"""
Demo: Option B Pipeline Flow (Without Real Data)

Shows exactly how Option B processes your physician query
"""

import json
from datetime import datetime

print("=" * 80)
print("OPTION B PIPELINE DEMO")
print("=" * 80)
print()

# Your test query
query = "what should a physician considering prescribing ianalumab for sjogren's disease know"

print(f"📝 PHYSICIAN QUERY:")
print(f"   {query}")
print()

# ===========================================================================
# STEP 1: QUERY PARSER LLM (Llama-70B)
# ===========================================================================
print("=" * 80)
print("STEP 1: QUERY PARSER LLM (Llama-70B)")
print("=" * 80)
print("⏱️  Time: ~3 seconds")
print("💰 Cost: $0.001")
print()

# Simulated LLM response
parsed_entities = {
    "drugs": [
        "ianalumab",
        "VAY736",  # Research code for ianalumab
        "anti-BAFF-R antibody"
    ],
    "diseases": [
        "Sjögren's syndrome",
        "Sjögren syndrome",
        "Sjogren's disease",
        "Sjogren disease",
        "primary Sjögren's syndrome",
        "sicca syndrome"
    ],
    "companies": [
        "Novartis",  # Ianalumab manufacturer
        "Novartis Pharmaceuticals"
    ],
    "endpoints": [
        "safety",
        "efficacy",
        "dosing",
        "contraindications",
        "clinical outcomes"
    ],
    "search_terms": "ianalumab VAY736 Sjögren syndrome Sjogren disease efficacy safety prescribing"
}

print("🔍 EXTRACTED ENTITIES:")
print(f"   Drugs:      {parsed_entities['drugs']}")
print(f"   Diseases:   {parsed_entities['diseases'][:3]}...")  # Show first 3
print(f"   Companies:  {parsed_entities['companies']}")
print(f"   Endpoints:  {parsed_entities['endpoints']}")
print()
print(f"🎯 OPTIMIZED SEARCH QUERY:")
print(f"   {parsed_entities['search_terms']}")
print()

# ===========================================================================
# STEP 2: RAG SEARCH (BM25 + Semantic + Inverted Index)
# ===========================================================================
print("=" * 80)
print("STEP 2: RAG SEARCH")
print("=" * 80)
print("⏱️  Time: ~2 seconds")
print("💰 Cost: $0 (local)")
print()

# Simulated search results
print("🔎 SEARCH PROCESS:")
print("   1. Inverted Index: Found 'ianalumab' in 8 trials (O(1) lookup)")
print("   2. Semantic Search: Computed similarity for 500,000+ trials")
print("   3. Hybrid Scoring: Combined keyword + semantic scores")
print()

candidate_trials = [
    {
        "nct_id": "NCT02962895",
        "title": "A Randomized, Double-blind, Placebo-controlled Study of Ianalumab in Patients With Sjögren's Syndrome",
        "hybrid_score": 0.856,
        "snippet": "Phase 2 study evaluating efficacy and safety of ianalumab (VAY736) in primary Sjögren's syndrome..."
    },
    {
        "nct_id": "NCT03334851",
        "title": "Extension Study of Ianalumab in Sjögren's Syndrome",
        "hybrid_score": 0.823,
        "snippet": "Open-label extension to evaluate long-term safety and efficacy of ianalumab in Sjögren's syndrome..."
    },
    {
        "nct_id": "NCT02808364",
        "title": "Safety and Tolerability Study of Ianalumab in Sjögren's Syndrome",
        "hybrid_score": 0.791,
        "snippet": "Phase 2a study assessing safety, tolerability, and pharmacokinetics of ianalumab..."
    }
]

print(f"✅ FOUND: {len(candidate_trials)} highly relevant trials")
print()
for i, trial in enumerate(candidate_trials, 1):
    print(f"   {i}. {trial['nct_id']}")
    print(f"      Hybrid Score: {trial['hybrid_score']:.3f}")
    print(f"      {trial['title'][:80]}...")
    print()

# ===========================================================================
# STEP 3: 355M PERPLEXITY RANKING
# ===========================================================================
print("=" * 80)
print("STEP 3: 355M PERPLEXITY RANKING")
print("=" * 80)
print("⏱️  Time: ~2-5 seconds (GPU) or ~15-30 seconds (CPU)")
print("💰 Cost: $0 (local model)")
print()

print("🧠 355M CLINICAL TRIAL GPT ANALYSIS:")
print("   For each trial, calculates: 'How natural is this query-trial pairing?'")
print()

# Simulated perplexity scores
ranked_trials = [
    {
        **candidate_trials[0],
        "perplexity": 12.4,  # Lower = more relevant
        "perplexity_score": 0.890,
        "combined_score": 0.923,  # 70% hybrid + 30% perplexity
        "rank_before": 1,
        "rank_after": 1
    },
    {
        **candidate_trials[1],
        "perplexity": 15.8,
        "perplexity_score": 0.863,
        "combined_score": 0.893,
        "rank_before": 2,
        "rank_after": 2
    },
    {
        **candidate_trials[2],
        "perplexity": 18.2,
        "perplexity_score": 0.846,
        "combined_score": 0.871,
        "rank_before": 3,
        "rank_after": 3
    }
]

for i, trial in enumerate(ranked_trials, 1):
    print(f"   {i}. {trial['nct_id']}")
    print(f"      Perplexity:     {trial['perplexity']:.1f} (lower = better)")
    print(f"      Hybrid Score:   {trial['hybrid_score']:.3f}")
    print(f"      Combined Score: {trial['combined_score']:.3f}")
    print(f"      Rank: {trial['rank_before']} → {trial['rank_after']}")
    print()

# ===========================================================================
# STEP 4: STRUCTURED JSON OUTPUT
# ===========================================================================
print("=" * 80)
print("STEP 4: STRUCTURED JSON OUTPUT")
print("=" * 80)
print("⏱️  Time: instant")
print("💰 Cost: $0")
print()

# Final structured response
final_response = {
    "query": query,
    "processing_time": 8.2,
    "query_analysis": {
        "extracted_entities": parsed_entities,
        "optimized_search": parsed_entities['search_terms'],
        "parsing_time": 3.1
    },
    "results": {
        "total_found": len(candidate_trials),
        "returned": len(ranked_trials),
        "top_relevance_score": ranked_trials[0]['combined_score']
    },
    "trials": [
        {
            "nct_id": trial['nct_id'],
            "title": trial['title'],
            "status": "Completed",
            "phase": "Phase 2",
            "conditions": "Primary Sjögren's Syndrome",
            "interventions": "Ianalumab (VAY736)",
            "sponsor": "Novartis Pharmaceuticals",
            "enrollment": "160 participants",
            "primary_outcome": "Change in ESSDAI score at Week 24",
            "description": trial['snippet'],
            "scoring": {
                "relevance_score": trial['combined_score'],
                "hybrid_score": trial['hybrid_score'],
                "perplexity": trial['perplexity'],
                "perplexity_score": trial['perplexity_score'],
                "rank_before_355m": trial['rank_before'],
                "rank_after_355m": trial['rank_after'],
                "ranking_method": "355m_perplexity"
            },
            "url": f"https://clinicaltrials.gov/study/{trial['nct_id']}"
        }
        for trial in ranked_trials
    ],
    "benchmarking": {
        "query_parsing_time": 3.1,
        "rag_search_time": 2.3,
        "355m_ranking_time": 2.8,
        "total_processing_time": 8.2
    }
}

print("📦 STRUCTURED JSON RESPONSE:")
print(json.dumps(final_response, indent=2)[:1000] + "...")
print()

# ===========================================================================
# WHAT THE CLIENT DOES WITH THIS DATA
# ===========================================================================
print("=" * 80)
print("WHAT CHATBOT COMPANIES DO WITH THIS JSON")
print("=" * 80)
print()

print("🤖 CLIENT'S LLM (GPT-4, Claude, etc.) GENERATES:")
print()
print("─" * 80)
print("PHYSICIAN RESPONSE (Generated by Client's LLM):")
print("─" * 80)
print()
print("Based on current clinical trial data, physicians considering prescribing")
print("ianalumab for Sjögren's disease should be aware of the following:")
print()
print("**Clinical Evidence:**")
print(f"- {len(ranked_trials)} major clinical trials have evaluated ianalumab in Sjögren's syndrome")
print()
print("**Primary Trial (NCT02962895):**")
print("- Phase 2, randomized, double-blind, placebo-controlled study")
print("- 160 participants with primary Sjögren's syndrome")
print("- Primary endpoint: Change in ESSDAI (disease activity) score at Week 24")
print("- Status: Completed")
print("- Sponsor: Novartis Pharmaceuticals")
print()
print("**Drug Information:**")
print("- Generic name: Ianalumab")
print("- Research code: VAY736")
print("- Mechanism: Anti-BAFF-R (B-cell activating factor receptor) antibody")
print()
print("**Key Considerations:**")
print("1. Safety profile from completed Phase 2 trials available")
print("2. Long-term extension study (NCT03334851) provides extended safety data")
print("3. Efficacy measured by ESSDAI score reduction")
print("4. Appropriate for patients with primary Sjögren's syndrome")
print()
print("**Additional Resources:**")
print(f"- NCT02962895: https://clinicaltrials.gov/study/NCT02962895")
print(f"- NCT03334851: https://clinicaltrials.gov/study/NCT03334851")
print(f"- NCT02808364: https://clinicaltrials.gov/study/NCT02808364")
print()
print("**Note:** This information is based on clinical trial data. Please refer")
print("to the complete prescribing information and consult current clinical")
print("guidelines before prescribing.")
print("─" * 80)
print()

# ===========================================================================
# SUMMARY
# ===========================================================================
print("=" * 80)
print("OPTION B SUMMARY")
print("=" * 80)
print()
print("✅ WHAT OPTION B PROVIDES:")
print("   • Fast query parsing with entity extraction (Llama-70B)")
print("   • Accurate trial retrieval (Hybrid RAG)")
print("   • Clinical relevance ranking (355M perplexity)")
print("   • Structured JSON output with all trial data")
print()
print("⏱️  TOTAL TIME: ~8 seconds (with GPU) or ~20-25 seconds (CPU)")
print("💰 TOTAL COST: $0.001 per query")
print()
print("❌ WHAT OPTION B DOESN'T DO:")
print("   • Does NOT generate text responses")
print("   • Does NOT use 355M for text generation (prevents hallucinations)")
print("   • Does NOT include 3-agent orchestration")
print()
print("🎯 WHY THIS IS PERFECT:")
print("   • Chatbot companies control response generation")
print("   • Your API focuses on accurate search & ranking")
print("   • Fast, cheap, and reliable")
print("   • No hallucinations (355M only scores, doesn't generate)")
print()
print("=" * 80)

# Save to file
with open("demo_option_b_output.json", "w") as f:
    json.dump(final_response, f, indent=2)

print()
print(f"💾 Full JSON response saved to: demo_option_b_output.json")
print()