"""
app.py โ Harivaidya Web Demo
A Gradio web interface that demonstrates:
1. PHI anonymization (patient safety)
2. Medical entity extraction (AI understanding)
3. Clinical severity assessment (rule-based triage)
Design philosophy:
- Severity banner FIRST (most important output)
- Show the JOURNEY: PHI stripped โ entities found โ severity assessed
- Everything visible, nothing hidden
To run locally:
uv run python app.py
Opens http://localhost:7860
๐๏ธ Dedicated to Lord Hari and Lord Vaidyanath
For every patient who ever held a report
and did not know what it meant.
"""
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent / "src"))
import gradio as gr
from harivaidya.anonymizer import PHIAnonymizer
from harivaidya.ner_pipeline import MedicalNERPipeline
from harivaidya.clinical_rules import ClinicalRuleEngine
# โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
# INITIALIZE MODELS (load once at startup)
# โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
print("๐๏ธ Initializing Harivaidya...")
print(" Loading anonymizer...")
anonymizer = PHIAnonymizer()
print(" Loading NER model (first time downloads ~250MB)...")
ner = MedicalNERPipeline()
ner.load()
print(" Loading clinical rule engine...")
rule_engine = ClinicalRuleEngine()
print("โ
Harivaidya ready.\n")
# โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
# MAIN ANALYSIS FUNCTION
# โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
def analyze_report(report_text: str):
"""
Returns 4 outputs:
1. Severity banner (HTML)
2. Anonymized text
3. Entity table
4. Summary markdown
"""
if not report_text or not report_text.strip():
return (
"โช Please paste a medical report to analyze.
",
"",
[],
"No analysis performed.",
)
# Layer 1: Strip PHI
anon_result = anonymizer.anonymize(report_text)
# Layer 2: Run NER
ner_result = ner.extract(anon_result.anonymized_text)
# Layer 3: Clinical rules
entity_texts = [e.text for e in ner_result.entities]
assessment = rule_engine.assess(
text=anon_result.anonymized_text,
entities=entity_texts,
)
# Build severity banner
severity_emojis = {
"CRITICAL": "๐จ",
"MODERATE": "โ ๏ธ",
"MILD": "๐",
"NORMAL": "โ
",
}
severity_colors = {
"CRITICAL": "#CC0000",
"MODERATE": "#FF9900",
"MILD": "#FFCC00",
"NORMAL": "#00AA00",
}
emoji = severity_emojis.get(assessment.severity.value, "โช")
color = severity_colors.get(assessment.severity.value, "#888888")
severity_banner = f"""
{emoji}
{assessment.severity.value}
{assessment.action}
Why: {assessment.reasoning}
"""
# Build entity table
entity_rows = []
for entity in ner_result.entities:
entity_rows.append([
entity.text,
entity.label.replace("_", " ").title(),
f"{entity.confidence:.1%}",
])
# Build summary
red_flag_text = ""
if assessment.red_flags:
red_flag_text = f"\n- **๐ฉ RED FLAGS:** {', '.join(assessment.red_flags)}"
summary = f"""
**๐ Analysis Summary**
- **Severity:** {assessment.severity.value}
- **Trigger findings:** {', '.join(assessment.trigger_findings) if assessment.trigger_findings else 'None'}{red_flag_text}
- **PHI items stripped:** {anon_result.phi_count}
- **Medical entities found:** {ner_result.total_entities}
- **Processing time:** {ner_result.processing_time_ms:.0f} ms
- **Audit hash:** `{anon_result.content_hash}`
**๐ Privacy guarantee:** The anonymized text above is what the AI actually analyzed.
Your original patient data never touched the AI model.
๐๏ธ *Powered by Harivaidya โ Hari + Vaidyanath, the Divine Physician.*
"""
return (
severity_banner,
anon_result.anonymized_text,
entity_rows,
summary,
)
# โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
# EXAMPLES
# โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
EXAMPLE_REPORTS = [
["""Patient Name: Ramesh Kumar Sharma
Aadhaar: 1234 5678 9012
Phone: +91 9876543210
Date: 15/11/2024
Ref. Doctor: Dr. Priya Singh
FINDINGS: Right lower lobe consolidation with mild cardiomegaly.
CTR 0.55. No pleural effusion.
IMPRESSION: Pneumonia in right lower lobe. Mild cardiomegaly."""],
["""Patient: Suresh Patel
Phone: 9988776655
Date: 10/01/2024
BLOOD TEST RESULTS:
HbA1c: 8.3% (diabetic range)
Fasting Glucose: 187 mg/dL
Hemoglobin: 9.8 g/dL (low)
Creatinine: 1.4 mg/dL (mildly elevated)
INTERPRETATION: Poorly controlled diabetes with
progressive anaemia and early kidney involvement."""],
["""IMPRESSION: Bilateral pneumothorax with mediastinal shift.
Severe respiratory compromise. Emergency chest tube placement required.
Blood pressure dropping โ likely tension pneumothorax."""],
["""IMPRESSION: Normal chest X-ray. Clear lung fields.
No abnormalities detected. Routine screening."""],
]
# โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
# GRADIO UI
# โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
custom_css = """
.gradio-container {
max-width: 1200px !important;
font-family: 'Segoe UI', Tahoma, sans-serif;
}
#title {
text-align: center;
background: linear-gradient(135deg, #FF9933, #138808);
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
font-size: 2.2em;
font-weight: bold;
}
"""
with gr.Blocks(title="Harivaidya โ Medical AI for India", css=custom_css) as demo:
# Header
gr.HTML("""
๐๏ธ Harivaidya โ เคนเคฐเคฟเคตเฅเคฆเฅเคฏ
AI Medical Report Analyzer for India
Hari (Narayan) + Vaidyanath (Shiva) โ The Divine Physician
""")
# Input
gr.Markdown("### ๐ Paste a medical report below")
gr.Markdown(
"Try an example below, or paste any X-ray, blood test, or clinical report. "
"Patient data will be automatically stripped before AI analysis."
)
input_text = gr.Textbox(
label="Medical report text",
lines=12,
placeholder="Paste any medical report here...",
)
analyze_btn = gr.Button("๐ Analyze with Harivaidya", variant="primary", size="lg")
# SEVERITY BANNER (most important output, shown first)
gr.Markdown("---")
gr.Markdown("### ๐ฏ Clinical Severity Assessment")
output_severity = gr.HTML()
# Anonymized text
gr.Markdown("---")
gr.Markdown("### ๐ Anonymized Text (PHI Stripped)")
gr.Markdown("This is what the AI actually sees โ all patient identifiers replaced.")
output_anon = gr.Textbox(
label="Anonymized version",
lines=10,
interactive=False,
)
# Entity table
gr.Markdown("### ๐ง Medical Entities Extracted")
gr.Markdown("Our AI found these medical concepts automatically.")
output_entities = gr.Dataframe(
headers=["Entity", "Category", "Confidence"],
label="Medical entities",
wrap=True,
)
# Summary
gr.Markdown("### ๐ Summary")
output_summary = gr.Markdown()
# Examples
gr.Examples(
examples=EXAMPLE_REPORTS,
inputs=input_text,
label="๐ก Try these example reports",
)
# Wire up the button
analyze_btn.click(
fn=analyze_report,
inputs=[input_text],
outputs=[output_severity, output_anon, output_entities, output_summary],
)
# Footer
gr.Markdown("""
---
**๐๏ธ About Harivaidya**
- Built with love for the people of India ๐ฎ๐ณ
- Open source: [github.com/bapuoldtown/harivaidya](https://github.com/bapuoldtown/harivaidya)
- Privacy first: PHI stripped before any AI sees your data
- Free for patients, sustainable for hospitals
- Dedicated to Lord Jagannath and Lord Lingaraj of Odisha
""")
# โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
# LAUNCH
# โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
if __name__ == "__main__":
demo.launch(
server_name="0.0.0.0",
server_port=7860,
share=False,
)