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main.py

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+from langchain.text_splitter import CharacterTextSplitter
+from langchain.docstore.document import Document
+from langchain.chains.summarize import load_summarize_chain
+from langchain import OpenAI, LLMChain, HuggingFaceHub
+import textwrap
+
+
+def summarize(doc):
+    text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=0,
+                                          separator="\n")  # Initilize an instance of CharacterTextSplitter
+    chunks = text_splitter.split_text(doc)
+    doc_store = [Document(page_content=text) for text in chunks]
+    llm_model = OpenAI(model_name="text-davinci-003", temperature=0)  # define your language model
+
+    summarization_chain2 = load_summarize_chain(llm=llm_model,
+                                                chain_type='map_reduce',
+                                                verbose=True  # define the chain type)
+                                                )
+    output_summary = summarization_chain2.run(doc_store)
+    wrapped_text = textwrap.fill(output_summary, width=100)
+
+    return wrapped_text
+
+if __name__ == "__main__":
+    # make a gradio interface
+    import gradio as gr
+
+    outputs = gr.outputs.Textbox()
+
+    app = gr.Interface(fn=summarize, inputs='text', outputs=outputs,description="This is a text summarization model").launch()
+
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requirements.txt

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+langchain
+openai
+textwrap