from llm import generate_response
from pubmed import search_pubmed
from rag import retrieve_context
from whisper_api import transcribe_audio
from molecules import generate_molecules


def run_pipeline(symptoms, audio_path=None):

    try:

        # Audio transcription
        if audio_path:

            transcription = transcribe_audio(
                audio_path
            )

            print(
                "TRANSCRIBED:",
                transcription
            )

            if not symptoms.strip():

                symptoms = transcription

            else:

                symptoms += (
                    " " + transcription
                )

        # Fallback
        if not symptoms.strip():

            symptoms = "general symptoms"

        # PubMed retrieval
        pubmed_papers = search_pubmed(
            symptoms
        )

        # REAL RAG retrieval
        context = retrieve_context(
            symptoms,
            pubmed_papers
        )

        # LLM reasoning
        response = generate_response(
            symptoms,
            context
        )

        # Drug generation
        molecules = generate_molecules(
            symptoms
        )

        return {

            "response": response,

            "pubmed": [
                p["id"]
                for p in pubmed_papers
            ],

            "molecules": molecules
        }

    except Exception as e:

        print(
            "PIPELINE ERROR:",
            e
        )

        return {

            "response":
            f"Pipeline Error: {str(e)}",

            "pubmed": [],

            "molecules": []
        }