diff --git a/src/ontogpt/cli.py b/src/ontogpt/cli.py
index 0019cf5cc..e3d36257f 100644
--- a/src/ontogpt/cli.py
+++ b/src/ontogpt/cli.py
@@ -38,6 +38,7 @@
 from ontogpt.engines.reasoner_engine import ReasonerEngine
 from ontogpt.engines.spires_engine import SPIRESEngine
 from ontogpt.engines.synonym_engine import SynonymEngine
+from ontogpt.engines.topic_classifier_engine import TopicClassifierEngine
 from ontogpt.evaluation.resolver import create_evaluator
 from ontogpt.io.csv_wrapper import parse_yaml_predictions, write_graph, write_obj_as_csv
 from ontogpt.io.html_exporter import HTMLExporter
@@ -1112,6 +1113,86 @@ def synonyms(
         output.write(f"{line}\n")
 
 
+@main.command()
+@inputfile_option
+@use_pdf_options
+@model_option
+@temperature_option
+@api_base_option
+@api_version_option
+@model_provider_option
+@system_message_option
+@click.argument("topic")
+def classify_by_topic(
+    inputfile,
+    model,
+    temperature,
+    api_base,
+    api_version,
+    model_provider,
+    system_message,
+    topic,
+    use_pdf,
+):
+    """Classify input text by topic.
+
+    Returns True if the input text is about the topic, False otherwise,
+    along with the name of the input file.
+
+    A path to a file containing input text may be passed as inputfile,
+    as may a directory of input files.
+
+    Example:
+
+    ontogpt classify-by-topic -i temp/30091466.txt
+     "clinical observations of human patients, including the diagnostic
+     and therapeutic procedures used during their clinical care"
+
+    """
+
+    if not model:
+        model = DEFAULT_MODEL
+
+    inputdict = {}
+
+    if not inputfile or inputfile == "-":
+        text = sys.stdin.read()
+        inputdict["Input"] = text
+    elif inputfile and Path(inputfile).is_dir():
+        logging.info(f"Input file directory: {inputfile}")
+        inputfiles = Path(inputfile).glob("*.txt")
+        inputdict = {f: (open(f, "r").read()) for f in inputfiles if f.is_file()}
+        logging.info(f"Found {len(inputdict)} input files here.")
+    elif inputfile and Path(inputfile).exists():
+        logging.info(f"Input file: {inputfile}")
+        if use_pdf:
+            import pymupdf
+
+            doc = pymupdf.open(inputfile)
+            text = ""
+            for page in doc:
+                text = text + (page.get_text())
+        else:
+            text = open(inputfile, "rb").read().decode(encoding="utf-8", errors="ignore")
+        logging.info(f"Input text: {text}")
+        inputdict[inputfile] = text
+    elif inputfile and not Path(inputfile).exists():
+        raise FileNotFoundError(f"Cannot find input file {inputfile}")
+
+    ke = TopicClassifierEngine(
+        model=model,
+        temperature=temperature,
+        api_base=api_base,
+        api_version=api_version,
+        model_provider=model_provider,
+        system_message=system_message,
+    )
+
+    for input_entry in inputdict:
+        response = ke.binary_classify(topic=topic, text=inputdict[input_entry])
+        print(f"{input_entry}\t{response}")
+
+
 @main.command()
 @model_option
 @api_base_option