diff --git a/yt_summarizer.py b/yt_summarizer.py
new file mode 100644
index 0000000..35af3e6
--- /dev/null
+++ b/yt_summarizer.py
@@ -0,0 +1,25 @@
+from langchain_community.document_loaders import YoutubeLoader
+from langchain.text_splitter import TokenTextSplitter
+from langchain_community.chat_models import ChatOllama
+from langchain.chains.summarize import load_summarize_chain
+from dotenv import load_dotenv
+
+# Get video transcript
+videos = ["https://www.youtube.com/watch?v=bYjQ9fzinT8", "https://www.youtube.com/watch?v=QCg0axyXxs4"]
+
+loader = YoutubeLoader.from_youtube_url(videos[0], language=["en", "en-US"])
+transcript = loader.load()
+#print(transcript)
+
+# Split the transcript into chunks
+# Llama 3 model takes up to 8192 input tokens, so I set chunk size to 7500 for leaving some space to model.
+splitter = TokenTextSplitter(chunk_size = 7500, chunk_overlap = 100)
+chunks = splitter.split_documents(transcript)
+#print(chunks)
+#print("chunks: ", len(chunks))
+
+llm = ChatOllama(model="llama3")
+summarize_chain = load_summarize_chain(llm=llm, chain_type="refine", verbose=True)
+
+summary = summarize_chain.run(chunks)
+print(summary)
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