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542c765 | 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 | #!/usr/bin/env python3
"""
Complete end-to-end test: Schema → Text → Chat Response
Shows the full document scanning conversion pipeline
"""
import sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).parent))
from app.routers.doctor_upload import convert_analysis_to_text
from app.ml.openrouter import chat
# Sample schema from PDF analysis
sample_analysis = {
"findings": [
{"parameter": "Haemoglobin", "value": 9.2, "unit": "g/dL", "status": "LOW", "reference": "13.0 - 17.0"},
{"parameter": "Iron", "value": 45, "unit": "µg/dL", "status": "LOW", "reference": "60 - 170"},
{"parameter": "Vitamin B12", "value": 180, "unit": "pg/mL", "status": "LOW", "reference": "200 - 900"},
{"parameter": "RBC", "value": 3.8, "unit": "10^6/µL", "status": "LOW", "reference": "4.5 - 5.5"},
{"parameter": "WBC", "value": 7.2, "unit": "10^3/µL", "status": "NORMAL", "reference": "4.5 - 11.0"}
],
"severity_level": "NORMAL",
"affected_organs": ["Blood", "Bone Marrow"],
"dietary_flags": ["Low Iron Intake", "Insufficient B12"],
"exercise_flags": ["Fatigue Limiting Activity"]
}
def print_section(title):
print("\n" + "=" * 90)
print(f" {title}")
print("=" * 90)
def test_full_pipeline():
"""Test the complete schema → text → chat response pipeline"""
print_section("1️⃣ INPUT: Schema-Based PDF Analysis")
print("\nFindings from PDF extraction:")
for finding in sample_analysis["findings"]:
status_color = "❌" if finding['status'] != "NORMAL" else "✓"
print(f" {status_color} {finding['parameter']}: {finding['value']} {finding['unit']} ({finding['status']})")
print(f"\n Severity: {sample_analysis['severity_level']}")
print(f" Affected: {', '.join(sample_analysis['affected_organs'])}")
print(f" Dietary: {', '.join(sample_analysis['dietary_flags'])}")
print(f" Exercise: {', '.join(sample_analysis['exercise_flags'])}")
print_section("2️⃣ CONVERSION: Schema → Natural Language Text")
analysis_text = convert_analysis_to_text(sample_analysis)
print(analysis_text)
print_section("3️⃣ PROCESSING: Text → Chat System")
print("\nSending text through chat system with patient context...")
# Build patient context
patient_context = {
"name": "Amit Kumar",
"age": "28",
"gender": "Male",
"language": "EN",
"latestReport": sample_analysis,
"mentalWellness": {"stressLevel": 6, "sleepQuality": 5}
}
# Send through chat system
print("Processing...\n")
doctor_response = chat(
message=analysis_text,
history=[],
guc=patient_context
)
print_section("4️⃣ OUTPUT: Doctor Response (Natural Language)")
print(f"\nDr. Raahat:\n{doctor_response}")
print_section("✅ PIPELINE COMPLETE")
print("""
Flow Summary:
┌─────────────────────────────────────────────────────────┐
│ 1. PDF Upload │
│ ↓ │
│ 2. PDF Analysis (Schema-Based) │
│ ↓ │
│ 3. Schema → Natural Language Text Conversion │
│ ↓ │
│ 4. Send Text to Chat System │
│ ↓ │
│ 5. Chat System Processes & Returns Response │
│ ↓ │
│ 6. Doctor's Human-Friendly Response │
└─────────────────────────────────────────────────────────┘
Key Innovation:
- Schema analysis receives proper natural language processing
- Doctor responses are contextual to actual findings
- No raw JSON schema returned - only human dialogue
- Seamless user experience in `/upload_and_chat` endpoint
""")
if __name__ == "__main__":
test_full_pipeline()
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