Create app.py

app.py

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+import os
+import getpass
+import streamlit as st
+from langchain.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import Chroma
+from langchain import HuggingFaceHub
+from langchain.chains import RetrievalQA
+# __import__('pysqlite3')
+# import sys
+# sys.modules['sqlite3'] = sys.modules.pop('pysqlite3')
+
+
+# load huggingface api key
+hubtok = os.environ["HUGGINGFACE_HUB_TOKEN"]
+
+# use streamlit file uploader to ask user for file
+# file = st.file_uploader("Upload PDF")
+
+
+path = "https://vedpuran.files.wordpress.com/2013/04/455_gita_roman.pdf"
+loader = PyPDFLoader(path)
+pages = loader.load()
+
+# st.write(pages)
+
+splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=20)
+docs = splitter.split_documents(pages)
+
+embeddings = HuggingFaceEmbeddings()
+doc_search = Chroma.from_documents(docs, embeddings)
+
+repo_id = "tiiuae/falcon-7b"
+llm = HuggingFaceHub(repo_id=repo_id, huggingfacehub_api_token=hubtok, model_kwargs={'temperature': 0.2,'max_length': 1000})
+
+from langchain.schema import retriever
+retireval_chain = RetrievalQA.from_chain_type(llm, chain_type="stuff", retriever=doc_search.as_retriever())
+
+if query := st.chat_input("Enter a question: "):
+  with st.chat_message("assistant"):
+    st.write(retireval_chain.run(query))