Create app.py

app.py

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+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.vectorstores import Chroma
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.chains.question_answering import load_qa_chain
+from langchain.llms import OpenAI
+import os
+import streamlit as st
+
+with open("guide1.txt") as f:
+    hitchhikersguide = f.read()
+
+text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0, separator = "\n")
+texts = text_splitter.split_text(hitchhikersguide)
+
+embeddings = OpenAIEmbeddings()
+
+docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{"source": str(i)} for i in range(len(texts))]).as_retriever()
+
+chain = load_qa_chain(OpenAI(temperature=0), chain_type="stuff")
+
+def make_inference(query):
+    docs = docsearch.get_relevant_documents(query)
+    return(chain.run(input_documents=docs, question=query))
+
+if __name__ == "__main__":
+    # Title of the web application
+    st.title('🗣️TalkToMyDoc📄')
+    
+    # Text input widget
+    user_input = st.text_input('Enter a question about Hitchhiker\'s Galaxy Guide book:', '', help='🗣️TalkToMyDoc📄 is a tool that allows you to ask questions about a document. In this case - Hitch Hitchhiker\'s Guide to the Galaxy..')
+    
+    # Displaying output directly below the input field
+    if user_input:
+        st.write('Answer:', make_inference(user_input))