Update app.py

app.py

@@ -1,18 +1,284 @@
-import os
 import sys
+import os
+import pandas as pd
+import pdfplumber
+import json
 import gradio as gr
-from multiprocessing import freeze_support
+from typing import List
+from concurrent.futures import ThreadPoolExecutor, as_completed
+import hashlib
+import shutil
+import re
+import psutil
+import subprocess
+import traceback
+import torch
+
+os.environ["VLLM_LOGGING_LEVEL"] = "DEBUG"
+if not torch.cuda.is_available():
+    print("No GPU detected. Forcing CPU mode by setting CUDA_VISIBLE_DEVICES to an empty string.")
+    os.environ["CUDA_VISIBLE_DEVICES"] = ""
+
+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)
+
+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"
+
+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)
+
+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 = 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):
+                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:
+        debug_msg = f"PDF processing error: {str(e)}"
+        print(debug_msg)
+        traceback.print_exc()
+        return debug_msg
+
+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"})
+        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:
+        error_msg = f"Error processing {os.path.basename(file_path)}: {str(e)}"
+        print(error_msg)
+        traceback.print_exc()
+        return json.dumps({"error": error_msg})
+
+def log_system_usage(tag=""):
+    try:
+        cpu = psutil.cpu_percent(interval=1)
+        mem = psutil.virtual_memory()
+        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:
+        print(f"[{tag}] GPU/CPU monitor failed: {e}")
+        traceback.print_exc()
+
+def init_agent():
+    try:
+        print("\U0001F501 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")
+        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
+    except Exception as e:
+        print("❌ Error initializing agent:", str(e))
+        traceback.print_exc()
+        raise e
+
+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):
+            try:
+                history.append({"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 = []
+                        for future in as_completed(futures):
+                            try:
+                                results.append(sanitize_utf8(future.result()))
+                            except Exception as e:
+                                print("❌ Error in file processing:", str(e))
+                                traceback.print_exc()
+                        extracted = "\n".join(results)
+                        file_hash_value = file_hash(files[0].name)
+
+                max_content_length = 8000
+                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[:max_content_length]}
+### Potential Oversights:
+"""
+
+                full_response = ""
+                response_chunks = []
+
+                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:
+                        chunk_content = ""
+                        if isinstance(chunk, str):
+                            chunk_content = chunk
+                        elif hasattr(chunk, 'content'):
+                            chunk_content = chunk.content
+                        elif isinstance(chunk, list):
+                            chunk_content = "".join([c.content for c in chunk if hasattr(c, "content") and c.content])
+
+                        if not chunk_content:
+                            continue
+
+                        response_chunks.append(chunk_content)
+                        full_response = "".join(response_chunks)
+
+                        display_response = re.split(r"\\[TOOL_CALLS\\].*?$", full_response, flags=re.DOTALL)[0].strip()
+                        display_response = display_response.replace('[TxAgent]', '').strip()
+
+                        if len(history) > 1 and history[-2]["role"] == "assistant" and history[-2]["content"] == display_response:
+                            pass
+                        else:
+                            if len(history) > 0 and history[-1]["role"] == "assistant":
+                                history[-1]["content"] = display_response
+                            else:
+                                history.append({"role": "assistant", "content": display_response})
+
+                        yield history, None
+                    except Exception as e:
+                        print("❌ Error processing chunk:", str(e))
+                        traceback.print_exc()
+                        continue
+
+                if not full_response:
+                    full_response = "⚠️ No clear oversights identified or model output was invalid."
+                else:
+                    full_response = re.split(r"\\[TOOL_CALLS\\].*?$", full_response, flags=re.DOTALL)[0].strip()
+                    full_response = full_response.replace('[TxAgent]', '').strip()
+
+                report_path = None
+                if file_hash_value:
+                    report_path = os.path.join(report_dir, f"{file_hash_value}_report.txt")
+                    try:
+                        with open(report_path, "w", encoding="utf-8") as f:
+                            f.write(full_response)
+                    except Exception as e:
+                        print("❌ Error saving report:", str(e))
+                        traceback.print_exc()
+
+                if len(history) > 0 and history[-1]["role"] == "assistant":
+                    history[-1]["content"] = full_response
+                else:
+                    history.append({"role": "assistant", "content": full_response})
+
+                yield history, report_path if report_path and os.path.exists(report_path) else None
+
+            except Exception as e:
+                error_message = f"❌ An error occurred in analyze: {str(e)}"
+                print(error_message)
+                traceback.print_exc()
+                history.append({"role": "assistant", "content": error_message})
+                yield history, None
 
-from ui.ui_core import create_ui
-from backend.agent_instance import init_agent
+        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__":
-    freeze_support()
-    agent = init_agent()
-    demo = create_ui(agent)
-    demo.queue().launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        show_error=True,
-        share=True
-    )
+    try:
+        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
+        )
+    except Exception as e:
+        print("❌ Fatal error during launch:", str(e))
+        traceback.print_exc()