Create app.py

app.py

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+import gradio as gr
+import os
+
+from langchain_community.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import Chroma
+from langchain.chains import ConversationalRetrievalChain
+from langchain_community.embeddings import HuggingFaceEmbeddings 
+from langchain_community.llms import HuggingFacePipeline
+from langchain.chains import ConversationChain
+from langchain.memory import ConversationBufferMemory
+from langchain_community.llms import HuggingFaceEndpoint
+
+from pathlib import Path
+import chromadb
+from unidecode import unidecode
+
+from transformers import AutoTokenizer
+import transformers
+import torch
+import tqdm 
+import accelerate
+import re
+
+list_llm = ["HuggingFaceH4/zephyr-7b-beta", "mistralai/Mistral-7B-Instruct-v0.2"]
+list_llm_simple = [os.path.basename(llm) for llm in list_llm]
+
+def summarize_document(document_text):
+    # Your summarization code here
+    summary = "The document covers various topics such as X, Y, and Z, providing detailed insights into each aspect."
+    return summary
+
+def demo():
+    with gr.Blocks(theme="base") as demo:
+        gr.Markdown("<center><h2>PDF Summarizer</center></h2>")
+        
+        text_input = gr.Textbox(placeholder="Paste your document text here", label="Document Text")
+        summarize_btn = gr.Button("Summarize")
+        summary_output = gr.Textbox(readonly=True, label="Summary")
+        
+        summarize_btn.click(summarize_document, inputs=[text_input], outputs=[summary_output])
+        
+    demo.launch()
+
+if __name__ == "__main__":
+    demo()