Update app.py

app.py

@@ -7,12 +7,14 @@ from langchain.embeddings import HuggingFaceEmbeddings
 from langchain.vectorstores import FAISS
 from langchain_core.vectorstores import InMemoryVectorStore
 from groq import Groq
+from langchain.memory import ConversationBufferMemory  # Import memory
 
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
 client = Groq(api_key="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")
 
+memory = ConversationBufferMemory()
 
 embeddings = HuggingFaceEmbeddings(model_name="heydariAI/persian-embeddings")
 vector_store = InMemoryVectorStore(embeddings)
@@ -24,7 +26,7 @@ def process_pdf_with_langchain(pdf_path):
         text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
         split_documents = text_splitter.split_documents(documents)
         
-        vectorstore = FAISS.from_documents(split_documents, embedding_model)
+        vectorstore = FAISS.from_documents(split_documents, embeddings)
         retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
         return retriever
     except Exception as e:
@@ -49,7 +51,6 @@ def generate_response(query, retriever=None):
 
         context += f"\n\nYou: {query}\nParvizGPT:"
 
-        # ابتدا یک پیام موقت نمایش داده شود
         response = "در حال پردازش..."
 
         retries = 3
@@ -71,9 +72,6 @@ def generate_response(query, retriever=None):
         logger.error(f"Error generating response: {e}")
         return f"Error: {e}"
 
-
-
-        
 def gradio_interface(user_message, chat_box, pdf_file=None):
     global retriever
     if pdf_file is not None:
@@ -108,4 +106,4 @@ with gr.Blocks() as interface:
     user_message.submit(gradio_interface, inputs=[user_message, chat_box, pdf_file], outputs=chat_box)
     clear_memory_btn.click(clear_memory, inputs=[], outputs=chat_box)
 
-interface.launch()
+interface.launch()