Update app.py

app.py

@@ -6,20 +6,16 @@ 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')
-
-# Configure logging to write to a file
 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)
@@ -31,13 +27,10 @@ def process_pdf(pdf):
     return text
 
 def main():
-    # Replace with the appropriate path or setup for Hugging Face Spaces
     load_dotenv()
-
     st.set_page_config(page_title="Chat PDF")
     st.header("Chat PDF 💬")
 
-    # Initialize session state for chat history
     if 'chat_history' not in st.session_state:
         st.session_state.chat_history = []
 
@@ -48,10 +41,7 @@ def main():
             start_time = time.time()
             text = ""
             for pdf in pdfs:
-                pdf_reader = PdfReader(pdf)
-                for page in pdf_reader.pages:
-                    page_text = page.extract_text() or ""
-                    text += page_text
+                text += process_pdf(pdf)
 
             if not text:
                 st.write("No text could be extracted from the PDFs.")
@@ -67,24 +57,26 @@ def main():
             embeddings = OpenAIEmbeddings()
             docsearch = FAISS.from_texts(text_chunks, embeddings)
             llm = OpenAI()
-
-            chain = load_qa_chain(llm, chain_type="stuff")  
+            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
-                st.session_state.chat_history.append({"question": query, "answer": response})
-                
+                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
                 for chat in st.session_state.chat_history:
-                    st.text(f"Q: {chat['question']}\nA: {chat['answer']}")
+                    st.text(f"Q: {chat['question']}\nA: {chat['answer']}\n{chat['time']}")
                     st.write("---")
 
         except Exception as e: