(base_dir, "vllm_cache")

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

# Hugging Face & Transformers cache
os.environ.update({
    "HF_HOME": model_cache_dir,
    "TRANSFORMERS_CACHE": 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): return text.encode("utf-8", "ignore").decode("utf-8")
def file_hash(path): return hashlib.md5(open(path, "rb").read()).hexdigest()

def extract_priority_pages(file_path, max_pages=20):
    try:
        with pdfplumber.open(file_path) as pdf:
            pages = []
            for i, page in enumerate(pdf.pages[:3]):
                pages.append(f"=== Page {i+1} ===\n{(page.extract_text() or '').strip()}")
            for i, page in enumerate(pdf.pages[3:max_pages], start=4):
                text = page.extract_text() or ""
                if any(re.search(rf'\b{kw}\b', text.lower()) for kw in MEDICAL_KEYWORDS):
                    pages.append(f"=== Page {i} ===\n{text.strip()}")
            return "\n\n".join(pages)
    except Exception as e:
        return f"PDF processing error: {str(e)}"

def convert_file_to_json(file_path, file_type):
    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")
            result = json.dumps({"filename": os.path.basename(file_path), "rows": df.fillna("").astype(str).values.tolist()})
        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)
            result = json.dumps({"filename": os.path.basename(file_path), "rows": df.fillna("").astype(str).values.tolist()})
        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, file_hash_value):
    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("PDF processing error:", 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
    )
    agent.init_model()
    return agent

# Lazy load agent only on first use
agent_container = {"agent": None}
def get_agent():
    if agent_container["agent"] is None:
        agent_container["agent"] = init_agent()
    return agent_container["agent"]

def create_ui(get_agent_func):
    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(file_types=[".pdf", ".csv", ".xls", ".xlsx"], file_count="multiple")
        msg_input = gr.Textbox(placeholder="Ask about potential oversights...")
        send_btn = gr.Button("Analyze", variant="primary")
        state = gr.State([])
        download_output = gr.File(label="Download Report")

        def analyze(message, history, conversation, files):
            try:
                extracted_data, file_hash_value = "", ""
                if files:
                    with ThreadPoolExecutor(max_workers=4) as pool:
                        futures = [pool.submit(convert_file_to_json, f.name, f.name.split(".")[-1].lower()) for f in files]
                        extracted_data = "\n".join([sanitize_utf8(f.result()) for f in as_completed(futures)])
                        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:\n{extracted_data[:15000]}

### Potential Oversights:\n"""

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

                cleaned = final_response.replace("[TOOL_CALLS]", "").strip()
                if not cleaned:
                    cleaned = "No oversights found. Consider further review."

                updated_history = history + [{"role": "user", "content": message}, {"role": "assistant", "content": cleaned}]

                report_path = os.path.join(report_dir, f"{file_hash_value}_report.txt") if file_hash_value and os.path.exists(os.path.join(report_dir, f"{file_hash_value}_report.txt")) else None
                yield updated_history, report_path
            except Exception as e:
                updated_history = history + [{"role": "user", "content": message}, {"role": "assistant", "content": f"❌ Error: {str(e)}"}]
                yield updated_history, None

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

    return demo

if __name__ == "__main__":
    print("Launching interface...")
    ui = create_ui(get_agent)
    ui.queue(api_open=False).launch(
        server_name="0.0.0.0",
        server_port=7860,
        show_error=True,
        allowed_paths=["/data/reports"],
        share=False
    )