File size: 2,578 Bytes
793d027 85020ae 793d027 82a7e73 793d027 85020ae 793d027 82a7e73 793d027 82a7e73 408e7e3 793d027 | 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 | #!/usr/bin/env python3
"""
Setup script for AMR-Guard Demo
Initializes the database and imports all data.
Drug interactions CSV is sourced from the Kaggle dataset:
https://www.kaggle.com/datasets/mghobashy/drug-drug-interactions
On Kaggle: attach the dataset via "Add data" — it will be mounted automatically.
Locally: place ~/.kaggle/kaggle.json or run:
kaggle datasets download -d mghobashy/drug-drug-interactions --unzip -p docs/drug_safety/
"""
import sys
from pathlib import Path
PROJECT_ROOT = Path(__file__).parent
sys.path.insert(0, str(PROJECT_ROOT))
def main():
print("=" * 60)
print("AMR-Guard Demo Setup")
print("AMR-Guard: Infection Lifecycle Orchestrator")
print("=" * 60)
print()
# Step 1: Import structured data into SQLite
print("Step 1: Importing structured data into SQLite...")
print("-" * 40)
from src.db.import_data import import_all_data
# Limit interactions to 50k for faster demo setup
structured_results = import_all_data(interactions_limit=50000)
# Step 2: Import docs into ChromaDB
print("\nStep 2: Importing documents into ChromaDB (Vector Store)...")
print("-" * 40)
from src.db.vector_store import import_all_vectors
vector_results = import_all_vectors()
# Summary
print("\n" + "=" * 60)
print("Setup Complete!")
print("=" * 60)
print("\nData imported:")
print(f" - EML Antibiotics: {structured_results.get('eml_antibiotics', 0):>6} records")
print(f" - ATLAS Susceptibility:{structured_results.get('atlas_susceptibility', 0):>5} records")
print(f" - MIC Breakpoints: {structured_results.get('mic_breakpoints', 0):>6} records")
print(f" - Drug Interactions: {structured_results.get('drug_interactions', 0):>6} records")
print(f" - IDSA Guidelines: {vector_results.get('idsa_guidelines', 0):>6} chunks")
print(f" - MIC Reference: {vector_results.get('mic_reference', 0):>6} chunks")
if structured_results.get('drug_interactions', 0) == 0:
print()
print(" ⚠ Drug interactions were not imported.")
print(" Dataset: https://www.kaggle.com/datasets/mghobashy/drug-drug-interactions")
print(" • On Kaggle: add the dataset via the notebook UI ('Add data').")
print(" • Locally: kaggle datasets download -d mghobashy/drug-drug-interactions \\")
print(" --unzip -p docs/drug_safety/")
print()
print("To run the app: uv run gradio app.py")
print()
if __name__ == "__main__":
main()
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