Update app.py

app.py

@@ -7,7 +7,7 @@ from langchain.vectorstores import FAISS
 from langchain.chains import ConversationalRetrievalChain
 from langchain.chat_models import ChatOpenAI
 from langchain.memory import ConversationBufferMemory
-
+import shutil
 from langchain.prompts import PromptTemplate
 
 
@@ -16,6 +16,7 @@ openai_api_key = os.environ.get("OPENAI_API_KEY")
 
 class AdvancedPdfChatbot:
     def __init__(self, openai_api_key):
+        self.memory = deque(maxlen=20)
         os.environ["OPENAI_API_KEY"] = openai_api_key
         self.embeddings = OpenAIEmbeddings()
         self.text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
@@ -56,9 +57,9 @@ You are a file-based knowledge assistant that interacts with users like ChatGPT.
 User: "Can you summarize the main argument from the introduction of the file?"  
 Response: "Sure! The introduction discusses [key points] and highlights the central argument that [main idea]. This can be found on page 2 under the heading 'Introduction'."  
 User: "Where can I find the definition of 'symbolic interactionism' in the document?"  
-Response: "The definition of 'symbolic interactionism' appears on page 12 under the subheading 'Key Theoretical Concepts'."  
+Bot Response: "The definition of 'symbolic interactionism' appears on page 12 under the subheading 'Key Theoretical Concepts'."  
 User: "Explain the concept of 'cognitive dissonance' as it is presented in the document."  
-Response: "In the document, 'cognitive dissonance' is defined as [definition from the file]. It appears in the context of [brief explanation] and can be found on page 15 under the section 'Theoretical Foundations'."  
+Bot Response: "In the document, 'cognitive dissonance' is defined as [definition from the file]. It appears in the context of [brief explanation] and can be found on page 15 under the section 'Theoretical Foundations'."  
 
 NOTE : DESCRIBE/SUMMARY should always return the overall summary of the documents in well documented and descriptions of the topic in great details.
 
@@ -72,12 +73,15 @@ NOTE : DESCRIBE/SUMMARY should always return the overall summary of the document
 
         self.prompt = PromptTemplate(template=self.template, input_variables=["context", "question"])
 
-    def load_and_process_pdf(self, pdf_path):
+def load_and_process_pdf(self, pdf_path):
+    try:
         loader = PyPDFLoader(pdf_path)
         documents = loader.load()
         texts = self.text_splitter.split_documents(documents)
         self.db = FAISS.from_documents(texts, self.embeddings)
         self.setup_conversation_chain()
+    except Exception as e:
+        return f"An error occurred while processing the PDF: {e}"
 
     def setup_conversation_chain(self):
         self.qa_chain = ConversationalRetrievalChain.from_llm(
@@ -96,16 +100,23 @@ NOTE : DESCRIBE/SUMMARY should always return the overall summary of the document
 # Initialize the chatbot
 pdf_chatbot = AdvancedPdfChatbot(openai_api_key)
 
+
+
 def upload_pdf(pdf_file):
     if pdf_file is None:
         return "Please upload a PDF file."
-    file_path = pdf_file.name
+    file_path = f"uploads/{pdf_file.name}"
+    with open(file_path, "wb") as f:
+        shutil.copyfileobj(pdf_file, f)
+    pdf_chatbot.memory.clear()  # Clears past memory before loading a new file
     pdf_chatbot.load_and_process_pdf(file_path)
     return "PDF uploaded and processed successfully. You can now start chatting!"
 
+
+
 def respond(message, history):
     bot_message = pdf_chatbot.chat(message)
-    history.append((message, bot_message))
+    history.append((f"User: {message}", f"Bot: {bot_message}"))
     return "", history
 
 def clear_chatbot():