Update app.py

app.py

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+import streamlit as st
+import os
+from PyPDF2 import PdfReader
+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_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 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("Human:", 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"
+            st.download_button("Download chat history👈", file_contents, file_name=file_name, mime="text/plain")
+
+
+def summary(summarization):
+    with st.container():
+        file_contents = ''
+        left , right = st.columns((2,1))
+        with left:
+            if summarization:
+                response1 = st.session_state.conversation({'question': 'Retrieve one-line topics and their descriptors; create detailed, bulleted summaries for each topic.'})
+                st.write("summary:\n", response1['answer'])
+                st.success("Done !")
+            else:
+                response1 = {}
+
+        with right:
+            file_contents = response1.get('answer', '')
+            file_name = "summarization_result.txt"
+            st.download_button("Download summery👈", file_contents, file_name=file_name, mime="text/plain")
+
+
+def main():
+    st.set_page_config("Chat with Multiple PDFs")
+    st.header("Chat with Multiple PDF 💬")
+    st.write("---")
+    with st.container():
+        with st.sidebar:
+            st.title("Settings")
+            st.subheader("Upload your Documents")
+            pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Process Button", accept_multiple_files=True)
+            if st.button("Process"):
+                with st.spinner("Processing"):
+                    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("Done")
+    with st.container():
+        # Summarization Section
+        st.subheader("PDF Summarization")
+        st.write('Click on summary button to get summary on given uploaded file.')
+        summarization = st.button("Summarize 👍")
+        summary(summarization)
+
+    st.write("#")
+    st.write("---")
+
+    with st.container():
+        # Question Section
+        st.subheader("PDF question-answer section")
+        user_question = st.text_input("Ask a Question from the PDF Files")
+        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:
+            user_input(user_question)
+
+
+if __name__ == "__main__":
+    main()