Upload 2 files

app.py

@@ -0,0 +1,109 @@
+from PyPDF2 import PdfReader
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.vectorstores import FAISS
+from langchain.chains.question_answering import load_qa_chain
+from langchain.chains import load_chain
+from langchain.llms import OpenAI
+import streamlit as st
+import pyautogui
+import os, shutil
+
+def delete_directory(directory_path):
+    try:
+        shutil.rmtree(directory_path)
+        print(f"Directory '{directory_path}' successfully deleted.")
+    except Exception as e:
+        print(f"Error deleting directory '{directory_path}': {e}")
+
+st.set_page_config(page_title="Query any Pdf", page_icon="📄")
+
+st.title("📄 PDF Query Bot 📄")
+st.write("Made with ❤️ by Mainak")
+
+def return_response(query,document_search,chain):
+    query = query
+    docs = document_search.similarity_search(query)
+    result = chain.run(input_documents=docs, question=query)
+    return result
+
+uploaded_file = st.file_uploader("Upload a PDF File", type=["pdf"])
+
+# API key input box
+api_key = st.text_input("Enter Your OpenAI API Key",type="password")
+
+if not  uploaded_file:
+    try:
+        delete_directory('faiss_index')
+    except:
+        pass
+
+if st.button('Submit'):
+    if api_key:
+        if uploaded_file is not None:
+            # Read text from the uploaded file
+            os.environ["OPENAI_API_KEY"] = api_key
+            with st.spinner('Wait for it...'):
+                pdfreader = PdfReader(uploaded_file)
+                # read text from pdf
+                raw_text = ''
+                for i, page in enumerate(pdfreader.pages):
+                    content = page.extract_text()
+                    if content:
+                        raw_text += content
+
+                text_splitter = CharacterTextSplitter(
+                    separator = "\n",
+                    chunk_size = 800,
+                    chunk_overlap  = 200,
+                    length_function = len,
+                )
+                texts = text_splitter.split_text(raw_text)
+                embeddings = OpenAIEmbeddings()
+                document_search = FAISS.from_texts(texts, embeddings)
+                document_search.save_local("faiss_index")
+        else:
+            st.warning("Please enter your Pdf File")
+    else:
+        st.warning("Please enter your API key")
+if os.path.exists("faiss_index"):
+    # if st.checkbox("chat"):
+        if api_key:
+            if uploaded_file is not None:
+                if "messages" not in st.session_state:
+                    st.session_state.messages = []
+
+                # Display chat messages from history on app rerun
+                for message in st.session_state.messages:
+                    with st.chat_message(message["role"]):
+                        st.markdown(message["content"])
+                if prompt := st.chat_input("What is up?          Type 'exit' for leave the chat"):
+                    # Display user message in chat message container
+                    with st.chat_message("user"):
+                        st.markdown(prompt)
+                    # Add user message to chat history
+                    st.session_state.messages.append({"role": "user", "content": prompt})
+                os.environ["OPENAI_API_KEY"] = api_key
+                embeddings = OpenAIEmbeddings()
+                document_search = FAISS.load_local("faiss_index", embeddings)
+                chain = load_qa_chain(OpenAI(), chain_type="stuff")
+                if prompt is None:
+                    re='Ask me anything about the pdf'
+                elif prompt=='exit':
+                    delete_directory('faiss_index')
+                    pyautogui.hotkey('f5') #Simulates F5 key press = page refresh
+                else:
+                    with st.spinner('Typping...'):
+                        re=return_response(str(prompt),document_search,chain)
+                response = f"PDF Mate: {re}"
+                # Display assistant response in chat message container
+                with st.chat_message("assistant"):
+                    st.markdown(response)
+                # Add assistant response to chat history
+                st.session_state.messages.append({"role": "assistant", "content": response})
+            else:
+                st.warning("Please enter your Pdf File")
+        else:
+            st.warning("Please enter your API key")
+else:
+    pass

requirements.txt

@@ -0,0 +1,8 @@
+langchain
+openai
+PyPDF2
+faiss-cpu
+tiktoken
+pinecone-client
+streamlit
+pyautogui