Create app.py

app.py

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+from langchain.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.embeddings.huggingface import HuggingFaceEmbeddings
+from langchain_community.vectorstores import Chroma
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain.prompts import ChatPromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
+import streamlit as st
+
+# Loading and spliting the document
+pdf = PyPDFLoader("/content/quran-in-modern-english.pdf")
+data = pdf.load()
+rs = RecursiveCharacterTextSplitter(chunk_size=1313, chunk_overlap=200)
+splits = rs.split_documents(data)
+
+# Initializing the embedding model
+em = HuggingFaceEmbeddings()
+
+# Creating vector data base
+vectordb = Chroma.from_documents(documents=splits, embedding=em)
+
+# Initializizing the LLM
+llm = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0, google_api_key="AIzaSyBG8UJFmZnGq417gnWyoA-5mrTKBn1D1r0")
+
+#prompt template
+template = """
+You are a helpful assistant to answer the queries. If you don't get an answer from the context, generate it by yourself. Use this context to answer the user's question.
+
+Context: {context}
+
+Question: {question}
+"""
+prompt_template = ChatPromptTemplate.from_template(template)
+
+#retriever
+retriever = vectordb.as_retriever()
+
+# Creating RAG chain
+rag_chain = (
+    {"context": retriever, "question": RunnablePassthrough()}
+    | prompt_template
+    | llm
+    | StrOutputParser()
+)
+
+# Streamlit app
+st.title("Quran Query Answering Bot")
+st.write("Ask your query, you will get an answer from the context of the Quran.")
+
+user_query = st.text_input("Enter your query:")
+
+if user_query:
+    answer = rag_chain.invoke({"question": user_query})
+    st.write("Answer:", answer)