Create app.py

app.py

@@ -0,0 +1,142 @@
+import streamlit as st
+from dotenv import load_dotenv
+import pickle
+from PyPDF2 import PdfReader
+from streamlit_extras.add_vertical_space import add_vertical_space
+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
+import os
+
+# Sidebar contents
+with st.sidebar:
+    st.title(':orange[BinDoc GmbH]')
+    st.markdown(
+        "Experience the future of document interaction with the revolutionary"
+    )
+
+    st.markdown("**BinDocs Chat App**.")
+
+        
+    st.markdown("Harnessing the power of a Large Language Model and AI technology,")
+       
+
+
+    st.markdown("this innovative platform redefines PDF engagement,")
+
+    st.markdown("enabling dynamic conversations that bridge the gap between")
+    st.markdown("human and machine intelligence.")
+
+    
+    
+    add_vertical_space(3)  # Add more vertical space between text blocks
+    st.write('Made with ❤️ by Anne')
+    
+
+openai_api_key = st.text_input("Enter your OpenAI API key:")
+pdf_path = "" 
+
+
+def load_pdf(file_path):
+    pdf_reader = PdfReader(file_path)
+    text = ""
+    for page in pdf_reader.pages:
+        text += page.extract_text()
+
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=1000,
+        chunk_overlap=200,
+        length_function=len
+    )
+    chunks = text_splitter.split_text(text=text)
+
+    store_name, _ = os.path.splitext(os.path.basename(file_path))
+
+    if os.path.exists(f"{store_name}.pkl"):
+        with open(f"{store_name}.pkl", "rb") as f:
+            VectorStore = pickle.load(f)
+    else:
+        embeddings = OpenAIEmbeddings()
+        VectorStore = FAISS.from_texts(chunks, embedding=embeddings)
+        with open(f"{store_name}.pkl", "wb") as f:
+            pickle.dump(VectorStore, f)
+
+    return VectorStore
+
+
+
+def load_chatbot(openai_api_key):
+    openai_config = {
+        "api_key": openai_api_key
+    }
+    return load_qa_chain(llm=OpenAI(config=openai_config), chain_type="stuff")
+
+
+def main():
+    st.title("BinDocs Chat App")
+
+
+    uploaded_pdf = st.file_uploader("Upload a PDF file:", type=["pdf"])
+
+    if uploaded_pdf is not None:
+        pdf_path = uploaded_pdf
+
+
+    if "chat_history" not in st.session_state:
+        st.session_state['chat_history'] = []
+
+    display_chat_history(st.session_state['chat_history'])
+
+    st.write("<!-- Start Spacer -->", unsafe_allow_html=True)
+    st.write("<div style='flex: 1;'></div>", unsafe_allow_html=True)
+    st.write("<!-- End Spacer -->", unsafe_allow_html=True)
+
+    new_messages_placeholder = st.empty()
+
+    if pdf_path is not None:
+        query = st.text_input("Ask questions about your PDF file (in any preferred language):")
+
+        if st.button("Ask") or (not st.session_state['chat_history'] and query) or (st.session_state['chat_history'] and query != st.session_state['chat_history'][-1][1]):
+            st.session_state['chat_history'].append(("User", query, "new"))
+
+            loading_message = st.empty()
+            loading_message.text('Bot is thinking...')
+
+            VectorStore = load_pdf(pdf_path)
+            chain = load_chatbot()
+            docs = VectorStore.similarity_search(query=query, k=3)
+            with get_openai_callback() as cb:
+                response = chain.run(input_documents=docs, question=query)
+
+            st.session_state['chat_history'].append(("Bot", response, "new"))
+
+            # Display new messages at the bottom
+            new_messages = st.session_state['chat_history'][-2:]
+            for chat in new_messages:
+                background_color = "#FFA07A" if chat[2] == "new" else "#acf" if chat[0] == "User" else "#caf"
+                new_messages_placeholder.markdown(f"<div style='background-color: {background_color}; padding: 10px; border-radius: 10px; margin: 10px;'>{chat[0]}: {chat[1]}</div>", unsafe_allow_html=True)
+
+            # Scroll to the latest response using JavaScript
+            st.write("<script>document.getElementById('response').scrollIntoView();</script>", unsafe_allow_html=True)
+
+            loading_message.empty()
+
+            # Clear the input field by setting the query variable to an empty string
+            query = ""
+
+        # Mark all messages as old after displaying
+        st.session_state['chat_history'] = [(sender, msg, "old") for sender, msg, _ in st.session_state['chat_history']]
+
+
+
+def display_chat_history(chat_history):
+    for chat in chat_history:
+        background_color = "#FFA07A" if chat[2] == "new" else "#acf" if chat[0] == "User" else "#caf"
+        st.markdown(f"<div style='background-color: {background_color}; padding: 10px; border-radius: 10px; margin: 10px;'>{chat[0]}: {chat[1]}</div>", unsafe_allow_html=True)
+
+if __name__ == "__main__":
+    main()
+