app.py

import os
import spaces
import gradio as gr

import torch
from peft import PeftModel
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
from huggingface_hub import login as hf_login

import xgrammar as xgr
from pydantic import BaseModel

hf_login(token=os.getenv("HF_TOKEN"))

model_name = "meta-llama/Llama-3.2-1B-Instruct"
model_name = "gregorlied/Llama-3.2-1B-Instruct-Medical-Report-Summarization"
device = 'cuda' if torch.cuda.is_available() else 'cpu'

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    torch_dtype=torch.bfloat16,
    attn_implementation='eager',
    trust_remote_code=True,
)

# checkpoint = "gregorlied/Llama-3.2-1B-Instruct-Medical-Report-Summarization"
# model = PeftModel.from_pretrained(model, checkpoint)
# model = model.merge_and_unload()

class Person(BaseModel):
  life_style: str
  family_history: str
  social_history: str
  medical_surgical_history: str
  signs_symptoms: str
  comorbidities: str
  diagnostic_techniques_procedures: str
  diagnosis: str
  laboratory_values: str
  pathology: str
  pharmacological_therapy: str
  interventional_therapy: str
  patient_outcome_assessment: str
  age: str
  gender: str
    
config = AutoConfig.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

tokenizer_info = xgr.TokenizerInfo.from_huggingface(
    tokenizer, vocab_size=config.vocab_size
)

grammar_compiler = xgr.GrammarCompiler(tokenizer_info)
compiled_grammar = grammar_compiler.compile_json_schema(Person)
xgr_logits_processor = xgr.contrib.hf.LogitsProcessor(compiled_grammar)

prompt = """You are a text extraction system for clinical reports.
Please extract relevant clinical information from the report.

### Instructions

- Use the JSON Schema given below.
- Return only a valid JSON object – no markdown, no comments.
- If no relevant facts are given for a field, set its value to "N/A".
- If multile relevant facts are given for a field, separate them with "; ".

### JSON Schema

{
  'life_style': '',
  'family_history': '',
  'social_history': '',
  'medical_surgical_history': '',
  'signs_symptoms': '',
  'comorbidities': '',
  'diagnostic_techniques_procedures': '',
  'diagnosis': '',
  'laboratory_values': '',
  'pathology': '',
  'pharmacological_therapy': '',
  'interventional_therapy': '',
  'patient_outcome_assessment': '',
  'age': '',
  'gender': '',
}

### Clinical Report
"""

@spaces.GPU(duration=60)
def summarize(text):
    if not text.strip():
        return "Please enter some text to summarize."

    messages = [
        {"role": "system", "content": prompt},
        {"role": "user", "content": text},
    ]
    
    model_inputs = tokenizer([text], return_tensors="pt").to(device)

    generated_ids = model.generate(
        input_ids=model_inputs["input_ids"],
        attention_mask = model_inputs["attention_mask"],
        # num_beams=8,
        # top_p=0.9,
        # do_sample=True,
        # temperature=0.6,
        # min_new_tokens=50,
        max_new_tokens=2048,
        logits_processor=[xgr_logits_processor]
    )

    generated_ids = [
        output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
    ]
    
    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
    return response[0]

with gr.Blocks() as demo:
    gr.Markdown("## 📝 Structured Information Extraction for Clinical Reports")

    with gr.Row():
        input_text = gr.Textbox(
            label="Clinical Report", 
            autoscroll=False,
            lines=15, 
            max_lines=15, 
            placeholder="Paste your clinical report here...",
        )

    with gr.Row():
        gr.CheckboxGroup(
            label="General Information", 
            choices=[
                "Age",
                "Gender",
                "Life Style", 
                "Social History",
            ]
        )
        gr.CheckboxGroup(
            label="Medical History", 
            choices=[
                "Family History", 
                "Medical Surgical History",
            ]
        )
        gr.CheckboxGroup(
            label="Signs and Symptoms", 
            choices=[
                "Signs and Symptoms",
                "Comorbidities",
            ]
        )

    with gr.Row():
        gr.CheckboxGroup(
            label="Medical Assesments", 
            choices=[
                "Diagnostic Techniques and Procedures",
                "Laboratory Values",
                "Pathology",
                "Diagnosis",                
            ]
        )
        gr.CheckboxGroup(
            label="Therapy and Results", 
            choices=[
                "Pharmacological Therapy",
                "Interventional Therapy",
                "Patient Outcome Assessment",                
            ]
        )

    with gr.Row():
        summarize_btn = gr.Button("Summarize")

    with gr.Row():
        output_text = gr.Textbox(
            label="Summary", 
            autoscroll=False,
            lines=15, 
            max_lines=15, 
            show_copy_button=True,
        )

    with gr.Row():
        examples = gr.Examples(
            label="Examples",
            examples=[
                "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."
            ],
            fn=summarize,
            inputs=input_text,
            outputs=output_text,
            cache_examples="lazy",
        )

    summarize_btn.click(
        fn=summarize, 
        inputs=input_text, 
        outputs=output_text, 
        show_progress=True,
    )

if __name__ == "__main__":
    demo.launch()