Upload app.py

app.py

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+import os
+import pickle
+import streamlit as st
+from streamlit_extras.add_vertical_space import add_vertical_space
+from PyPDF2 import PdfReader
+from openai.embeddings_utils import get_embedding
+import openai
+from dotenv import load_dotenv
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.llms import OpenAI
+from langchain.chains.question_answering import load_qa_chain
+from langchain.callbacks import get_openai_callback
+# Sidebar contents
+with st.sidebar:
+    st.title('🤗LLM Chat App💬')
+    st.markdown('''
+    ## About
+    OpenAI based LLM-powered chatbot built using:
+    - [OpenAI](https://platform.openai.com/docs/models) LLM model                
+    - [Streamlit](https://streamlit.io/)
+    - [LangChain](https://python.langchain.com/)
+    ''')
+    add_vertical_space(5)
+    st.write('Made with ❤️ by Harry')
+
+
+# Load environment variables
+# load_dotenv()
+
+# # Retrieve OpenAI API key
+# openai_api_key = os.getenv("OPENAI_API_KEY")
+# if openai_api_key is None:
+#     raise ValueError("The OPENAI_API_KEY environment variable is not set")
+
+# # Set the OpenAI API key for the OpenAI library
+# openai.api_key = openai_api_key
+
+def extract_text_from_pdf(pdf):
+    pdf_reader = PdfReader(pdf)
+    text = ""
+    for page in pdf_reader.pages:
+        text += page.extract_text()
+    return text
+def get_embeddings(text_list):
+    return [get_embedding(text) for text in text_list]
+def main():
+    st.header("Chat with PDF 💬")
+    # Upload a PDF file
+    pdf = st.file_uploader("Upload your PDF file", type='pdf')
+
+    if pdf is not None:
+        # Extract text from the PDF
+                
+        text = extract_text_from_pdf(pdf)
+        # Split text into chunks 
+        text_splitter = RecursiveCharacterTextSplitter(
+            chunk_size=1000,
+            chunk_overlap=200,
+            length_function=len
+            )
+        chunks = text_splitter.split_text(text=text)
+        # chunks data with langchain
+        #chunks = [text[i:i+chunk_size] for i in range(0, len(text), chunk_size - chunk_overlap)]
+        st.write("PDF content successfully extracted.")
+        #st.write("Below is chunks data")
+        #st.write(chunks)
+        
+        # Create or load embeddings
+        store_name = pdf.name[:-4]
+        st.write(f'Processing: {store_name}')
+        
+        if os.path.exists(f"{store_name}.pkl"):
+            with open(f"{store_name}.pkl", "rb") as f:
+                VectorStore = pickle.load(f)
+            st.write('Embeddings loaded from the disk')
+        else:
+            embeddings = OpenAIEmbeddings()
+            VectorStore = FAISS.from_texts(chunks, embedding=embeddings)
+            with open(f"{store_name}.pkl", "wb") as f:
+                pickle.dump(VectorStore, f)
+            st.write('Embeddings created and saved to disk')
+
+        # Accept user questions/query
+        query = st.text_input("Ask questions about your PDF file:")
+
+        if query:
+            docs = VectorStore.similarity_search(query=query, k=3)
+ 
+            llm = OpenAI(model_name="gpt-3.5-turbo") 
+            chain = load_qa_chain(llm=llm, chain_type="stuff")
+            with get_openai_callback() as cb:
+                response = chain.run(input_documents=docs, question=query)
+                print(cb)
+            st.write(response)
+if __name__ == '__main__':
+    main()