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import os |
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import ast |
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import spaces |
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import gradio as gr |
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from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer |
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from huggingface_hub import login as hf_login |
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import xgrammar as xgr |
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from pydantic import BaseModel |
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hf_login(token=os.getenv("HF_TOKEN")) |
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model_name = "gregorlied/Llama-3.2-1B-Instruct-Medical-Report-Summarization-FP32" |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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device_map="auto", |
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attn_implementation='eager', |
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trust_remote_code=True, |
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) |
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class Person(BaseModel): |
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life_style: str |
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family_history: str |
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social_history: str |
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medical_surgical_history: str |
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signs_symptoms: str |
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comorbidities: str |
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diagnostic_techniques_procedures: str |
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diagnosis: str |
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laboratory_values: str |
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pathology: str |
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pharmacological_therapy: str |
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interventional_therapy: str |
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patient_outcome_assessment: str |
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age: str |
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gender: str |
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config = AutoConfig.from_pretrained(model_name) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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tokenizer_info = xgr.TokenizerInfo.from_huggingface( |
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tokenizer, vocab_size=len(tokenizer) |
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) |
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grammar_compiler = xgr.GrammarCompiler(tokenizer_info) |
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compiled_grammar = grammar_compiler.compile_json_schema(Person) |
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default_value = "A 57-year-old male presented with fever (38.9°C), chest pain, cough, and progressive dyspnea. The patient exhibited tachypnea (34 breaths/min) and tachycardia (134 bpm). Auscultation revealed decreased breath sounds in both lung bases, with crackles on the left. A chest X-ray revealed bilateral pleural opacities and enlargement of the cardiac silhouette ( A). Echocardiography showed moderate pericardial effusion affecting the entire cardiac silhouette. Pericardiocentesis yielded 250 mL of exudative fluid. A CT scan of the chest showed pneumonia in the left lower lobe, bilateral pleural effusion, and moderate pericardial effusion ( B). Thoracentesis was performed and yielded 1,050 mL of exudative fluid. Laboratory tests yielded the following data: white blood cell count, 11.78 × 109 cells/L (84.3% neutrophils, 4.3% lymphocytes, and 9.1% monocytes); platelet count, 512 × 109/L; serum C-reactive protein, 31.27 mg/dL; serum creatinine, 0.94 mg/dL; serum sodium, 133 mEq/L; and serum potassium, 3.72 mEq/L. Examination of the pleural fluid showed a pH of 7.16, a glucose level of 4.5 mg/dL, proteins at 49.1 g/L, and an LDH content of 1,385 U/L. A urinary pneumococcal antigen test was positive. Pleural fluid culture was positive for S. pneumoniae. The patient was treated for four weeks with amoxicillin-clavulanate (2.2 g/8 h, i.v.) plus levofloxacin (500 mg twice a day), together with a nonsteroidal anti-inflammatory drug (ibuprofen, 800 mg/day), after which there was nearly complete resolution of the alterations seen on the chest X-ray and CT scan." |
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prompt = """You are a text extraction system for clinical reports. |
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Please extract relevant clinical information from the report. |
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### Instructions |
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- Use the JSON Schema given below. |
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- Return only a valid JSON object – no markdown, no comments. |
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- If no relevant facts are given for a field, set its value to "N/A". |
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- If multile relevant facts are given for a field, separate them with "; ". |
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### JSON Schema |
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{ |
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'life_style': '', |
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'family_history': '', |
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'social_history': '', |
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'medical_surgical_history': '', |
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'signs_symptoms': '', |
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'comorbidities': '', |
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'diagnostic_techniques_procedures': '', |
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'diagnosis': '', |
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'laboratory_values': '', |
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'pathology': '', |
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'pharmacological_therapy': '', |
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'interventional_therapy': '', |
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'patient_outcome_assessment': '', |
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'age': '', |
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'gender': '', |
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} |
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### Clinical Report |
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""" |
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def generate_html_tables(data, selected_fields): |
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key_label_map = { |
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'age': 'Age', |
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'gender': 'Gender', |
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'life_style': 'Lifestyle', |
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'social_history': 'Social Background', |
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'medical_surgical_history': 'Personal', |
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'family_history': 'Family Members', |
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'signs_symptoms': 'Symptoms', |
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'comorbidities': 'Comorbid Conditions', |
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'diagnostic_techniques_procedures': 'Diagnostic Procedures', |
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'laboratory_values': 'Laboratory Results', |
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'pathology': 'Pathology Report', |
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'diagnosis': 'Diagnosis', |
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'interventional_therapy': 'Interventional Therapy', |
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'pharmacological_therapy': 'Pharmacological Therapy', |
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'patient_outcome_assessment': 'Patient Outcome', |
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} |
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label_key_map = {v: k for k, v in key_label_map.items()} |
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categories = { |
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"Personal Information": ["Age", "Gender", "Lifestyle", "Social Background"], |
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"Medical History": ["Personal", "Family Members"], |
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"Clinical Presentation": ["Symptoms", "Comorbid Conditions"], |
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"Medical Assessment": ["Diagnostic Procedures", "Laboratory Results", "Pathology Report"], |
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"Diagnosis": ["Diagnosis"], |
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"Treatment": ["Interventional Therapy", "Pharmacological Therapy"], |
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"Patient Outcome": ["Patient Outcome"], |
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} |
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def format_bullets(value): |
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items = [item.strip() for item in value.split(";") if item.strip()] |
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if not items: |
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return "<i>Not Available</i>" |
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if len(items) == 1: |
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return items[0] |
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return "<ul style='margin: 0; padding-left: 1em'>" + "".join(f"<li>{item}</li>" for item in items) + "</ul>" |
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table_style = ( |
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"width: 100%;" |
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"height: 100%;" |
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"table-layout: fixed;" |
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) |
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th_td_style = ( |
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"padding: 8px;" |
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"border: 1px solid #ccc;" |
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"vertical-align: top;" |
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"text-align: left;" |
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) |
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html_tables = [] |
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for section, labels in categories.items(): |
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section_fields = [label for label in labels if label in selected_fields] |
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if section_fields: |
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table_html = f"<h3 style='margin-bottom: 0.5em;'>{section}</h3>" |
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table_html += f"<table style='{table_style}'>" |
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table_html += f"<tr><th style='height: 30px; {th_td_style}; width: 150px;'>Field</th><th style='height: 30px; {th_td_style};'>Details</th></tr>" |
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for label in section_fields: |
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key = label_key_map[label] |
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value = data.get(key, "N/A") |
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details = "<i>Not Available</i>" if value == "N/A" else format_bullets(value) |
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table_html += f"<tr><td style='{th_td_style}; width: 150px;'><b>{label}</b></td><td style='{th_td_style}'>{details}</td></tr>" |
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table_html += "</table>" |
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html_tables.append(table_html) |
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i = 0 |
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grouped_html = "" |
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while i < len(html_tables): |
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num_per_row = 2 if i < 4 else 3 |
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row_tables = html_tables[i:i+num_per_row] |
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grouped_html += ( |
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"<div style='display: flex; gap: 1em; margin-bottom: 2em;'>" |
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) |
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for table in row_tables: |
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grouped_html += ( |
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"<div style='display: flex; flex-direction: column;'>" |
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f"{table}" |
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"</div>" |
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) |
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grouped_html += "</div>" |
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i += num_per_row |
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return f"<div style='font-family: sans-serif;'>{grouped_html}</div>" |
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@spaces.GPU(duration=60) |
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def summarize( |
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text, |
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personal_info, |
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medical_history, |
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clinical_presentation, |
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medical_assessment, |
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diagnosis, |
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treatment, |
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patient_outcome, |
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): |
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if not text.strip(): |
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return "Please enter some text to summarize." |
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if text == default_value: |
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response = ['{"life_style": "N/A", "family_history": "N/A", "social_history": "N/A", "medical_surgical_history": "N/A", "signs_symptoms": "Fever; Chest pain; Cough; Progressive dyspnea; Tachypnea; Tachycardia; Decreased breath sounds in both lung bases; Crackles on the left", "comorbidities": "N/A", "diagnostic_techniques_procedures": "Chest X-ray; Echocardiography; Thoracentesis; Laboratory tests; Pleural fluid analysis; Urinary pneumococcal antigen test; Pleural fluid culture", "diagnosis": "Pneumonia; Pericardial effusion; S. pneumoniae infection", "laboratory_values": "White blood cell count: 11.78 \\u00d7 10^9 cells/L (84.3% neutrophils, 4.3% lymphocytes, 9.1% monocytes); Platelet count: 512 \\u00d7 10^9/L; Serum C-reactive protein: 31.27 mg/dL; Serum creatinine: 0.94 mg/dL; Serum sodium: 133 mEq/L; Serum potassium: 3.72 mEq/L; Pleural fluid pH: 7.16; Pleural fluid glucose: 4.5 mg/dL; Pleural fluid proteins: 49.1 g/L; Pleural fluid LDH: 1,385 U/L", "pathology": "N/A", "pharmacological_therapy": "Amoxicillin-clavulanate (2.2 g/8 h, i.v.); Levofloxacin (500 mg twice a day); Ibuprofen (800 mg/day)", "interventional_therapy": "Pericardiocentesis; Thoracentesis", "patient_outcome_assessment": "Nearly complete resolution of alterations on chest X-ray and CT scan", "age": "57 year", "gender": "Male"}'] |
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else: |
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messages = [ |
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{"role": "system", "content": prompt.strip()}, |
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{"role": "user", "content": text.strip()}, |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True, |
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enable_thinking=False, |
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) |
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xgr_logits_processor = xgr.contrib.hf.LogitsProcessor(compiled_grammar) |
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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generated_ids = model.generate( |
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input_ids=model_inputs["input_ids"], |
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attention_mask = model_inputs["attention_mask"], |
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max_new_tokens=2048, |
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logits_processor=[xgr_logits_processor] |
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) |
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generated_ids = [ |
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
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] |
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) |
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try: |
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data = ast.literal_eval(response[0]) |
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except: |
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data = { |
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'life_style': 'N/A', |
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'family_history': 'N/A', |
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'social_history': 'N/A', |
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'medical_surgical_history': 'N/A', |
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'signs_symptoms': 'N/A', |
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'comorbidities': 'N/A', |
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'diagnostic_techniques_procedures': 'N/A', |
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'diagnosis': 'N/A', |
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'laboratory_values': 'N/A', |
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'pathology': '', |
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'pharmacological_therapy': 'N/A', |
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'interventional_therapy': 'N/A', |
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'patient_outcome_assessment': 'N/A', |
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'age': 'N/A', |
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'gender': 'N/A', |
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} |
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selected_fields = [] |
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selected_fields += personal_info |
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selected_fields += medical_history |
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selected_fields += clinical_presentation |
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selected_fields += medical_assessment |
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selected_fields += diagnosis |
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selected_fields += treatment |
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selected_fields += patient_outcome |
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return generate_html_tables(data, selected_fields) |
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with gr.Blocks() as demo: |
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with gr.Column(): |
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gr.HTML(""" |
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<div align="center"> |
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<img src="https://huggingface.co/spaces/gregorlied/medical-text-summarization/resolve/main/assets/LlamaMD-logo.png" alt="LlamaMD Logo" width="120" style="margin-bottom: 10px;"> |
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<h2><strong>LlamaMD</strong></h2> |
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<p><em>Structured Information Extraction from Clinical Reports</em></p> |
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</div> |
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""") |
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with gr.Tabs(): |
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with gr.Tab("LLamaMD"): |
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with gr.Row(): |
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input_text = gr.Textbox( |
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label="Clinical Report", |
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autoscroll=False, |
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lines=15, |
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max_lines=15, |
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placeholder="Paste your clinical report here...", |
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value=default_value, |
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) |
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with gr.Accordion("Advanced Settings", open=False): |
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with gr.Row(): |
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with gr.Column(): |
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personal_info = gr.CheckboxGroup( |
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label="Personal Information", |
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choices=[ |
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"Age", |
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"Gender", |
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"Lifestyle", |
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"Social Background", |
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], |
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value=[ |
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"Age", |
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"Gender", |
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"Lifestyle", |
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"Social Background", |
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], |
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) |
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with gr.Column(): |
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medical_history = gr.CheckboxGroup( |
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label="Medical History", |
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choices=[ |
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"Personal", |
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"Family Members", |
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], |
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value=[ |
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"Personal", |
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"Family Members", |
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], |
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) |
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with gr.Row(): |
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with gr.Column(): |
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clinical_presentation = gr.CheckboxGroup( |
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label="Clinical Presentation", |
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choices=[ |
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"Symptoms", |
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"Comorbid Conditions", |
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], |
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value=[ |
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"Symptoms", |
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"Comorbid Conditions", |
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], |
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) |
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with gr.Column(): |
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medical_assessment = gr.CheckboxGroup( |
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label="Medical Assessment", |
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choices=[ |
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"Diagnostic Procedures", |
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"Laboratory Results", |
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"Pathology Report", |
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], |
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value=[ |
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"Diagnostic Procedures", |
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"Laboratory Results", |
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"Pathology Report", |
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], |
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) |
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with gr.Row(): |
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with gr.Column(): |
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diagnosis = gr.CheckboxGroup( |
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label="Diagnosis", |
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choices=[ |
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"Diagnosis", |
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], |
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value=[ |
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"Diagnosis", |
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], |
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) |
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with gr.Column(): |
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treatment = gr.CheckboxGroup( |
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label="Treatment", |
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choices=[ |
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"Interventional Therapy", |
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"Pharmacological Therapy", |
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], |
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value=[ |
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"Interventional Therapy", |
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"Pharmacological Therapy", |
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], |
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) |
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with gr.Column(): |
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patient_outcome = gr.CheckboxGroup( |
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label="Patient Outcome", |
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choices=[ |
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"Patient Outcome", |
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], |
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value=[ |
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"Patient Outcome", |
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], |
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) |
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with gr.Row(): |
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summarize_btn = gr.Button("Extract") |
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with gr.Row(): |
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output_text = gr.HTML() |
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summarize_btn.click( |
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fn=summarize, |
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inputs=[input_text, personal_info, medical_history, clinical_presentation, medical_assessment, diagnosis, treatment, patient_outcome], |
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outputs=output_text, |
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show_progress=True, |
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) |
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with gr.Tab("Help"): |
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gr.Markdown("""## Help |
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### Personal Information |
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**Age**: Age of the patient.<br> |
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**Gender**: Gender of the patient.<br> |
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**Lifestyle**: Daily habits and activities of the patient (e.g. alcohol consumption, diet, smoking status).<br> |
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**Social Background**: Social factors of the patient (e.g. housing situation, marital status).<br> |
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### Medical History |
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**Personal**: Past medical conditions, previous surgeries or treatments of the patient.<br> |
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**Family Members**: Relevant medical conditions or genetic disorders in the patient’s family (e.g. cancer, heart disease).<br> |
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### Clinical Presentation |
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**Symptoms**: Current symptoms of the patient.<br> |
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**Comorbid Conditions**: Other medical conditions of the patient that may influence the treatment.<br> |
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### Medical Assessment |
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**Diagnostic Procedures**: Description of the diagnostic tests or procedures performed (e.g. X-rays, MRIs)<br> |
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**Laboratory Results**: Results foom laboratory test (e.g. blood counts, electrolyte levels)<br> |
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**Pathology Report**: Findings from pathological examinations (e.g. biopsy results)<br> |
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### Diagnosis |
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**Diagnosis**: All levels of diagnosis mentioned in the report.<br> |
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### Treatment |
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**Interventional Therapy**: Medications prescribed to the patient.<br> |
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**Pharmacological Therapy**: Information on surgical or non-surgical interventions performed.<br> |
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### Patient Outcome |
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**Patient Outcome**: Evaluation of the patient’s health status at the end of treatment.<br> |
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""") |
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with gr.Tab("About"): |
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gr.Markdown("""## About |
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LlamaMD is a project developed as part of the "NLP for Social Good" course at TU Berlin. |
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The goal of this project is to perform structured information extraction from clinical reports, helping doctors to have more time for their patients. |
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The system is based on `meta-llama/Llama-3.2-1B-Instruct`, which has been fine-tuned on the ELMTEX dataset. |
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""") |
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if __name__ == "__main__": |
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demo.launch() |
