Create app.py

app.py

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+from dotenv import load_dotenv
+import streamlit as st
+from PyPDF2 import PdfReader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.chains.question_answering import load_qa_chain
+from langchain.llms import OpenAI
+import time
+import logging
+import os
+
+# Setup logging
+logging.basicConfig(level=logging.INFO,
+                    format='%(asctime)s - %(levelname)s - %(message)s',
+                    filename='app_log.log',  # Log file name
+                    filemode='a')  # Append mode
+
+def process_pdf(pdf):
+    start_time = time.time()
+    pdf_reader = PdfReader(pdf)
+    text = ""
+    for page in pdf_reader.pages:
+        text += page.extract_text() or ""
+    end_time = time.time()
+    logging.info(f"Processed PDF in {end_time - start_time} seconds")
+    return text
+
+def main():
+    load_dotenv()
+    st.set_page_config(page_title="Chat PDF")
+    st.header("Chat PDF 💬")
+
+    if 'chat_history' not in st.session_state:
+        st.session_state.chat_history = []
+
+    pdfs = st.file_uploader("Upload your PDF files", type="pdf", accept_multiple_files=True)
+
+    if pdfs:
+        try:
+            start_time = time.time()
+            text = ""
+            for pdf in pdfs:
+                text += process_pdf(pdf)
+
+            if not text:
+                st.write("No text could be extracted from the PDFs.")
+                return
+
+            processing_time = time.time() - start_time
+            logging.info(f"Total PDF processing time: {processing_time} seconds")
+
+            char_text_splitter = CharacterTextSplitter(separator="\n", chunk_size=1000,
+                                                       chunk_overlap=200, length_function=len)
+            text_chunks = char_text_splitter.split_text(text)
+
+            embeddings = OpenAIEmbeddings()
+            docsearch = FAISS.from_texts(text_chunks, embeddings)
+            llm = OpenAI()
+            chain = load_qa_chain(llm, chain_type="stuff")
+
+            query = st.text_input("Type your question:")
+
+            if query:
+                qa_start_time = time.time()
+                docs = docsearch.similarity_search(query)
+                response = chain.run(input_documents=docs, question=query)
+                qa_end_time = time.time()
+
+                # Update chat history
+                processing_info = f"Processing Time: {qa_end_time - qa_start_time:.2f} seconds"
+                st.session_state.chat_history.append({"question": query, "answer": response, "time": processing_info})
+
+                # Clear the input
+                st.session_state.query = ""
+
+                # Display chat history in a text area
+                history_text = ""
+                for chat in st.session_state.chat_history:
+                    history_text += f"Q: {chat['question']}\nA: {chat['answer']}\n{chat['time']}\n---\n"
+                st.text_area("Chat History", history_text, height=300)
+
+        except Exception as e:
+            logging.error(f"An error occurred: {e}")
+            st.error(f"An error occurred: {e}")
+
+if __name__ == "__main__":
+    main()