Update mtdna_classifier.py

mtdna_classifier.py

@@ -1,519 +1,524 @@
-# mtDNA Location Classifier MVP (Google Colab)
-# Accepts accession number → Fetches PubMed ID + isolate name → Gets abstract → Predicts location
-import os
-import subprocess
-import re
-from Bio import Entrez
-import fitz
-import spacy
-from spacy.cli import download
-from NER.PDF import pdf
-from NER.WordDoc import wordDoc
-from NER.html import extractHTML
-from NER.word2Vec import word2vec
-from transformers import pipeline
-import urllib.parse, requests
-from pathlib import Path
-from upgradeClassify import filter_context_for_sample, infer_location_for_sample
-# Set your email (required by NCBI Entrez)
-#Entrez.email = "[email protected]"
-import nltk
-
-nltk.download("stopwords")
-nltk.download("punkt")
-nltk.download('punkt_tab')
-# Step 1: Get PubMed ID from Accession using EDirect
-
-'''def get_info_from_accession(accession):
-    cmd = f'{os.environ["HOME"]}/edirect/esummary -db nuccore -id {accession} -format medline | egrep "PUBMED|isolate"'
-    result = subprocess.run(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
-    output = result.stdout
-    pubmedID, isolate = "", ""
-    for line in output.split("\n"):
-      if len(line) > 0:
-        if "PUBMED" in line:
-          pubmedID = line.split()[-1]
-        if "isolate" in line:  # Check for isolate information
-          # Try direct GenBank annotation: /isolate="XXX"
-          match1 = re.search(r'/isolate\s*=\s*"([^"]+)"', line)  # search on current line
-          if match1:
-            isolate = match1.group(1)
-          else:
-            # Try from DEFINITION line: ...isolate XXX...
-            match2 = re.search(r'isolate\s+([A-Za-z0-9_-]+)', line) # search on current line
-            if match2:
-              isolate = match2.group(1)'''
-from Bio import Entrez, Medline
-import re
-
-Entrez.email = "[email protected]"
-
-def get_info_from_accession(accession):
-    try:
-        handle = Entrez.efetch(db="nuccore", id=accession, rettype="medline", retmode="text")
-        text = handle.read()
-        handle.close()
-
-        # Extract PUBMED ID from the Medline text
-        pubmed_match = re.search(r'PUBMED\s+(\d+)', text)
-        pubmed_id = pubmed_match.group(1) if pubmed_match else ""
-
-        # Extract isolate if available
-        isolate_match = re.search(r'/isolate="([^"]+)"', text)
-        if not isolate_match:
-            isolate_match = re.search(r'isolate\s+([A-Za-z0-9_-]+)', text)
-        isolate = isolate_match.group(1) if isolate_match else ""
-
-        if not pubmed_id:
-            print(f"⚠️ No PubMed ID found for accession {accession}")
-
-        return pubmed_id, isolate
-
-    except Exception as e:
-        print("❌ Entrez error:", e)
-        return "", ""
-# Step 2: Get doi link to access the paper
-'''def get_doi_from_pubmed_id(pubmed_id):
-    cmd = f'{os.environ["HOME"]}/edirect/esummary -db pubmed -id {pubmed_id} -format medline | grep -i "AID"'
-    result = subprocess.run(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
-    output = result.stdout
-
-    doi_pattern = r'10\.\d{4,9}/[-._;()/:A-Z0-9]+(?=\s*\[doi\])'
-    match = re.search(doi_pattern, output, re.IGNORECASE)
-
-    if match:
-        return match.group(0)
-    else:
-        return None  # or raise an Exception with a helpful message'''
-
-def get_doi_from_pubmed_id(pubmed_id):
-    try:
-        handle = Entrez.efetch(db="pubmed", id=pubmed_id, rettype="medline", retmode="text")
-        records = list(Medline.parse(handle))
-        handle.close()
-
-        if not records:
-            return None
-        
-        record = records[0]
-        if "AID" in record:
-            for aid in record["AID"]:
-                if "[doi]" in aid:
-                    return aid.split(" ")[0]  # extract the DOI
-
-        return None
-
-    except Exception as e:
-        print(f"❌ Failed to get DOI from PubMed ID {pubmed_id}: {e}")
-        return None
-
-
-# Step 3: Extract Text: Get the paper (html text), sup. materials (pdf, doc, excel) and do text-preprocessing
-# Step 3.1: Extract Text
-# sub: download excel file
-def download_excel_file(url, save_path="temp.xlsx"):
-    if "view.officeapps.live.com" in url:
-        parsed_url = urllib.parse.parse_qs(urllib.parse.urlparse(url).query)
-        real_url = urllib.parse.unquote(parsed_url["src"][0])
-        response = requests.get(real_url)
-        with open(save_path, "wb") as f:
-            f.write(response.content)
-        return save_path
-    elif url.startswith("http") and (url.endswith(".xls") or url.endswith(".xlsx")):
-        response = requests.get(url)
-        response.raise_for_status()  # Raises error if download fails
-        with open(save_path, "wb") as f:
-            f.write(response.content)
-        return save_path
-    else:
-        print("URL must point directly to an .xls or .xlsx file\n or it already downloaded.")
-        return url
-def get_paper_text(doi,id,manualLinks=None):
-  # create the temporary folder to contain the texts
-  folder_path = Path("data/"+str(id))
-  if not folder_path.exists():
-      cmd = f'mkdir data/{id}'
-      result = subprocess.run(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
-      print("data/"+str(id) +" created.")
-  else:
-      print("data/"+str(id) +" already exists.")
-  saveLinkFolder = "data/"+id
-
-  link = 'https://doi.org/' + doi
-  '''textsToExtract = { "doiLink":"paperText"
-                        "file1.pdf":"text1",
-                        "file2.doc":"text2",
-                        "file3.xlsx":excelText3'''
-  textsToExtract = {}
-  # get the file to create listOfFile for each id
-  html = extractHTML.HTML("",link)
-  jsonSM = html.getSupMaterial()
-  text = ""
-  links  = [link] + sum((jsonSM[key] for key in jsonSM),[])
-  if manualLinks != None:
-    links += manualLinks
-  for l in links:
-    # get the main paper
-    name = l.split("/")[-1]
-    file_path = folder_path / name
-    if l == link:
-      text = html.getListSection()
-      textsToExtract[link] = text
-    elif l.endswith(".pdf"):
-      if file_path.is_file():
-          l = saveLinkFolder + "/" + name
-          print("File exists.")
-      p = pdf.PDF(l,saveLinkFolder,doi)
-      f = p.openPDFFile()
-      pdf_path = saveLinkFolder + "/" + l.split("/")[-1]
-      doc = fitz.open(pdf_path)
-      text = "\n".join([page.get_text() for page in doc])
-      textsToExtract[l] = text
-    elif l.endswith(".doc") or l.endswith(".docx"):
-      d = wordDoc.wordDoc(l,saveLinkFolder)
-      text = d.extractTextByPage()
-      textsToExtract[l] = text
-    elif l.split(".")[-1].lower() in "xlsx":
-      wc = word2vec.word2Vec()
-      # download excel file if it not downloaded yet
-      savePath = saveLinkFolder +"/"+ l.split("/")[-1]
-      excelPath = download_excel_file(l, savePath)
-      corpus = wc.tableTransformToCorpusText([],excelPath)
-      text = ''
-      for c in corpus:
-        para = corpus[c]
-        for words in para:
-          text += " ".join(words)
-      textsToExtract[l] = text
-  # delete folder after finishing getting text
-  #cmd = f'rm -r data/{id}'
-  #result = subprocess.run(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
-  return textsToExtract
-# Step 3.2: Extract context
-def extract_context(text, keyword, window=500):
-    # firstly try accession number
-    idx = text.find(keyword)
-    if idx == -1:
-        return "Sample ID not found."
-    return text[max(0, idx-window): idx+window]
-def extract_relevant_paragraphs(text, accession, keep_if=None, isolate=None):
-    if keep_if is None:
-        keep_if = ["sample", "method", "mtdna", "sequence", "collected", "dataset", "supplementary", "table"]
-
-    outputs = ""
-    text = text.lower()
-
-    # If isolate is provided, prioritize paragraphs that mention it
-    # If isolate is provided, prioritize paragraphs that mention it
-    if accession and accession.lower() in text:
-        if extract_context(text, accession.lower(), window=700) != "Sample ID not found.":
-            outputs += extract_context(text, accession.lower(), window=700)       
-    if isolate and isolate.lower() in text:
-        if extract_context(text, isolate.lower(), window=700) != "Sample ID not found.":
-            outputs += extract_context(text, isolate.lower(), window=700)
-    for keyword in keep_if:
-        para = extract_context(text, keyword)
-        if para and para not in outputs:
-            outputs += para + "\n"
-    return outputs
-# Step 4: Classification for now (demo purposes)
-# 4.1: Using a HuggingFace model (question-answering)
-def infer_fromQAModel(context, question="Where is the mtDNA sample from?"):
-    try:
-        qa = pipeline("question-answering", model="distilbert-base-uncased-distilled-squad")
-        result = qa({"context": context, "question": question})
-        return result.get("answer", "Unknown")
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-# 4.2: Infer from haplogroup
-# Load pre-trained spaCy model for NER
-try:
-    nlp = spacy.load("en_core_web_sm")
-except OSError:
-    download("en_core_web_sm")
-    nlp = spacy.load("en_core_web_sm")
-
-# Define the haplogroup-to-region mapping (simple rule-based)
-import csv
-
-def load_haplogroup_mapping(csv_path):
-    mapping = {}
-    with open(csv_path) as f:
-        reader = csv.DictReader(f)
-        for row in reader:
-            mapping[row["haplogroup"]] = [row["region"],row["source"]]
-    return mapping
-
-# Function to extract haplogroup from the text
-def extract_haplogroup(text):
-    match = re.search(r'\bhaplogroup\s+([A-Z][0-9a-z]*)\b', text)
-    if match:
-        submatch = re.match(r'^[A-Z][0-9]*', match.group(1))
-        if submatch:
-            return submatch.group(0)
-        else:
-            return match.group(1)  # fallback
-    fallback = re.search(r'\b([A-Z][0-9a-z]{1,5})\b', text)
-    if fallback:
-        return fallback.group(1)
-    return None
-
-
-# Function to extract location based on NER
-def extract_location(text):
-    doc = nlp(text)
-    locations = []
-    for ent in doc.ents:
-        if ent.label_ == "GPE":  # GPE = Geopolitical Entity (location)
-            locations.append(ent.text)
-    return locations
-
-# Function to infer location from haplogroup
-def infer_location_from_haplogroup(haplogroup):
-  haplo_map = load_haplogroup_mapping("data/haplogroup_regions_extended.csv")
-  return haplo_map.get(haplogroup, ["Unknown","Unknown"])
-
-# Function to classify the mtDNA sample
-def classify_mtDNA_sample_from_haplo(text):
-    # Extract haplogroup
-    haplogroup = extract_haplogroup(text)
-    # Extract location based on NER
-    locations = extract_location(text)
-    # Infer location based on haplogroup
-    inferred_location, sourceHaplo = infer_location_from_haplogroup(haplogroup)[0],infer_location_from_haplogroup(haplogroup)[1]
-    return {
-        "source":sourceHaplo,
-        "locations_found_in_context": locations,
-        "haplogroup": haplogroup,
-        "inferred_location": inferred_location
-
-    }
-# 4.3 Get from available NCBI
-def infer_location_fromNCBI(accession):
-    try:
-        handle = Entrez.efetch(db="nuccore", id=accession, rettype="medline", retmode="text")
-        text = handle.read()
-        handle.close()
-        match = re.search(r'/(geo_loc_name|country|location)\s*=\s*"([^"]+)"', text)
-        if match:
-            return match.group(2), match.group(0)  # This is the value like "Brunei"
-        return "Not found", "Not found"
-
-    except Exception as e:
-        print("❌ Entrez error:", e)
-        return "Not found", "Not found"
-
-### ANCIENT/MODERN FLAG
-from Bio import Entrez
-import re
-
-def flag_ancient_modern(accession, textsToExtract, isolate=None):
-    """
-    Try to classify a sample as Ancient or Modern using:
-    1. NCBI accession (if available)
-    2. Supplementary text or context fallback
-    """
-    context = ""
-    label, explain = "", ""
-
-    try:
-        # Check if we can fetch metadata from NCBI using the accession
-        handle = Entrez.efetch(db="nuccore", id=accession, rettype="medline", retmode="text")
-        text = handle.read()
-        handle.close()
-
-        isolate_source = re.search(r'/(isolation_source)\s*=\s*"([^"]+)"', text)
-        if isolate_source:
-            context += isolate_source.group(0) + " "
-
-        specimen = re.search(r'/(specimen|specimen_voucher)\s*=\s*"([^"]+)"', text)
-        if specimen:
-            context += specimen.group(0) + " "
-
-        if context.strip():
-            label, explain = detect_ancient_flag(context)
-            if label!="Unknown":
-              return label, explain + " from NCBI\n(" + context + ")"
-
-        # If no useful NCBI metadata, check supplementary texts
-        if textsToExtract:
-            labels = {"modern": [0, ""], "ancient": [0, ""], "unknown": 0}
-
-            for source in textsToExtract:
-                text_block = textsToExtract[source]
-                context = extract_relevant_paragraphs(text_block, accession, isolate=isolate)  # Reduce to informative paragraph(s)
-                label, explain = detect_ancient_flag(context)
-
-                if label == "Ancient":
-                    labels["ancient"][0] += 1
-                    labels["ancient"][1] += f"{source}:\n{explain}\n\n"
-                elif label == "Modern":
-                    labels["modern"][0] += 1
-                    labels["modern"][1] += f"{source}:\n{explain}\n\n"
-                else:
-                    labels["unknown"] += 1
-
-            if max(labels["modern"][0],labels["ancient"][0]) > 0:
-                if labels["modern"][0] > labels["ancient"][0]:
-                    return "Modern", labels["modern"][1]
-                else:
-                    return "Ancient", labels["ancient"][1]
-            else:
-              return "Unknown", "No strong keywords detected"
-        else:
-            print("No DOI or PubMed ID available for inference.")
-            return "", ""
-
-    except Exception as e:
-        print("Error:", e)
-        return "", ""
-
-
-def detect_ancient_flag(context_snippet):
-    context = context_snippet.lower()
-
-    ancient_keywords = [
-        "ancient", "archaeological", "prehistoric", "neolithic", "mesolithic", "paleolithic",
-        "bronze age", "iron age", "burial", "tomb", "skeleton", "14c", "radiocarbon", "carbon dating",
-        "postmortem damage", "udg treatment", "adna", "degradation", "site", "excavation",
-        "archaeological context", "temporal transect", "population replacement", "cal bp", "calbp", "carbon dated"
-    ]
-
-    modern_keywords = [
-        "modern", "hospital", "clinical", "consent","blood","buccal","unrelated", "blood sample","buccal sample","informed consent", "donor", "healthy", "patient",
-        "genotyping", "screening", "medical", "cohort", "sequencing facility", "ethics approval",
-        "we analysed", "we analyzed", "dataset includes", "new sequences", "published data",
-        "control cohort", "sink population", "genbank accession", "sequenced", "pipeline", 
-        "bioinformatic analysis", "samples from", "population genetics", "genome-wide data"
-    ]
-
-    ancient_hits = [k for k in ancient_keywords if k in context]
-    modern_hits = [k for k in modern_keywords if k in context]
-
-    if ancient_hits and not modern_hits:
-        return "Ancient", f"Flagged as ancient due to keywords: {', '.join(ancient_hits)}"
-    elif modern_hits and not ancient_hits:
-        return "Modern", f"Flagged as modern due to keywords: {', '.join(modern_hits)}"
-    elif ancient_hits and modern_hits:
-        if len(ancient_hits) >= len(modern_hits):
-            return "Ancient", f"Mixed context, leaning ancient due to: {', '.join(ancient_hits)}"
-        else:
-            return "Modern", f"Mixed context, leaning modern due to: {', '.join(modern_hits)}"
-    
-    # Fallback to QA
-    answer = infer_fromQAModel(context, question="Are the mtDNA samples ancient or modern? Explain why.")
-    if answer.startswith("Error"):
-        return "Unknown", answer
-    if "ancient" in answer.lower():
-        return "Ancient", f"Leaning ancient based on QA: {answer}"
-    elif "modern" in answer.lower():
-        return "Modern", f"Leaning modern based on QA: {answer}"
-    else:
-        return "Unknown", f"No strong keywords or QA clues. QA said: {answer}"
-
-# STEP 5: Main pipeline: accession -> 1. get pubmed id and isolate -> 2. get doi -> 3. get text -> 4. prediction -> 5. output: inferred location + explanation + confidence score
-def classify_sample_location(accession):
-  outputs = {}
-  keyword, context, location, qa_result, haplo_result = "", "", "", "", ""
-  # Step 1: get pubmed id and isolate
-  pubmedID, isolate = get_info_from_accession(accession)
-  '''if not pubmedID:
-    return {"error": f"Could not retrieve PubMed ID for accession {accession}"}'''
-  if not isolate:
-    isolate = "UNKNOWN_ISOLATE"
-  # Step 2: get doi
-  doi = get_doi_from_pubmed_id(pubmedID)
-  '''if not doi:
-    return {"error": "DOI not found for this accession. Cannot fetch paper or context."}'''
-  # Step 3: get text
-  '''textsToExtract = { "doiLink":"paperText"
-                        "file1.pdf":"text1",
-                        "file2.doc":"text2",
-                        "file3.xlsx":excelText3'''
-  if doi and pubmedID:                      
-    textsToExtract = get_paper_text(doi,pubmedID)
-  else: textsToExtract = {}  
-  '''if not textsToExtract:
-    return {"error": f"No texts extracted for DOI {doi}"}'''
-  if isolate not in [None, "UNKNOWN_ISOLATE"]:
-    label, explain = flag_ancient_modern(accession,textsToExtract,isolate)
-  else: 
-    label, explain = flag_ancient_modern(accession,textsToExtract)  
-  # Step 4: prediction
-  outputs[accession] = {}
-  outputs[isolate] = {}
-  # 4.0 Infer from NCBI
-  location, outputNCBI = infer_location_fromNCBI(accession)
-  NCBI_result = {
-      "source": "NCBI",
-      "sample_id": accession,
-      "predicted_location": location,
-      "context_snippet": outputNCBI}
-  outputs[accession]["NCBI"]= {"NCBI": NCBI_result}
-  if textsToExtract:
-    long_text = ""
-    for key in textsToExtract:
-      text = textsToExtract[key]
-      # try accession number first
-      outputs[accession][key] = {}
-      keyword = accession
-      context = extract_context(text, keyword, window=500)
-      # 4.1: Using a HuggingFace model (question-answering)
-      location = infer_fromQAModel(context, question=f"Where is the mtDNA sample {keyword} from?")
-      qa_result = {
-          "source": key,
-          "sample_id": keyword,
-          "predicted_location": location,
-          "context_snippet": context
-      }
-      outputs[keyword][key]["QAModel"] = qa_result
-      # 4.2: Infer from haplogroup
-      haplo_result = classify_mtDNA_sample_from_haplo(context)
-      outputs[keyword][key]["haplogroup"] = haplo_result
-      # try isolate
-      keyword = isolate
-      outputs[isolate][key] = {}
-      context = extract_context(text, keyword, window=500)
-      # 4.1.1: Using a HuggingFace model (question-answering)
-      location = infer_fromQAModel(context, question=f"Where is the mtDNA sample {keyword} from?")
-      qa_result = {
-          "source": key,
-          "sample_id": keyword,
-          "predicted_location": location,
-          "context_snippet": context
-      }
-      outputs[keyword][key]["QAModel"] = qa_result
-      # 4.2.1: Infer from haplogroup
-      haplo_result = classify_mtDNA_sample_from_haplo(context)
-      outputs[keyword][key]["haplogroup"] = haplo_result
-      # add long text
-      long_text += text + ". \n"
-    # 4.3: UpgradeClassify
-    # try sample_id as accession number
-    sample_id = accession
-    if sample_id:
-      filtered_context = filter_context_for_sample(sample_id.upper(), long_text, window_size=1)
-      locations = infer_location_for_sample(sample_id.upper(), filtered_context)
-      if locations!="No clear location found in top matches":
-        outputs[sample_id]["upgradeClassifier"] = {}
-        outputs[sample_id]["upgradeClassifier"]["upgradeClassifier"] = {
-          "source": "From these sources combined: "+ ", ".join(list(textsToExtract.keys())),
-          "sample_id": sample_id,
-          "predicted_location": ", ".join(locations),
-          "context_snippep": "First 1000 words: \n"+ filtered_context[:1000]
-        }
-    # try sample_id as isolate name
-    sample_id = isolate
-    if sample_id:
-      filtered_context = filter_context_for_sample(sample_id.upper(), long_text, window_size=1)
-      locations = infer_location_for_sample(sample_id.upper(), filtered_context)
-      if locations!="No clear location found in top matches":
-        outputs[sample_id]["upgradeClassifier"] = {}
-        outputs[sample_id]["upgradeClassifier"]["upgradeClassifier"] = {
-          "source": "From these sources combined: "+ ", ".join(list(textsToExtract.keys())),
-          "sample_id": sample_id,
-          "predicted_location": ", ".join(locations),
-          "context_snippep": "First 1000 words: \n"+ filtered_context[:1000]
-        }
-  return outputs, label, explain
+# mtDNA Location Classifier MVP (Google Colab)
+# Accepts accession number → Fetches PubMed ID + isolate name → Gets abstract → Predicts location
+import os
+import subprocess
+import re
+from Bio import Entrez
+import fitz
+import spacy
+from spacy.cli import download
+from NER.PDF import pdf
+from NER.WordDoc import wordDoc
+from NER.html import extractHTML
+from NER.word2Vec import word2vec
+from transformers import pipeline
+import urllib.parse, requests
+from pathlib import Path
+from upgradeClassify import filter_context_for_sample, infer_location_for_sample
+# Set your email (required by NCBI Entrez)
+#Entrez.email = "[email protected]"
+import nltk
+
+nltk.download("stopwords")
+#nltk.download("punkt")
+nltk.download('punkt', download_dir='/home/user/nltk_data')
+
+nltk.download('punkt_tab')
+# Step 1: Get PubMed ID from Accession using EDirect
+
+'''def get_info_from_accession(accession):
+    cmd = f'{os.environ["HOME"]}/edirect/esummary -db nuccore -id {accession} -format medline | egrep "PUBMED|isolate"'
+    result = subprocess.run(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
+    output = result.stdout
+    pubmedID, isolate = "", ""
+    for line in output.split("\n"):
+      if len(line) > 0:
+        if "PUBMED" in line:
+          pubmedID = line.split()[-1]
+        if "isolate" in line:  # Check for isolate information
+          # Try direct GenBank annotation: /isolate="XXX"
+          match1 = re.search(r'/isolate\s*=\s*"([^"]+)"', line)  # search on current line
+          if match1:
+            isolate = match1.group(1)
+          else:
+            # Try from DEFINITION line: ...isolate XXX...
+            match2 = re.search(r'isolate\s+([A-Za-z0-9_-]+)', line) # search on current line
+            if match2:
+              isolate = match2.group(1)'''
+from Bio import Entrez, Medline
+import re
+
+Entrez.email = "[email protected]"
+
+def get_info_from_accession(accession):
+    try:
+        handle = Entrez.efetch(db="nuccore", id=accession, rettype="medline", retmode="text")
+        text = handle.read()
+        handle.close()
+
+        # Extract PUBMED ID from the Medline text
+        pubmed_match = re.search(r'PUBMED\s+(\d+)', text)
+        pubmed_id = pubmed_match.group(1) if pubmed_match else ""
+
+        # Extract isolate if available
+        isolate_match = re.search(r'/isolate="([^"]+)"', text)
+        if not isolate_match:
+            isolate_match = re.search(r'isolate\s+([A-Za-z0-9_-]+)', text)
+        isolate = isolate_match.group(1) if isolate_match else ""
+
+        if not pubmed_id:
+            print(f"⚠️ No PubMed ID found for accession {accession}")
+
+        return pubmed_id, isolate
+
+    except Exception as e:
+        print("❌ Entrez error:", e)
+        return "", ""
+# Step 2: Get doi link to access the paper
+'''def get_doi_from_pubmed_id(pubmed_id):
+    cmd = f'{os.environ["HOME"]}/edirect/esummary -db pubmed -id {pubmed_id} -format medline | grep -i "AID"'
+    result = subprocess.run(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
+    output = result.stdout
+
+    doi_pattern = r'10\.\d{4,9}/[-._;()/:A-Z0-9]+(?=\s*\[doi\])'
+    match = re.search(doi_pattern, output, re.IGNORECASE)
+
+    if match:
+        return match.group(0)
+    else:
+        return None  # or raise an Exception with a helpful message'''
+
+def get_doi_from_pubmed_id(pubmed_id):
+    try:
+        handle = Entrez.efetch(db="pubmed", id=pubmed_id, rettype="medline", retmode="text")
+        records = list(Medline.parse(handle))
+        handle.close()
+
+        if not records:
+            return None
+        
+        record = records[0]
+        if "AID" in record:
+            for aid in record["AID"]:
+                if "[doi]" in aid:
+                    return aid.split(" ")[0]  # extract the DOI
+
+        return None
+
+    except Exception as e:
+        print(f"❌ Failed to get DOI from PubMed ID {pubmed_id}: {e}")
+        return None
+
+
+# Step 3: Extract Text: Get the paper (html text), sup. materials (pdf, doc, excel) and do text-preprocessing
+# Step 3.1: Extract Text
+# sub: download excel file
+def download_excel_file(url, save_path="temp.xlsx"):
+    if "view.officeapps.live.com" in url:
+        parsed_url = urllib.parse.parse_qs(urllib.parse.urlparse(url).query)
+        real_url = urllib.parse.unquote(parsed_url["src"][0])
+        response = requests.get(real_url)
+        with open(save_path, "wb") as f:
+            f.write(response.content)
+        return save_path
+    elif url.startswith("http") and (url.endswith(".xls") or url.endswith(".xlsx")):
+        response = requests.get(url)
+        response.raise_for_status()  # Raises error if download fails
+        with open(save_path, "wb") as f:
+            f.write(response.content)
+        return save_path
+    else:
+        print("URL must point directly to an .xls or .xlsx file\n or it already downloaded.")
+        return url
+def get_paper_text(doi,id,manualLinks=None):
+  # create the temporary folder to contain the texts
+  '''folder_path = Path("data/"+str(id))
+  if not folder_path.exists():
+      cmd = f'mkdir data/{id}'
+      result = subprocess.run(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
+      print("data/"+str(id) +" created.")
+  else:
+      print("data/"+str(id) +" already exists.")'''
+
+  cmd = f'mkdir data/{id}'
+  result = subprocess.run(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)  
+  saveLinkFolder = "data/"+id
+
+  link = 'https://doi.org/' + doi
+  '''textsToExtract = { "doiLink":"paperText"
+                        "file1.pdf":"text1",
+                        "file2.doc":"text2",
+                        "file3.xlsx":excelText3'''
+  textsToExtract = {}
+  # get the file to create listOfFile for each id
+  html = extractHTML.HTML("",link)
+  jsonSM = html.getSupMaterial()
+  text = ""
+  links  = [link] + sum((jsonSM[key] for key in jsonSM),[])
+  if manualLinks != None:
+    links += manualLinks
+  for l in links:
+    # get the main paper
+    name = l.split("/")[-1]
+    #file_path = folder_path / name
+    if l == link:
+      text = html.getListSection()
+      textsToExtract[link] = text
+    elif l.endswith(".pdf"):
+      '''if file_path.is_file():
+          l = saveLinkFolder + "/" + name
+          print("File exists.")'''
+      p = pdf.PDF(l,saveLinkFolder,doi)
+      f = p.openPDFFile()
+      pdf_path = saveLinkFolder + "/" + l.split("/")[-1]
+      doc = fitz.open(pdf_path)
+      text = "\n".join([page.get_text() for page in doc])
+      textsToExtract[l] = text
+    elif l.endswith(".doc") or l.endswith(".docx"):
+      d = wordDoc.wordDoc(l,saveLinkFolder)
+      text = d.extractTextByPage()
+      textsToExtract[l] = text
+    elif l.split(".")[-1].lower() in "xlsx":
+      wc = word2vec.word2Vec()
+      # download excel file if it not downloaded yet
+      savePath = saveLinkFolder +"/"+ l.split("/")[-1]
+      excelPath = download_excel_file(l, savePath)
+      corpus = wc.tableTransformToCorpusText([],excelPath)
+      text = ''
+      for c in corpus:
+        para = corpus[c]
+        for words in para:
+          text += " ".join(words)
+      textsToExtract[l] = text
+  # delete folder after finishing getting text
+  cmd = f'rm -r data/{id}'
+  result = subprocess.run(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
+  return textsToExtract
+# Step 3.2: Extract context
+def extract_context(text, keyword, window=500):
+    # firstly try accession number
+    idx = text.find(keyword)
+    if idx == -1:
+        return "Sample ID not found."
+    return text[max(0, idx-window): idx+window]
+def extract_relevant_paragraphs(text, accession, keep_if=None, isolate=None):
+    if keep_if is None:
+        keep_if = ["sample", "method", "mtdna", "sequence", "collected", "dataset", "supplementary", "table"]
+
+    outputs = ""
+    text = text.lower()
+
+    # If isolate is provided, prioritize paragraphs that mention it
+    # If isolate is provided, prioritize paragraphs that mention it
+    if accession and accession.lower() in text:
+        if extract_context(text, accession.lower(), window=700) != "Sample ID not found.":
+            outputs += extract_context(text, accession.lower(), window=700)       
+    if isolate and isolate.lower() in text:
+        if extract_context(text, isolate.lower(), window=700) != "Sample ID not found.":
+            outputs += extract_context(text, isolate.lower(), window=700)
+    for keyword in keep_if:
+        para = extract_context(text, keyword)
+        if para and para not in outputs:
+            outputs += para + "\n"
+    return outputs
+# Step 4: Classification for now (demo purposes)
+# 4.1: Using a HuggingFace model (question-answering)
+def infer_fromQAModel(context, question="Where is the mtDNA sample from?"):
+    try:
+        qa = pipeline("question-answering", model="distilbert-base-uncased-distilled-squad")
+        result = qa({"context": context, "question": question})
+        return result.get("answer", "Unknown")
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# 4.2: Infer from haplogroup
+# Load pre-trained spaCy model for NER
+try:
+    nlp = spacy.load("en_core_web_sm")
+except OSError:
+    download("en_core_web_sm")
+    nlp = spacy.load("en_core_web_sm")
+
+# Define the haplogroup-to-region mapping (simple rule-based)
+import csv
+
+def load_haplogroup_mapping(csv_path):
+    mapping = {}
+    with open(csv_path) as f:
+        reader = csv.DictReader(f)
+        for row in reader:
+            mapping[row["haplogroup"]] = [row["region"],row["source"]]
+    return mapping
+
+# Function to extract haplogroup from the text
+def extract_haplogroup(text):
+    match = re.search(r'\bhaplogroup\s+([A-Z][0-9a-z]*)\b', text)
+    if match:
+        submatch = re.match(r'^[A-Z][0-9]*', match.group(1))
+        if submatch:
+            return submatch.group(0)
+        else:
+            return match.group(1)  # fallback
+    fallback = re.search(r'\b([A-Z][0-9a-z]{1,5})\b', text)
+    if fallback:
+        return fallback.group(1)
+    return None
+
+
+# Function to extract location based on NER
+def extract_location(text):
+    doc = nlp(text)
+    locations = []
+    for ent in doc.ents:
+        if ent.label_ == "GPE":  # GPE = Geopolitical Entity (location)
+            locations.append(ent.text)
+    return locations
+
+# Function to infer location from haplogroup
+def infer_location_from_haplogroup(haplogroup):
+  haplo_map = load_haplogroup_mapping("data/haplogroup_regions_extended.csv")
+  return haplo_map.get(haplogroup, ["Unknown","Unknown"])
+
+# Function to classify the mtDNA sample
+def classify_mtDNA_sample_from_haplo(text):
+    # Extract haplogroup
+    haplogroup = extract_haplogroup(text)
+    # Extract location based on NER
+    locations = extract_location(text)
+    # Infer location based on haplogroup
+    inferred_location, sourceHaplo = infer_location_from_haplogroup(haplogroup)[0],infer_location_from_haplogroup(haplogroup)[1]
+    return {
+        "source":sourceHaplo,
+        "locations_found_in_context": locations,
+        "haplogroup": haplogroup,
+        "inferred_location": inferred_location
+
+    }
+# 4.3 Get from available NCBI
+def infer_location_fromNCBI(accession):
+    try:
+        handle = Entrez.efetch(db="nuccore", id=accession, rettype="medline", retmode="text")
+        text = handle.read()
+        handle.close()
+        match = re.search(r'/(geo_loc_name|country|location)\s*=\s*"([^"]+)"', text)
+        if match:
+            return match.group(2), match.group(0)  # This is the value like "Brunei"
+        return "Not found", "Not found"
+
+    except Exception as e:
+        print("❌ Entrez error:", e)
+        return "Not found", "Not found"
+
+### ANCIENT/MODERN FLAG
+from Bio import Entrez
+import re
+
+def flag_ancient_modern(accession, textsToExtract, isolate=None):
+    """
+    Try to classify a sample as Ancient or Modern using:
+    1. NCBI accession (if available)
+    2. Supplementary text or context fallback
+    """
+    context = ""
+    label, explain = "", ""
+
+    try:
+        # Check if we can fetch metadata from NCBI using the accession
+        handle = Entrez.efetch(db="nuccore", id=accession, rettype="medline", retmode="text")
+        text = handle.read()
+        handle.close()
+
+        isolate_source = re.search(r'/(isolation_source)\s*=\s*"([^"]+)"', text)
+        if isolate_source:
+            context += isolate_source.group(0) + " "
+
+        specimen = re.search(r'/(specimen|specimen_voucher)\s*=\s*"([^"]+)"', text)
+        if specimen:
+            context += specimen.group(0) + " "
+
+        if context.strip():
+            label, explain = detect_ancient_flag(context)
+            if label!="Unknown":
+              return label, explain + " from NCBI\n(" + context + ")"
+
+        # If no useful NCBI metadata, check supplementary texts
+        if textsToExtract:
+            labels = {"modern": [0, ""], "ancient": [0, ""], "unknown": 0}
+
+            for source in textsToExtract:
+                text_block = textsToExtract[source]
+                context = extract_relevant_paragraphs(text_block, accession, isolate=isolate)  # Reduce to informative paragraph(s)
+                label, explain = detect_ancient_flag(context)
+
+                if label == "Ancient":
+                    labels["ancient"][0] += 1
+                    labels["ancient"][1] += f"{source}:\n{explain}\n\n"
+                elif label == "Modern":
+                    labels["modern"][0] += 1
+                    labels["modern"][1] += f"{source}:\n{explain}\n\n"
+                else:
+                    labels["unknown"] += 1
+
+            if max(labels["modern"][0],labels["ancient"][0]) > 0:
+                if labels["modern"][0] > labels["ancient"][0]:
+                    return "Modern", labels["modern"][1]
+                else:
+                    return "Ancient", labels["ancient"][1]
+            else:
+              return "Unknown", "No strong keywords detected"
+        else:
+            print("No DOI or PubMed ID available for inference.")
+            return "", ""
+
+    except Exception as e:
+        print("Error:", e)
+        return "", ""
+
+
+def detect_ancient_flag(context_snippet):
+    context = context_snippet.lower()
+
+    ancient_keywords = [
+        "ancient", "archaeological", "prehistoric", "neolithic", "mesolithic", "paleolithic",
+        "bronze age", "iron age", "burial", "tomb", "skeleton", "14c", "radiocarbon", "carbon dating",
+        "postmortem damage", "udg treatment", "adna", "degradation", "site", "excavation",
+        "archaeological context", "temporal transect", "population replacement", "cal bp", "calbp", "carbon dated"
+    ]
+
+    modern_keywords = [
+        "modern", "hospital", "clinical", "consent","blood","buccal","unrelated", "blood sample","buccal sample","informed consent", "donor", "healthy", "patient",
+        "genotyping", "screening", "medical", "cohort", "sequencing facility", "ethics approval",
+        "we analysed", "we analyzed", "dataset includes", "new sequences", "published data",
+        "control cohort", "sink population", "genbank accession", "sequenced", "pipeline", 
+        "bioinformatic analysis", "samples from", "population genetics", "genome-wide data"
+    ]
+
+    ancient_hits = [k for k in ancient_keywords if k in context]
+    modern_hits = [k for k in modern_keywords if k in context]
+
+    if ancient_hits and not modern_hits:
+        return "Ancient", f"Flagged as ancient due to keywords: {', '.join(ancient_hits)}"
+    elif modern_hits and not ancient_hits:
+        return "Modern", f"Flagged as modern due to keywords: {', '.join(modern_hits)}"
+    elif ancient_hits and modern_hits:
+        if len(ancient_hits) >= len(modern_hits):
+            return "Ancient", f"Mixed context, leaning ancient due to: {', '.join(ancient_hits)}"
+        else:
+            return "Modern", f"Mixed context, leaning modern due to: {', '.join(modern_hits)}"
+    
+    # Fallback to QA
+    answer = infer_fromQAModel(context, question="Are the mtDNA samples ancient or modern? Explain why.")
+    if answer.startswith("Error"):
+        return "Unknown", answer
+    if "ancient" in answer.lower():
+        return "Ancient", f"Leaning ancient based on QA: {answer}"
+    elif "modern" in answer.lower():
+        return "Modern", f"Leaning modern based on QA: {answer}"
+    else:
+        return "Unknown", f"No strong keywords or QA clues. QA said: {answer}"
+
+# STEP 5: Main pipeline: accession -> 1. get pubmed id and isolate -> 2. get doi -> 3. get text -> 4. prediction -> 5. output: inferred location + explanation + confidence score
+def classify_sample_location(accession):
+  outputs = {}
+  keyword, context, location, qa_result, haplo_result = "", "", "", "", ""
+  # Step 1: get pubmed id and isolate
+  pubmedID, isolate = get_info_from_accession(accession)
+  '''if not pubmedID:
+    return {"error": f"Could not retrieve PubMed ID for accession {accession}"}'''
+  if not isolate:
+    isolate = "UNKNOWN_ISOLATE"
+  # Step 2: get doi
+  doi = get_doi_from_pubmed_id(pubmedID)
+  '''if not doi:
+    return {"error": "DOI not found for this accession. Cannot fetch paper or context."}'''
+  # Step 3: get text
+  '''textsToExtract = { "doiLink":"paperText"
+                        "file1.pdf":"text1",
+                        "file2.doc":"text2",
+                        "file3.xlsx":excelText3'''
+  if doi and pubmedID:                      
+    textsToExtract = get_paper_text(doi,pubmedID)
+  else: textsToExtract = {}  
+  '''if not textsToExtract:
+    return {"error": f"No texts extracted for DOI {doi}"}'''
+  if isolate not in [None, "UNKNOWN_ISOLATE"]:
+    label, explain = flag_ancient_modern(accession,textsToExtract,isolate)
+  else: 
+    label, explain = flag_ancient_modern(accession,textsToExtract)  
+  # Step 4: prediction
+  outputs[accession] = {}
+  outputs[isolate] = {}
+  # 4.0 Infer from NCBI
+  location, outputNCBI = infer_location_fromNCBI(accession)
+  NCBI_result = {
+      "source": "NCBI",
+      "sample_id": accession,
+      "predicted_location": location,
+      "context_snippet": outputNCBI}
+  outputs[accession]["NCBI"]= {"NCBI": NCBI_result}
+  if textsToExtract:
+    long_text = ""
+    for key in textsToExtract:
+      text = textsToExtract[key]
+      # try accession number first
+      outputs[accession][key] = {}
+      keyword = accession
+      context = extract_context(text, keyword, window=500)
+      # 4.1: Using a HuggingFace model (question-answering)
+      location = infer_fromQAModel(context, question=f"Where is the mtDNA sample {keyword} from?")
+      qa_result = {
+          "source": key,
+          "sample_id": keyword,
+          "predicted_location": location,
+          "context_snippet": context
+      }
+      outputs[keyword][key]["QAModel"] = qa_result
+      # 4.2: Infer from haplogroup
+      haplo_result = classify_mtDNA_sample_from_haplo(context)
+      outputs[keyword][key]["haplogroup"] = haplo_result
+      # try isolate
+      keyword = isolate
+      outputs[isolate][key] = {}
+      context = extract_context(text, keyword, window=500)
+      # 4.1.1: Using a HuggingFace model (question-answering)
+      location = infer_fromQAModel(context, question=f"Where is the mtDNA sample {keyword} from?")
+      qa_result = {
+          "source": key,
+          "sample_id": keyword,
+          "predicted_location": location,
+          "context_snippet": context
+      }
+      outputs[keyword][key]["QAModel"] = qa_result
+      # 4.2.1: Infer from haplogroup
+      haplo_result = classify_mtDNA_sample_from_haplo(context)
+      outputs[keyword][key]["haplogroup"] = haplo_result
+      # add long text
+      long_text += text + ". \n"
+    # 4.3: UpgradeClassify
+    # try sample_id as accession number
+    sample_id = accession
+    if sample_id:
+      filtered_context = filter_context_for_sample(sample_id.upper(), long_text, window_size=1)
+      locations = infer_location_for_sample(sample_id.upper(), filtered_context)
+      if locations!="No clear location found in top matches":
+        outputs[sample_id]["upgradeClassifier"] = {}
+        outputs[sample_id]["upgradeClassifier"]["upgradeClassifier"] = {
+          "source": "From these sources combined: "+ ", ".join(list(textsToExtract.keys())),
+          "sample_id": sample_id,
+          "predicted_location": ", ".join(locations),
+          "context_snippep": "First 1000 words: \n"+ filtered_context[:1000]
+        }
+    # try sample_id as isolate name
+    sample_id = isolate
+    if sample_id:
+      filtered_context = filter_context_for_sample(sample_id.upper(), long_text, window_size=1)
+      locations = infer_location_for_sample(sample_id.upper(), filtered_context)
+      if locations!="No clear location found in top matches":
+        outputs[sample_id]["upgradeClassifier"] = {}
+        outputs[sample_id]["upgradeClassifier"]["upgradeClassifier"] = {
+          "source": "From these sources combined: "+ ", ".join(list(textsToExtract.keys())),
+          "sample_id": sample_id,
+          "predicted_location": ", ".join(locations),
+          "context_snippep": "First 1000 words: \n"+ filtered_context[:1000]
+        }
+  return outputs, label, explain