import requests
from transformers import pipeline
from nltk.tokenize import sent_tokenize
import nltk

from config import MY_PUBMED_EMAIL, MAX_PUBMED_RESULTS, SUMMARIZATION_CHUNK_SIZE

nltk.download("punkt")

# Summarization pipeline
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")

def search_pubmed(query, max_results=MAX_PUBMED_RESULTS):
    """
    Search PubMed for articles matching the query.
    """
    url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi"
    params = {
        "db": "pubmed",
        "term": query,
        "retmax": max_results,
        "retmode": "json",
        "tool": "AdvancedMedicalAI",
        "email": MY_PUBMED_EMAIL,
    }
    response = requests.get(url, params=params, timeout=10)
    response.raise_for_status()
    data = response.json()
    return data.get("esearchresult", {}).get("idlist", [])

def fetch_abstract(pmid):
    """
    Fetch the abstract of a given PubMed ID.
    """
    url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi"
    params = {
        "db": "pubmed",
        "id": pmid,
        "retmode": "text",
        "rettype": "abstract",
        "tool": "AdvancedMedicalAI",
        "email": MY_PUBMED_EMAIL,
    }
    response = requests.get(url, params=params, timeout=10)
    response.raise_for_status()
    return response.text.strip()

def fetch_pubmed_abstracts(pmids):
    """
    Fetch multiple abstracts for a list of PMIDs.
    """
    results = {}
    for pmid in pmids:
        try:
            abstract = fetch_abstract(pmid)
            results[pmid] = abstract
        except Exception as e:
            results[pmid] = f"Error fetching PMID {pmid}: {e}"
    return results

def summarize_text(text, chunk_size=SUMMARIZATION_CHUNK_SIZE):
    """
    Summarize long text using a chunking strategy.
    """
    sentences = sent_tokenize(text)
    chunks = []
    current_chunk = []
    current_length = 0

    for sentence in sentences:
        tokens = len(sentence.split())
        if current_length + tokens > chunk_size:
            chunks.append(" ".join(current_chunk))
            current_chunk = []
            current_length = 0
        current_chunk.append(sentence)
        current_length += tokens

    if current_chunk:
        chunks.append(" ".join(current_chunk))

    summaries = []
    for chunk in chunks:
        summary = summarizer(chunk, max_length=100, min_length=30, do_sample=False)[0]["summary_text"]
        summaries.append(summary)
    return " ".join(summaries)