Update app.py

app.py

@@ -10,7 +10,6 @@ from langchain.llms import OpenAI
 import time
 import logging
 
-
 # Setup logging
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 
@@ -37,56 +36,53 @@ def main():
 
     pdfs = st.file_uploader("Upload your PDF files", type="pdf", accept_multiple_files=True)
 
-
     if pdfs:
         try:
+            start_time = time.time()
             text = ""
-            if pdfs:
-                try:
-                    start_time = time.time()
-                    for pdf in pdfs:
-                        pdf_reader = PdfReader(pdf)
-                        for page in pdf_reader.pages:
-                            page_text = page.extract_text() or ""
-                            text += page_text
-
-                    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,
+            for pdf in pdfs:
+                pdf_reader = PdfReader(pdf)
+                for page in pdf_reader.pages:
+                    page_text = page.extract_text() or ""
+                    text += page_text
+
+            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)
+            text_chunks = char_text_splitter.split_text(text)
 
-                    embeddings = OpenAIEmbeddings()
-                    docsearch = FAISS.from_texts(text_chunks, embeddings)
-                    llm = OpenAI()
+            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:")
+            query = st.text_input("Type your question:")
 
-                    if query:
-                        docs = docsearch.similarity_search(query)
-                        response = chain.run(input_documents=docs, question=query)
+            if query:
+                docs = docsearch.similarity_search(query)
+                response = chain.run(input_documents=docs, question=query)
 
-                    # Update chat history
-                    st.session_state.chat_history.append({"question": query, "answer": response})
+                # Update chat history
+                st.session_state.chat_history.append({"question": query, "answer": response})
                 
-                    #clear the input
-                    st.session_state.query=""
+                # 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.write("---")
-
-                except Exception as e:
-                    logging.error(f"An error occurred: {e}")
-                    st.error(f"An error occurred: {e}")
+                # Display chat history
+                for chat in st.session_state.chat_history:
+                    st.text(f"Q: {chat['question']}\nA: {chat['answer']}")
+                    st.write("---")
+
+        except Exception as e:
+            logging.error(f"An error occurred: {e}")
+            st.error(f"An error occurred: {e}")
 
 if __name__ == "__main__":
     main()