genesis/pipeline.py

# genesis/pipeline.py
import os
import re
from datetime import datetime
from typing import Dict, Any, List

# API client imports
from genesis.api_clients.pubmed_api import search_pubmed_literature
from genesis.api_clients.bioportal_api import expand_with_bioportal
from genesis.api_clients.umls_api import expand_with_umls
from genesis.api_clients.chembl_api import get_molecule_data
from genesis.api_clients.ncbi_api import fetch_ncbi_structure
from genesis.utils.pdf_export import export_report_to_pdf


# Core logic providers
from genesis.providers import (
    run_deepseek_summary,
    run_gemini_summary,
    run_openai_summary,
    run_gemini_image,
    run_openai_image,
    run_hf_image,
    narrate_text_elevenlabs
)
from genesis.utils.pdf_export import export_report_to_pdf
from genesis.visualization import generate_pathway_graph, generate_funding_network
from genesis.funding import fetch_funding_data
from genesis.trials import fetch_clinical_trials
from genesis.biosecurity import analyze_biosecurity_risks
from genesis.regulation import fetch_regulatory_info
from genesis.safety import analyze_safety_concerns
from genesis.structures import fetch_structures_for_terms
from genesis.ontology import merge_ontology_terms
from genesis.utils.graph_tools import write_topic_and_papers

# Environment vars
ELEVEN_LABS_API_KEY = os.getenv("ELEVEN_LABS_API_KEY")
NEO4J_URI = os.getenv("NEO4J_URI")
NEO4J_USER = os.getenv("NEO4J_USER")
NEO4J_PASSWORD = os.getenv("NEO4J_PASSWORD")

SYNBIO_MODE = True  # Bias towards synthetic biology context

# Demo queries for UI preload
DEMO_QUERIES = [
    "CRISPR living therapeutics in clinical trials since 2020",
    "AI-designed enzymes for plastic degradation — literature + pathways",
    "Synthetic biology startups in oncology — funding map",
    "Metabolic pathway for artemisinin biosynthesis in yeast",
    "Oncolytic virus engineering — biosecurity risk analysis"
]


def extract_citations(text: str) -> List[Dict[str, str]]:
    """Extract citations (DOI, PMID, URLs) from text."""
    citations = []
    doi_pattern = r"(10\.\d{4,9}/[-._;()/:A-Z0-9]+)"
    pmid_pattern = r"PMID:\s*(\d+)"
    url_pattern = r"(https?://[^\s)]+)"

    for match in re.finditer(doi_pattern, text, re.IGNORECASE):
        citations.append({"type": "DOI", "id": match.group(1), "url": f"https://doi.org/{match.group(1)}"})
    for match in re.finditer(pmid_pattern, text, re.IGNORECASE):
        citations.append({"type": "PMID", "id": match.group(1), "url": f"https://pubmed.ncbi.nlm.nih.gov/{match.group(1)}/"})
    for match in re.finditer(url_pattern, text, re.IGNORECASE):
        if not any(c["url"] == match.group(1) for c in citations):
            citations.append({"type": "URL", "id": "", "url": match.group(1)})

    return citations


def inject_synbio_context(query: str, expanded_terms: List[str]) -> str:
    """Injects synthetic biology expertise into the prompt."""
    context = (
        "You are an expert in synthetic biology, biosecurity, and regulatory affairs. "
        "Provide literature review, molecular insights, market trends, and policy implications. "
        "Focus on CRISPR, metabolic engineering, living therapeutics, protein design, biosensors, and biosecurity. "
        "Be concise, factual, and provide citations."
    )
    return f"{context}\n\nQuery: {query}\nExpanded terms: {', '.join(expanded_terms)}"


def multimodal_research(query: str, narration: bool = False, generate_pdf: bool = False) -> Dict[str, Any]:
    """Main multi-modal synthetic biology pipeline."""
    print(f"[Pipeline] Starting research for query: {query}")

    # 1 — Expand query with ontology
    expanded_terms = merge_ontology_terms(
        query,
        expand_with_umls(query),
        expand_with_bioportal(query)
    )
    print(f"[Pipeline] Expanded terms: {expanded_terms}")

    # 2 — Inject domain-specific context
    enriched_query = inject_synbio_context(query, expanded_terms) if SYNBIO_MODE else query

    # 3 — Summarization with fallback
    summary = None
    for summarizer in [run_deepseek_summary, run_gemini_summary, run_openai_summary]:
        try:
            summary = summarizer(enriched_query)
            if summary:
                print(f"[Pipeline] Summary generated by {summarizer.__name__}")
                break
        except Exception as e:
            print(f"[Pipeline] {summarizer.__name__} failed: {e}")

    if not summary:
        summary = "No summary generated — please refine your query."

    # 4 — Citations extraction & PubMed fallback
    citations = extract_citations(summary)
    if not citations:
        print("[Pipeline] No citations in summary, querying PubMed...")
        citations = search_pubmed_literature(query)

    # 5 — Structures (NCBI, ChEMBL)
    structures = fetch_structures_for_terms(expanded_terms)

    # 6 — Image generation with fallback
    image_url = None
    for img_fn in [run_gemini_image, run_openai_image, run_hf_image]:
        try:
            image_url = img_fn(query)
            if image_url:
                print(f"[Pipeline] Image generated by {img_fn.__name__}")
                break
        except Exception as e:
            print(f"[Pipeline] {img_fn.__name__} failed: {e}")

    # 7 — Funding, Trials, Regulation, Safety, Biosecurity
    funding_data = fetch_funding_data(query) or []
    trial_data = fetch_clinical_trials(query) or []
    regulation_data = fetch_regulatory_info(query) or []
    safety_data = analyze_safety_concerns(query) or []
    biosecurity_data = analyze_biosecurity_risks(query) or []

    # 8 — Graph visualizations
    pathway_graph = generate_pathway_graph(query, expanded_terms) if expanded_terms else None
    funding_graph = generate_funding_network(query, funding_data) if funding_data else None

    # 9 — Save to Neo4j
    if NEO4J_URI:
        try:
            write_topic_and_papers(query, citations, expanded_terms)
            print("[Pipeline] Data saved to Neo4j")
        except Exception as e:
            print(f"[Pipeline] Neo4j save failed: {e}")

    # 10 — Optional narration
    audio_url = None
    if narration and ELEVEN_LABS_API_KEY:
        try:
            audio_url = narrate_text_elevenlabs(summary)
            print("[Pipeline] Narration generated")
        except Exception as e:
            print(f"[Pipeline] Narration failed: {e}")

    # 11 — Optional PDF export
    pdf_path = None
    if generate_pdf:
        try:
            pdf_path = export_report_to_pdf(query, summary, citations, structures, funding_data, regulation_data)
            print("[Pipeline] PDF report generated")
        except Exception as e:
            print(f"[Pipeline] PDF generation failed: {e}")

    # 12 — Build output
    return {
        "timestamp": datetime.utcnow().isoformat(),
        "query": query,
        "expanded_terms": expanded_terms,
        "summary": summary,
        "citations": citations,
        "structures": structures,
        "image_url": image_url,
        "funding_data": funding_data,
        "trial_data": trial_data,
        "regulation_data": regulation_data,
        "safety_data": safety_data,
        "biosecurity_data": biosecurity_data,
        "pathway_graph": pathway_graph,
        "funding_graph": funding_graph,
        "audio_url": audio_url,
        "pdf_path": pdf_path
    }