Update app.py
Browse files
app.py
CHANGED
@@ -1,27 +1,28 @@
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import sys
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import os
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import pdfplumber
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import json
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import gradio as gr
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from typing import List
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from concurrent.futures import ThreadPoolExecutor, as_completed
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import hashlib
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import re
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import psutil
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import subprocess
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from collections import defaultdict
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from vllm import LLM, SamplingParams
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# Persistent directory
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persistent_dir =
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os.makedirs(persistent_dir, exist_ok=True)
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model_cache_dir = os.path.join(persistent_dir, "txagent_models")
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file_cache_dir = os.path.join(persistent_dir, "cache")
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report_dir = os.path.join(persistent_dir, "reports")
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vllm_cache_dir = os.path.join(persistent_dir, "vllm_cache")
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for directory in [model_cache_dir, file_cache_dir, report_dir, vllm_cache_dir]:
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os.makedirs(directory, exist_ok=True)
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os.environ["HF_HOME"] = model_cache_dir
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@@ -29,13 +30,15 @@ os.environ["TRANSFORMERS_CACHE"] = model_cache_dir
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os.environ["VLLM_CACHE_DIR"] = vllm_cache_dir
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
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os.environ["VLLM_NO_TORCH_COMPILE"] = "1"
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current_dir = os.path.dirname(os.path.abspath(__file__))
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src_path = os.path.abspath(os.path.join(current_dir, "src"))
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sys.path.insert(0, src_path)
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from txagent.txagent import TxAgent
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def sanitize_utf8(text: str) -> str:
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return text.encode("utf-8", "ignore").decode("utf-8")
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@@ -44,14 +47,15 @@ def file_hash(path: str) -> str:
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with open(path, "rb") as f:
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return hashlib.md5(f.read()).hexdigest()
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def
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try:
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text_chunks = []
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with pdfplumber.open(file_path) as pdf:
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for page in pdf.pages:
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page_text = page.extract_text() or ""
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except Exception as e:
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return f"PDF processing error: {str(e)}"
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@@ -62,9 +66,22 @@ def convert_file_to_json(file_path: str, file_type: str) -> str:
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if os.path.exists(cache_path):
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with open(cache_path, "r", encoding="utf-8") as f:
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return f.read()
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if file_type == "pdf":
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text =
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result = json.dumps({"filename": os.path.basename(file_path), "content": text, "status": "initial"})
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else:
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result = json.dumps({"error": f"Unsupported file type: {file_type}"})
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with open(cache_path, "w", encoding="utf-8") as f:
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@@ -88,61 +105,47 @@ def log_system_usage(tag=""):
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except Exception as e:
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print(f"[{tag}] GPU/CPU monitor failed: {e}")
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def
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headings = ["Missed Diagnoses", "Medication Conflicts", "Incomplete Assessments", "Urgent Follow-up"]
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for response in responses:
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if not response:
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continue
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current_heading = None
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for line in response.split("\n"):
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line = line.strip()
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if not line:
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continue
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if line.lower().startswith(tuple(h.lower() + ":" for h in headings)):
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current_heading = next(h for h in headings if line.lower().startswith(h.lower() + ":"))
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elif current_heading and line.startswith("-"):
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findings[current_heading].add(normalize_text(line))
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output = []
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for heading in headings:
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if findings[heading]:
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output.append(f"**{heading}**:")
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original_lines = {normalize_text(r): r for r in sum([r.split("\n") for r in responses], []) if r.startswith("-")}
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output.extend(sorted(original_lines.get(n, "- " + n) for n in findings[heading]))
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return "\n".join(output).strip() if output else "No oversights identified."
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def init_agent():
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print("π Initializing model...")
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log_system_usage("Before Load")
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)
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log_system_usage("After Load")
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print("β
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return
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def create_ui(
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with gr.Blocks(theme=gr.themes.Soft()) as demo:
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gr.Markdown("<h1 style='text-align: center;'>π©Ί Clinical Oversight Assistant</h1>")
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chatbot = gr.Chatbot(label="Analysis", height=600, type="messages")
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file_upload = gr.File(file_types=[".pdf"], file_count="multiple")
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msg_input = gr.Textbox(placeholder="Ask about potential oversights...", show_label=False)
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send_btn = gr.Button("Analyze", variant="primary")
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download_output = gr.File(label="Download Report")
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def analyze(message: str, history: List[dict], files: List):
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history.append({"role": "user", "content": message})
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history.append({"role": "assistant", "content": "
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yield history, None
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extracted = ""
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@@ -151,82 +154,97 @@ def create_ui(model):
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with ThreadPoolExecutor(max_workers=6) as executor:
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futures = [executor.submit(convert_file_to_json, f.name, f.name.split(".")[-1].lower()) for f in files]
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results = [sanitize_utf8(f.result()) for f in as_completed(futures)]
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extracted = "\n".join(
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file_hash_value = file_hash(files[0].name) if files else ""
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chunks = [extracted[i:i + chunk_size] for i in range(0, len(extracted), chunk_size)]
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batch_size = 4 # MODIFIED: Lower for VRAM
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total_chunks = len(chunks)
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prompt_template = """
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**Urgent Follow-up**:
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for i in range(0, len(chunks), batch_size):
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batch = chunks[i:i + batch_size]
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prompts = [prompt_template.format(chunk=chunk) for chunk in batch]
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log_system_usage(f"Batch {i//batch_size + 1}")
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outputs = model.generate(prompts, sampling_params, use_tqdm=True) # MODIFIED: Stream progress
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batch_responses = []
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with ThreadPoolExecutor(max_workers=4) as executor:
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futures = [executor.submit(clean_response, output.outputs[0].text) for output in outputs]
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batch_responses.extend(f.result() for f in as_completed(futures))
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processed = min(i + len(batch), total_chunks)
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batch_output = []
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for response in batch_responses:
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if response:
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chunk_responses.append(response)
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current_heading = None
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for line in response.split("\n"):
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line = line.strip()
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if line.lower().startswith(tuple(h.lower() + ":" for h in ["missed diagnoses", "medication conflicts", "incomplete assessments", "urgent follow-up"])):
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current_heading = line[:-1]
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if current_heading not in batch_output:
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batch_output.append(current_heading + ":")
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elif current_heading and line.startswith("-"):
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findings[current_heading].append(line)
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batch_output.append(line)
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# MODIFIED: Stream partial results
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if batch_output:
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history[-1]["content"] = "\n".join(batch_output) + f"\n\nπ Processing chunk {processed}/{total_chunks}..."
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else:
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history[-1]["content"] = f"π Processing chunk {processed}/{total_chunks}..."
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yield history, None
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#
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report_path = os.path.join(report_dir, f"{file_hash_value}_report.txt") if file_hash_value else None
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if report_path
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with open(report_path, "w", encoding="utf-8") as f:
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f.write(
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yield history, report_path if report_path and os.path.exists(report_path) else None
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except Exception as e:
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print("π¨ ERROR:", e)
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history
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yield history, None
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send_btn.click(analyze, inputs=[msg_input, gr.State([]), file_upload], outputs=[chatbot, download_output])
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if __name__ == "__main__":
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print("π Launching app...")
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demo = create_ui(
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demo.queue(api_open=False).launch(
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server_name="0.0.0.0",
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server_port=7860,
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import sys
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import os
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import pandas as pd
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import pdfplumber
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import json
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import gradio as gr
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from typing import List
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from concurrent.futures import ThreadPoolExecutor, as_completed
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import hashlib
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import shutil
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import re
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import psutil
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import subprocess
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# Persistent directory
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persistent_dir = "/data/hf_cache"
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os.makedirs(persistent_dir, exist_ok=True)
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model_cache_dir = os.path.join(persistent_dir, "txagent_models")
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tool_cache_dir = os.path.join(persistent_dir, "tool_cache")
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file_cache_dir = os.path.join(persistent_dir, "cache")
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report_dir = os.path.join(persistent_dir, "reports")
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vllm_cache_dir = os.path.join(persistent_dir, "vllm_cache")
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for directory in [model_cache_dir, tool_cache_dir, file_cache_dir, report_dir, vllm_cache_dir]:
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os.makedirs(directory, exist_ok=True)
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os.environ["HF_HOME"] = model_cache_dir
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os.environ["VLLM_CACHE_DIR"] = vllm_cache_dir
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
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current_dir = os.path.dirname(os.path.abspath(__file__))
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src_path = os.path.abspath(os.path.join(current_dir, "src"))
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sys.path.insert(0, src_path)
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from txagent.txagent import TxAgent
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MEDICAL_KEYWORDS = {'diagnosis', 'assessment', 'plan', 'results', 'medications',
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'allergies', 'summary', 'impression', 'findings', 'recommendations'}
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def sanitize_utf8(text: str) -> str:
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return text.encode("utf-8", "ignore").decode("utf-8")
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with open(path, "rb") as f:
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return hashlib.md5(f.read()).hexdigest()
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def extract_priority_pages(file_path: str) -> str:
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try:
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text_chunks = []
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with pdfplumber.open(file_path) as pdf:
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for i, page in enumerate(pdf.pages):
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page_text = page.extract_text() or ""
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if i < 3 or any(re.search(rf'\b{kw}\b', page_text.lower()) for kw in MEDICAL_KEYWORDS):
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text_chunks.append(f"=== Page {i+1} ===\n{page_text.strip()}")
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return "\n\n".join(text_chunks)
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except Exception as e:
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return f"PDF processing error: {str(e)}"
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if os.path.exists(cache_path):
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with open(cache_path, "r", encoding="utf-8") as f:
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return f.read()
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if file_type == "pdf":
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text = extract_priority_pages(file_path)
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result = json.dumps({"filename": os.path.basename(file_path), "content": text, "status": "initial"})
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elif file_type == "csv":
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df = pd.read_csv(file_path, encoding_errors="replace", header=None, dtype=str,
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skip_blank_lines=False, on_bad_lines="skip")
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content = df.fillna("").astype(str).values.tolist()
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result = json.dumps({"filename": os.path.basename(file_path), "rows": content})
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elif file_type in ["xls", "xlsx"]:
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try:
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df = pd.read_excel(file_path, engine="openpyxl", header=None, dtype=str)
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except Exception:
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df = pd.read_excel(file_path, engine="xlrd", header=None, dtype=str)
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content = df.fillna("").astype(str).values.tolist()
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result = json.dumps({"filename": os.path.basename(file_path), "rows": content})
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else:
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result = json.dumps({"error": f"Unsupported file type: {file_type}"})
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with open(cache_path, "w", encoding="utf-8") as f:
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except Exception as e:
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print(f"[{tag}] GPU/CPU monitor failed: {e}")
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def clean_response(text: str) -> str:
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text = sanitize_utf8(text)
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text = re.sub(r"\[TOOL_CALLS\].*", "", text, flags=re.DOTALL)
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text = re.sub(r"\n{3,}", "\n\n", text).strip()
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return text
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def init_agent():
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print("π Initializing model...")
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log_system_usage("Before Load")
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default_tool_path = os.path.abspath("data/new_tool.json")
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target_tool_path = os.path.join(tool_cache_dir, "new_tool.json")
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if not os.path.exists(target_tool_path):
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shutil.copy(default_tool_path, target_tool_path)
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agent = TxAgent(
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model_name="mims-harvard/TxAgent-T1-Llama-3.1-8B",
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rag_model_name="mims-harvard/ToolRAG-T1-GTE-Qwen2-1.5B",
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tool_files_dict={"new_tool": target_tool_path},
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force_finish=True,
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enable_checker=True,
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step_rag_num=4,
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seed=100,
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additional_default_tools=[],
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)
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agent.init_model()
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log_system_usage("After Load")
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print("β
Agent Ready")
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return agent
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def create_ui(agent):
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with gr.Blocks(theme=gr.themes.Soft()) as demo:
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gr.Markdown("<h1 style='text-align: center;'>π©Ί Clinical Oversight Assistant</h1>")
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chatbot = gr.Chatbot(label="Analysis", height=600, type="messages")
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file_upload = gr.File(file_types=[".pdf", ".csv", ".xls", ".xlsx"], file_count="multiple")
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msg_input = gr.Textbox(placeholder="Ask about potential oversights...", show_label=False)
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send_btn = gr.Button("Analyze", variant="primary")
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download_output = gr.File(label="Download Full Report")
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def analyze(message: str, history: List[dict], files: List):
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history.append({"role": "user", "content": message})
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history.append({"role": "assistant", "content": "β³ Analyzing records for potential oversights..."})
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yield history, None
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extracted = ""
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with ThreadPoolExecutor(max_workers=6) as executor:
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futures = [executor.submit(convert_file_to_json, f.name, f.name.split(".")[-1].lower()) for f in files]
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results = [sanitize_utf8(f.result()) for f in as_completed(futures)]
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extracted = "\n".join(results)
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file_hash_value = file_hash(files[0].name) if files else ""
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# Split extracted text into chunks of ~6,000 characters
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chunk_size = 6000
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chunks = [extracted[i:i + chunk_size] for i in range(0, len(extracted), chunk_size)]
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combined_response = ""
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prompt_template = f"""
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Analyze the medical records for clinical oversights. Provide a concise, evidence-based summary under these headings:
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1. **Missed Diagnoses**:
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- Identify inconsistencies in history, symptoms, or tests.
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- Consider psychiatric, neurological, infectious, autoimmune, genetic conditions, family history, trauma, and developmental factors.
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2. **Medication Conflicts**:
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171 |
+
- Check for contraindications, interactions, or unjustified off-label use.
|
172 |
+
- Assess if medications worsen diagnoses or cause adverse effects.
|
173 |
+
3. **Incomplete Assessments**:
|
174 |
+
- Note missing or superficial cognitive, psychiatric, social, or family assessments.
|
175 |
+
- Highlight gaps in medical history, substance use, or lab/imaging documentation.
|
176 |
+
4. **Urgent Follow-up**:
|
177 |
+
- Flag abnormal lab results, imaging, behaviors, or legal history needing immediate reassessment or referral.
|
178 |
+
Medical Records (Chunk {0} of {1}):
|
179 |
+
{{chunk}}
|
180 |
+
Begin analysis:
|
181 |
+
"""
|
182 |
|
183 |
+
try:
|
184 |
+
if history and history[-1]["content"].startswith("β³"):
|
185 |
+
history.pop()
|
|
|
186 |
|
187 |
+
# Process each chunk and stream results in real-time
|
188 |
+
for chunk_idx, chunk in enumerate(chunks, 1):
|
189 |
+
# Update UI with progress
|
190 |
+
history.append({"role": "assistant", "content": f"π Processing Chunk {chunk_idx} of {len(chunks)}..."})
|
191 |
+
yield history, None
|
192 |
|
193 |
+
prompt = prompt_template.format(chunk_idx, len(chunks), chunk=chunk)
|
194 |
+
chunk_response = ""
|
195 |
+
for chunk_output in agent.run_gradio_chat(
|
196 |
+
message=prompt,
|
197 |
+
history=[],
|
198 |
+
temperature=0.2,
|
199 |
+
max_new_tokens=1024,
|
200 |
+
max_token=4096,
|
201 |
+
call_agent=False,
|
202 |
+
conversation=[],
|
203 |
+
):
|
204 |
+
if chunk_output is None:
|
205 |
+
continue
|
206 |
+
if isinstance(chunk_output, list):
|
207 |
+
for m in chunk_output:
|
208 |
+
if hasattr(m, 'content') and m.content:
|
209 |
+
cleaned = clean_response(m.content)
|
210 |
+
if cleaned:
|
211 |
+
chunk_response += cleaned + "\n"
|
212 |
+
# Update UI with partial response
|
213 |
+
if history[-1]["content"].startswith("π"):
|
214 |
+
history[-1] = {"role": "assistant", "content": f"--- Analysis for Chunk {chunk_idx} ---\n{chunk_response.strip()}"}
|
215 |
+
else:
|
216 |
+
history[-1]["content"] = f"--- Analysis for Chunk {chunk_idx} ---\n{chunk_response.strip()}"
|
217 |
+
yield history, None
|
218 |
+
elif isinstance(chunk_output, str) and chunk_output.strip():
|
219 |
+
cleaned = clean_response(chunk_output)
|
220 |
+
if cleaned:
|
221 |
+
chunk_response += cleaned + "\n"
|
222 |
+
# Update UI with partial response
|
223 |
+
if history[-1]["content"].startswith("π"):
|
224 |
+
history[-1] = {"role": "assistant", "content": f"--- Analysis for Chunk {chunk_idx} ---\n{chunk_response.strip()}"}
|
225 |
+
else:
|
226 |
+
history[-1]["content"] = f"--- Analysis for Chunk {chunk_idx} ---\n{chunk_response.strip()}"
|
227 |
+
yield history, None
|
228 |
|
229 |
+
# Append completed chunk response to combined response
|
230 |
+
combined_response += f"--- Analysis for Chunk {chunk_idx} ---\n{chunk_response}\n"
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
231 |
|
232 |
+
# Finalize UI with complete response
|
233 |
+
if combined_response:
|
234 |
+
history[-1]["content"] = combined_response.strip()
|
235 |
+
else:
|
236 |
+
history.append({"role": "assistant", "content": "No oversights identified."})
|
237 |
|
238 |
+
# Generate report file
|
239 |
report_path = os.path.join(report_dir, f"{file_hash_value}_report.txt") if file_hash_value else None
|
240 |
+
if report_path:
|
241 |
with open(report_path, "w", encoding="utf-8") as f:
|
242 |
+
f.write(combined_response)
|
243 |
yield history, report_path if report_path and os.path.exists(report_path) else None
|
244 |
|
245 |
except Exception as e:
|
246 |
print("π¨ ERROR:", e)
|
247 |
+
history.append({"role": "assistant", "content": f"β Error occurred: {str(e)}"})
|
248 |
yield history, None
|
249 |
|
250 |
send_btn.click(analyze, inputs=[msg_input, gr.State([]), file_upload], outputs=[chatbot, download_output])
|
|
|
253 |
|
254 |
if __name__ == "__main__":
|
255 |
print("π Launching app...")
|
256 |
+
agent = init_agent()
|
257 |
+
demo = create_ui(agent)
|
258 |
demo.queue(api_open=False).launch(
|
259 |
server_name="0.0.0.0",
|
260 |
server_port=7860,
|