Create app.py

app,py

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+import streamlit as st
+import os
+from PyPDF2 import PdfReader
+import openpyxl
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import GooglePalmEmbeddings
+from langchain.llms import GooglePalm
+from langchain.vectorstores import FAISS
+from langchain.chains import ConversationalRetrievalChain
+from langchain.memory import ConversationBufferMemory
+
+os.environ['GOOGLE_API_KEY'] = 'AIzaSyD8uzXToT4I2ABs7qo_XiuKh8-L2nuWCEM'
+
+def get_pdf_text(pdf_docs):
+    text = ""
+    for pdf in pdf_docs:
+        pdf_reader = PdfReader(pdf)
+        for page in pdf_reader.pages:
+            text += page.extract_text()
+    return text
+
+def get_excel_text(excel_docs):
+    text = ""
+    for excel_doc in excel_docs:
+        workbook = openpyxl.load_workbook(filename=excel_doc)
+        for sheet in workbook:
+            for row in sheet:
+                for cell in row:
+                    text += str(cell.value) + " "
+    return text.strip()
+
+def get_text_chunks(text):
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=20)
+    chunks = text_splitter.split_text(text)
+    return chunks
+
+def get_vector_store(text_chunks):
+    embeddings = GooglePalmEmbeddings()
+    vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
+    return vector_store
+
+def get_conversational_chain(vector_store):
+    llm = GooglePalm()
+    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+    conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=vector_store.as_retriever(), memory=memory)
+    return conversation_chain
+
+def get_user_input(user_question):
+    with st.container():
+        response = st.session_state.conversation({'question': user_question})
+        st.session_state.chatHistory = response['chat_history']
+        file_contents = ""
+        left , right = st.columns((2,1))
+        with left:
+            for i, message in enumerate(st.session_state.chatHistory):
+                if i % 2 == 0:
+                    st.markdown(f'<div style="background-color: rgb(30 24 17 / 77%); border-radius: 10px; padding: 10px; margin-bottom: 5px; text-align: end;"><span style="text-align: end;">User:</span> {message.content}</div>', unsafe_allow_html=True)
+                else:
+                    st.markdown(f'<div style="background-color: rgb(145 74 1 / 25%); border-radius: 10px; padding: 10px; margin-bottom: 5px; ">Bot: {message.content}</div>', unsafe_allow_html=True)
+        with right:
+            for message in st.session_state.chatHistory:
+                file_contents += f"{message.content}\n"
+            file_name = "Chat_History.txt"
+
+def main():
+    st.set_page_config("DocChat")
+    # Define Streamlit app layout
+    st.markdown("<h3 style='color: orange;'>🧾 DocChat - Chat with multiple documents</h3>", unsafe_allow_html=True)
+    st.caption("🚀 Chat bot developed By :- [Dinesh Abeysinghe](https://www.linkedin.com/in/dinesh-abeysinghe-bb773293) | [GitHub Source Code](https://github.com/dineshabey/AI-TypeTalkChat.git) | [About model](https://arxiv.org/abs/2004.13637) ")
+    st.markdown("<div style= 'text-align: center;'>First need to upload PDF file or Excel file. Then you can start chat with document related things <span style='color: orange;'>Please click like button</span>❤️ and support me and enjoy it.</div>", unsafe_allow_html=True)
+    st.write("---")
+    with st.container():
+        with st.sidebar:
+            st.title("Settings")
+            st.subheader("Upload Documents")
+            st.markdown("**PDF files:**")
+            pdf_docs = st.file_uploader("Upload PDF Files", accept_multiple_files=True)
+            if st.button("Process PDF file"):
+                with st.spinner("Processing PDFs..."):
+                    raw_text = get_pdf_text(pdf_docs)
+                    text_chunks = get_text_chunks(raw_text)
+                    vector_store = get_vector_store(text_chunks)
+                    st.session_state.conversation = get_conversational_chain(vector_store)
+                    st.success("PDF processed successfully!")
+
+            st.markdown("**Excel files:**")
+            excel_docs = st.file_uploader("Upload Excel Files", accept_multiple_files=True)
+            if st.button("Process Excel file"):
+                with st.spinner("Processing Excel files..."):
+                    raw_text = get_excel_text(excel_docs)
+                    text_chunks = get_text_chunks(raw_text)
+                    vector_store = get_vector_store(text_chunks)
+                    st.session_state.conversation = get_conversational_chain(vector_store)
+                    st.success("Excel file processed successfully!")
+
+    with st.container():
+        st.subheader("Document Q&A")
+        user_question = st.text_input("Ask a Question from the document")
+        if "conversation" not in st.session_state:
+            st.session_state.conversation = None
+        if "chatHistory" not in st.session_state:
+            st.session_state.chatHistory = None
+        if user_question:
+            get_user_input(user_question)
+
+if __name__ == "__main__":
+    main()