test

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.write("User: ", message.content)
+                else:
+                    st.write("Bot: ", message.content)
+            st.success("Done !")
+        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")
+  st.header("DocChat - Chat with multiple documents")
+  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")
+      st.write('Ask a question : ')
+      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()

requirements.txt

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+google-generativeai
+langchain
+PyPDF2
+faiss-cpu
+streamlit
+openpyxl