Spaces:
Running
Running
File size: 27,323 Bytes
c72cb4c 32dd65f c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c e321c3e c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c e2ce928 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 e2ce928 82d1193 e2ce928 c72cb4c 82d1193 e2ce928 82d1193 e2ce928 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 82d1193 c72cb4c 32dd65f c72cb4c 32dd65f c72cb4c 82d1193 c72cb4c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 |
"""
Beautiful Medical NER Demo using OpenMed Models
A comprehensive Named Entity Recognition demo for medical professionals
featuring multiple specialized medical models with beautiful entity visualization.
"""
import gradio as gr
import spacy
from spacy import displacy
from transformers import pipeline
import warnings
import logging
import re
from typing import Dict, List, Tuple
import random
# Suppress warnings for cleaner output
warnings.filterwarnings("ignore")
logging.getLogger("transformers").setLevel(logging.ERROR)
# Model configurations
MODELS = {
"Oncology Detection": {
"model_id": "OpenMed/OpenMed-NER-OncologyDetect-SuperMedical-355M",
"description": "Specialized in cancer, genetics, and oncology entities",
},
# "Pharmaceutical Detection": {
# "model_id": "OpenMed/OpenMed-NER-PharmaDetect-SuperClinical-434M",
# "description": "Detects drugs, chemicals, and pharmaceutical entities",
# },
# "Disease Detection": {
# "model_id": "OpenMed/OpenMed-NER-DiseaseDetect-SuperClinical-434M",
# "description": "Identifies diseases, conditions, and pathologies",
# },
# "Genome Detection": {
# "model_id": "OpenMed/OpenMed-NER-GenomeDetect-ModernClinical-395M",
# "description": "Recognizes genes, proteins, and genomic entities",
# },
}
# Medical text examples for each model
EXAMPLES = {
"Oncology Detection": [
"The patient presented with metastatic adenocarcinoma of the lung with mutations in EGFR and KRAS genes. Treatment with erlotinib was initiated, targeting the epidermal growth factor receptor pathway.",
"Histological examination revealed invasive ductal carcinoma with high-grade nuclear features. The tumor showed positive estrogen receptor and HER2 amplification, indicating potential for targeted therapy.",
"The oncologist recommended adjuvant chemotherapy with doxorubicin and cyclophosphamide, followed by paclitaxel, to target rapidly dividing cancer cells in the breast tissue.",
],
"Pharmaceutical Detection": [
"The patient was prescribed metformin 500mg twice daily for diabetes management, along with lisinopril 10mg for hypertension control and atorvastatin 20mg for cholesterol reduction.",
"Administration of morphine sulfate provided effective pain relief, while ondansetron prevented chemotherapy-induced nausea. The patient also received dexamethasone as an anti-inflammatory agent.",
"The pharmacokinetic study evaluated the absorption of ibuprofen and its interaction with warfarin, monitoring plasma concentrations and potential bleeding risks.",
],
"Disease Detection": [
"The patient was diagnosed with type 2 diabetes mellitus, hypertension, and coronary artery disease. Additional findings included diabetic nephropathy and peripheral neuropathy.",
"Clinical presentation was consistent with acute myocardial infarction complicated by cardiogenic shock. The patient also had a history of chronic obstructive pulmonary disease and atrial fibrillation.",
"Laboratory results confirmed the diagnosis of rheumatoid arthritis with elevated inflammatory markers. The patient also exhibited symptoms of Sjögren's syndrome and osteoporosis.",
],
"Genome Detection": [
"Genetic analysis revealed mutations in the BRCA1 and BRCA2 genes, significantly increasing the risk of hereditary breast and ovarian cancer. The p53 tumor suppressor gene also showed alterations.",
"Expression profiling identified upregulation of MYC oncogene and downregulation of PTEN tumor suppressor. The mTOR signaling pathway showed significant activation in the tumor samples.",
"Whole genome sequencing detected variants in CFTR gene associated with cystic fibrosis, along with polymorphisms in CYP2D6 affecting drug metabolism and APOE influencing Alzheimer's risk.",
],
}
def ner_filtered(text, *, pipe, min_score=0.60, min_length=1, remove_punctuation=True):
"""
Apply confidence and punctuation filtering to NER pipeline results.
This is the proven filtering approach that eliminates spurious predictions.
"""
# 1️⃣ Run the NER model
raw_entities = pipe(text)
# 2️⃣ Define regex for content detection
if remove_punctuation:
has_content = re.compile(r"[A-Za-z0-9]") # At least one letter or digit
else:
has_content = re.compile(r".") # Allow everything
# 3️⃣ Apply filters
filtered_entities = []
for entity in raw_entities:
# Confidence filter
if entity["score"] < min_score:
continue
# Length filter
if len(entity["word"].strip()) < min_length:
continue
# Punctuation filter
if remove_punctuation and not has_content.search(entity["word"]):
continue
filtered_entities.append(entity)
return filtered_entities
def advanced_ner_filter(text, *, pipe, min_score=0.60, strip_edges=True, exclude_patterns=None):
"""
Advanced filtering with edge stripping and pattern exclusion.
"""
entities = pipe(text)
filtered = []
for entity in entities:
if entity["score"] < min_score:
continue
word = entity["word"]
# Strip punctuation from edges
if strip_edges:
stripped = word.strip(".,!?;:()[]{}\"'-_")
if not stripped:
continue
entity = entity.copy()
entity["word"] = stripped
# Apply exclusion patterns
if exclude_patterns:
skip = any(re.match(pattern, entity["word"]) for pattern in exclude_patterns)
if skip:
continue
# Only keep entities with actual content
if re.search(r"[A-Za-z0-9]", entity["word"]):
filtered.append(entity)
return filtered
def merge_adjacent_entities(entities, original_text, max_gap=10):
"""
Merge adjacent entities of the same type that are separated by small gaps.
Useful for handling cases like "BRCA1 and BRCA2" or "HER2-positive".
"""
if len(entities) < 2:
return entities
merged = []
current = entities[0].copy()
for next_entity in entities[1:]:
# Check if same entity type and close proximity
if (current["entity_group"] == next_entity["entity_group"] and
next_entity["start"] - current["end"] <= max_gap):
# Check what's between them
gap_text = original_text[current["end"]:next_entity["start"]]
# Merge if gap contains only connecting words/punctuation
if re.match(r"^[\s\-,/and]*$", gap_text.lower()):
# Extend current entity to include the next one
current["word"] = original_text[current["start"]:next_entity["end"]]
current["end"] = next_entity["end"]
current["score"] = (current["score"] + next_entity["score"]) / 2
continue
# No merge, add current and move to next
merged.append(current)
current = next_entity.copy()
# Don't forget the last entity
merged.append(current)
return merged
class MedicalNERApp:
def __init__(self):
self.pipelines = {}
self.nlp = spacy.blank("en") # SpaCy model for visualization
self.load_models()
def load_models(self):
"""Load and cache all models with proper aggregation strategy"""
print("🏥 Loading Medical NER Models...")
for model_name, config in MODELS.items():
print(f"Loading {model_name}...")
try:
# Use aggregation_strategy=None and handle grouping ourselves for better control
ner_pipeline = pipeline(
"token-classification",
model=config["model_id"],
aggregation_strategy=None, # ← Get raw tokens, group them properly ourselves
device=0 if __name__ == "__main__" else -1 # Use GPU if available
)
self.pipelines[model_name] = ner_pipeline
print(f"✅ {model_name} loaded successfully with custom entity grouping")
except Exception as e:
print(f"❌ Error loading {model_name}: {str(e)}")
self.pipelines[model_name] = None
print("🎉 All models loaded and cached!")
def smart_group_entities(self, tokens, text):
"""
Smart entity grouping that properly merges sub-tokens into complete entities.
This fixes the issue where aggregation_strategy="simple" creates overlapping spans.
"""
if not tokens:
return []
entities = []
current_entity = None
for token in tokens:
label = token['entity']
score = token['score']
word = token['word']
start = token['start']
end = token['end']
# Skip O (Outside) tags
if label == 'O':
if current_entity:
entities.append(current_entity)
current_entity = None
continue
# Clean the label (remove B- and I- prefixes)
clean_label = label.replace('B-', '').replace('I-', '')
# Start new entity (B- tag or different entity type)
if label.startswith('B-') or (current_entity and current_entity['entity_group'] != clean_label):
if current_entity:
entities.append(current_entity)
current_entity = {
'entity_group': clean_label,
'score': score,
'word': text[start:end], # Use actual text from the source
'start': start,
'end': end
}
# Continue current entity (I- tag)
elif current_entity and clean_label == current_entity['entity_group']:
# Extend the current entity
current_entity['end'] = end
current_entity['word'] = text[current_entity['start']:end]
current_entity['score'] = (current_entity['score'] + score) / 2 # Average scores
# Don't forget the last entity
if current_entity:
entities.append(current_entity)
return entities
def create_spacy_visualization(self, text: str, entities: List[Dict], model_name: str) -> str:
"""Create spaCy displaCy visualization with dynamic colors and improved span handling."""
print(f"\n🔍 VISUALIZATION DEBUG for {model_name}")
print(f"Input text length: {len(text)} chars")
print(f"Total entities to visualize: {len(entities)}")
# Show all entities found
print("\n📋 ENTITIES TO VISUALIZE:")
entity_by_type = {}
for i, ent in enumerate(entities):
entity_type = ent['entity_group']
if entity_type not in entity_by_type:
entity_by_type[entity_type] = []
entity_by_type[entity_type].append(ent)
print(f" {i+1:2d}. [{ent['start']:3d}:{ent['end']:3d}] '{ent['word']:25}' -> {entity_type:20} (score: {ent['score']:.3f})")
print(f"\n📊 ENTITY COUNTS BY TYPE:")
for entity_type, ents in entity_by_type.items():
print(f" {entity_type}: {len(ents)} instances")
doc = self.nlp(text)
spacy_ents = []
failed_entities = []
print(f"\n🔧 CREATING SPACY SPANS:")
for i, entity in enumerate(entities):
try:
start = entity['start']
end = entity['end']
label = entity['entity_group']
entity_text = entity['word']
print(f" {i+1:2d}. Trying span [{start}:{end}] '{entity_text}' -> {label}")
# Try to create span with default mode first
span = doc.char_span(start, end, label=label)
if span is not None:
spacy_ents.append(span)
print(f" ✅ SUCCESS: '{span.text}' -> {label}")
else:
# Try different alignment modes
span = doc.char_span(start, end, label=label, alignment_mode="expand")
if span is not None:
spacy_ents.append(span)
print(f" ✅ SUCCESS (expand): '{span.text}' -> {label}")
else:
failed_entities.append(entity)
print(f" ❌ FAILED: Could not create span for '{entity_text}' -> {label}")
except Exception as e:
failed_entities.append(entity)
print(f" 💥 EXCEPTION: {str(e)}")
print(f"\n📈 SPAN CREATION RESULTS:")
print(f" ✅ Successful spans: {len(spacy_ents)}")
print(f" ❌ Failed spans: {len(failed_entities)}")
# Filter overlapping spans (this is much cleaner now)
print(f"\n🔄 FILTERING OVERLAPPING SPANS...")
print(f" Before filtering: {len(spacy_ents)} spans")
spacy_ents = spacy.util.filter_spans(spacy_ents)
print(f" After filtering: {len(spacy_ents)} spans")
doc.ents = spacy_ents
print(f"\n🎨 FINAL VISUALIZATION ENTITIES:")
for ent in doc.ents:
print(f" '{ent.text}' ({ent.label_}) [{ent.start_char}:{ent.end_char}]")
# Define color palette
color_palette = {
"DISEASE": "#FF5733",
"CHEM": "#33FF57",
"GENE/PROTEIN": "#3357FF",
"Cancer": "#FF33F6",
"Cell": "#33FFF6",
"Organ": "#F6FF33",
"Tissue": "#FF8333",
"Simple_chemical": "#8333FF",
"Gene_or_gene_product": "#33FF83",
"Organism": "#FF6B33",
}
unique_labels = sorted(list(set(ent.label_ for ent in doc.ents)))
colors = {}
for label in unique_labels:
if label in color_palette:
colors[label] = color_palette[label]
else:
colors[label] = "#" + ''.join([hex(x)[2:].zfill(2) for x in (random.randint(100, 255), random.randint(100, 255), random.randint(100, 255))])
options = {
"ents": unique_labels,
"colors": colors,
"style": "max-width: 100%; line-height: 2.5; direction: ltr;"
}
print(f"\n🎨 VISUALIZATION CONFIG:")
print(f" Entity types for display: {unique_labels}")
print(f" Color mapping: {colors}")
# Add debug info to the HTML output if there are issues
debug_info = ""
if failed_entities:
debug_info = f"""
<div style="margin-top: 15px; padding: 10px; background: #fff3cd; border: 1px solid #ffeaa7; border-radius: 5px; font-size: 12px;">
<strong>⚠️ Visualization Info:</strong><br>
{len(failed_entities)} entities could not be visualized due to text alignment issues.<br>
All entities are still counted in the summary below.
</div>
"""
displacy_html = displacy.render(doc, style="ent", options=options, page=False)
return displacy_html + debug_info
def predict_entities(self, text: str, model_name: str, confidence_threshold: float = 0.60) -> Tuple[str, str]:
"""
Predict entities using smart grouping for maximum accuracy.
"""
if not text.strip():
return "<p>Please enter medical text to analyze.</p>", "No text provided"
if model_name not in self.pipelines or self.pipelines[model_name] is None:
return f"<p>❌ Model {model_name} is not available.</p>", "Model not available"
try:
print(f"\nDEBUG: Processing text with {model_name}")
print(f"Text: {text}")
print(f"Confidence threshold: {confidence_threshold}")
# Get raw token predictions from the pipeline
pipeline_instance = self.pipelines[model_name]
raw_tokens = pipeline_instance(text)
print(f"Got {len(raw_tokens)} raw tokens from pipeline")
if not raw_tokens:
return "<p>No entities detected.</p>", "No entities found"
# Use our smart grouping to merge sub-tokens into complete entities
grouped_entities = self.smart_group_entities(raw_tokens, text)
print(f"Smart grouping created {len(grouped_entities)} entities")
# Apply confidence filtering to the grouped entities
filtered_entities = []
for entity in grouped_entities:
if entity["score"] >= confidence_threshold:
# Apply additional quality filters
if (len(entity["word"].strip()) > 0 and # Not empty
re.search(r"[A-Za-z0-9]", entity["word"])): # Contains actual content
filtered_entities.append(entity)
print(f"✅ After confidence filtering: {len(filtered_entities)} high-quality entities")
if not filtered_entities:
return f"<p>No entities found with confidence ≥ {confidence_threshold:.0%}. Try lowering the threshold.</p>", "No entities found"
# Create visualization and summary
html_output = self.create_spacy_visualization(text, filtered_entities, model_name)
wrapped_html = self.wrap_displacy_output(html_output, model_name, len(filtered_entities), confidence_threshold)
summary = self.create_summary(filtered_entities, model_name, confidence_threshold)
return wrapped_html, summary
except Exception as e:
import traceback
print(f"ERROR in predict_entities: {str(e)}")
traceback.print_exc()
error_msg = f"Error during prediction: {str(e)}"
return f"<p>❌ {error_msg}</p>", error_msg
def wrap_displacy_output(self, displacy_html: str, model_name: str, entity_count: int, confidence_threshold: float) -> str:
"""Wrap displaCy output in a beautiful container with filtering info."""
return f"""
<div style="font-family: 'Segoe UI', Arial, sans-serif;
border-radius: 10px;
box-shadow: 0 4px 6px rgba(0,0,0,0.1);
overflow: hidden;">
<div style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
color: white; padding: 15px; text-align: center;">
<h3 style="margin: 0; font-size: 18px;">{model_name}</h3>
<p style="margin: 5px 0 0 0; opacity: 0.9; font-size: 14px;">
Found {entity_count} high-confidence medical entities (≥{confidence_threshold:.0%})
</p>
<div style="margin-top: 8px; font-size: 12px; opacity: 0.8;">
✅ Filtered with aggregation_strategy="simple" + confidence threshold
</div>
</div>
<div style="padding: 20px; margin: 0; line-height: 2.5;">
{displacy_html}
</div>
</div>
"""
def create_summary(self, entities: List[Dict], model_name: str, confidence_threshold: float) -> str:
"""Create a summary of detected entities with filtering info."""
if not entities:
return "No entities detected."
entity_counts = {}
for entity in entities:
label = entity["entity_group"]
if label not in entity_counts:
entity_counts[label] = []
entity_counts[label].append(entity)
summary_parts = [f"📊 **{model_name} Analysis Results**\n"]
summary_parts.append(f"**Total high-confidence entities**: {len(entities)} (threshold ≥{confidence_threshold:.0%})\n")
for label, ents in sorted(entity_counts.items()):
avg_confidence = sum(e["score"] for e in ents) / len(ents)
unique_texts = sorted(list(set(e["word"] for e in ents)))
summary_parts.append(
f"• **{label}**: {len(ents)} instances "
f"(avg confidence: {avg_confidence:.2f})\n"
f" Examples: {', '.join(unique_texts[:3])}"
f"{'...' if len(unique_texts) > 3 else ''}\n"
)
# Add filtering information
summary_parts.append("\n🎯 **Accuracy Improvements Applied**\n")
summary_parts.append("✅ Smart BIO token grouping - Properly merges sub-tokens into complete entities\n")
summary_parts.append(f"✅ Confidence threshold filtering - Only entities ≥ {confidence_threshold:.0%} confidence\n")
summary_parts.append("✅ Content validation - Excludes empty or punctuation-only predictions\n")
summary_parts.append("✅ Precise span alignment - Improved text-to-visual mapping\n")
# Add model information
summary_parts.append(f"\n🔬 **Model Information**\n")
summary_parts.append(f"Model: `{MODELS[model_name]['model_id']}`\n")
summary_parts.append(f"Description: {MODELS[model_name]['description']}\n")
return "\n".join(summary_parts)
# Initialize the app
print("🚀 Initializing Medical NER Application...")
ner_app = MedicalNERApp()
# Warmup
print("🔥 Warming up models...")
warmup_text = "The patient has diabetes and takes metformin."
for model_name in MODELS.keys():
if ner_app.pipelines[model_name] is not None:
try:
print(f"Warming up {model_name}...")
_ = ner_app.predict_entities(warmup_text, model_name, 0.60)
print(f"✅ {model_name} warmed up successfully")
except Exception as e:
print(f"⚠️ Warmup failed for {model_name}: {str(e)}")
print("🎉 Model warmup complete!")
def predict_wrapper(text: str, model_name: str, confidence_threshold: float):
"""Wrapper function for Gradio interface with confidence control"""
html_output, summary = ner_app.predict_entities(text, model_name, confidence_threshold)
return html_output, summary
def load_example(model_name: str, example_idx: int):
"""Load example text for the selected model"""
if model_name in EXAMPLES and 0 <= example_idx < len(EXAMPLES[model_name]):
return EXAMPLES[model_name][example_idx]
return ""
# Create Gradio interface
with gr.Blocks(
title="🏥 Medical NER Expert",
theme=gr.themes.Soft(),
css="""
.gradio-container {
font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
}
.main-header {
text-align: center;
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
color: white;
padding: 2rem;
border-radius: 15px;
margin-bottom: 2rem;
box-shadow: 0 8px 32px rgba(0,0,0,0.1);
}
.model-info {
padding: 1rem;
border-radius: 10px;
border-left: 4px solid #667eea;
margin: 1rem 0;
}
.accuracy-badge {
background: #28a745;
color: white;
padding: 4px 8px;
border-radius: 12px;
font-size: 12px;
font-weight: bold;
}
""",
) as demo:
# Header
gr.HTML(
"""
<div class="main-header">
<h1>🏥 Medical NER Expert</h1>
<p>Advanced Named Entity Recognition for Medical Professionals</p>
<div style="margin-top: 10px;">
<span class="accuracy-badge">✅ HIGH ACCURACY MODE</span>
</div>
<p style="font-size: 14px; margin-top: 10px; opacity: 0.9;">
Powered by OpenMed models + proven filtering techniques (aggregation_strategy="simple" + confidence thresholds)
</p>
</div>
"""
)
with gr.Row():
with gr.Column(scale=2):
# Model selection
model_dropdown = gr.Dropdown(
choices=list(MODELS.keys()),
value="Oncology Detection",
label="🔬 Select Medical NER Model",
info="Choose the specialized model for your analysis",
)
# Model info display
model_info = gr.HTML(
value=f"""
<div class="model-info">
<strong>Oncology Detection</strong><br>
{MODELS["Oncology Detection"]["description"]}
</div>
"""
)
# Confidence threshold slider
confidence_slider = gr.Slider(
minimum=0.30,
maximum=0.95,
value=0.60,
step=0.05,
label="🎯 Confidence Threshold",
info="Higher values = fewer but more confident predictions"
)
# Text input
text_input = gr.Textbox(
lines=8,
placeholder="Enter medical text here for entity recognition...",
label="📝 Medical Text Input",
value=EXAMPLES["Oncology Detection"][0],
)
# Example buttons
with gr.Row():
example_buttons = []
for i in range(3):
btn = gr.Button(f"Example {i+1}", size="sm", variant="secondary")
example_buttons.append(btn)
# Analyze button
analyze_btn = gr.Button("🔍 Analyze Text", variant="primary", size="lg")
with gr.Column(scale=3):
# Results
results_html = gr.HTML(
label="🎯 Entity Recognition Results",
value="<p>Select a model and enter text to see entity recognition results.</p>",
)
# Summary
summary_output = gr.Markdown(
value="Analysis summary will appear here...",
label="📊 Analysis Summary",
)
# Update model info when model changes
def update_model_info(model_name):
if model_name in MODELS:
return f"""
<div class="model-info">
<strong>{model_name}</strong><br>
{MODELS[model_name]["description"]}<br>
<small>Model: {MODELS[model_name]["model_id"]}</small>
</div>
"""
return ""
model_dropdown.change(
update_model_info, inputs=[model_dropdown], outputs=[model_info]
)
# Example button handlers
for i, btn in enumerate(example_buttons):
btn.click(
lambda model_name, idx=i: load_example(model_name, idx),
inputs=[model_dropdown],
outputs=[text_input],
)
# Main analysis function
analyze_btn.click(
predict_wrapper,
inputs=[text_input, model_dropdown, confidence_slider],
outputs=[results_html, summary_output],
)
# Auto-update when model changes (load first example)
model_dropdown.change(
lambda model_name: load_example(model_name, 0),
inputs=[model_dropdown],
outputs=[text_input],
)
if __name__ == "__main__":
demo.launch(
share=False,
show_error=True,
server_name="0.0.0.0",
server_port=7860,
)
|