Update app.py

app.py

@@ -15,40 +15,39 @@ load_dotenv()
 def main():
     st.set_page_config(page_title="PDF Chat")
     st.header("Chat with your PDFs 💬")
-    
+
     # Upload PDF files
     pdf_files = st.file_uploader("Upload your PDF files", type="pdf", accept_multiple_files=True)
-    
     if pdf_files:
         for idx, pdf_file in enumerate(pdf_files):
-            pdf_reader = PdfReader(pdf_file)
-            
-            text = ""
-            for page in pdf_reader.pages:
-                text += page.extract_text()
-            
-            text_splitter = CharacterTextSplitter(
-                separator="\n",
-                chunk_size=1000,
-                chunk_overlap=200,
-                length_function=len
-            )
-            chunks = text_splitter.split_text(text)
-            
-            embeddings = OpenAIEmbeddings()
-            knowledge_base = FAISS.from_texts(chunks, embeddings)
-            
-            user_question = st.text_input(f"Ask a question about '{pdf_file.name}':", key=f"question_{idx}")
-            if user_question:
-                docs = knowledge_base.similarity_search(user_question)
+            try:
+                pdf_reader = PdfReader(pdf_file)
+                text = ""
+                for page in pdf_reader.pages:
+                    text += page.extract_text()
+                
+                text_splitter = CharacterTextSplitter(
+                    separator="\n", chunk_size=1000, chunk_overlap=200, length_function=len
+                )
+                chunks = text_splitter.split_text(text)
                 
-                llm = OpenAI()
-                chain = load_qa_chain(llm, chain_type="stuff")
-                with get_openai_callback() as cb:
-                    response = chain.run(input_documents=docs, question=user_question)
-                    print(cb)
+                embeddings = OpenAIEmbeddings()
+                knowledge_base = FAISS.from_texts(chunks, embeddings)
                 
-                st.write(response)
+                user_question = st.text_input(f"Ask a question about '{pdf_file.name}':", key=f"question_{idx}")
+                if user_question:
+                    docs = knowledge_base.similarity_search(user_question)
+                    
+                    llm = OpenAI()
+                    chain = load_qa_chain(llm, chain_type="stuff")
+                    
+                    with get_openai_callback() as cb:
+                        response = chain.run(input_documents=docs, question=user_question)
+                        print(cb)
+                    
+                    st.write(response)
+            except Exception as e:
+                st.error(f"An error occurred while processing '{pdf_file.name}': {str(e)}. This file may be protected by the author, or contain scanned text which this basic demo is not set up to process.")
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()