Update app.py

app.py

@@ -12,21 +12,21 @@ from langchain_chroma import Chroma
 import tempfile
 from langchain_cohere import CohereEmbeddings
 
-st.set_page_config(page_title="Document Genie", layout="wide")
+#st.set_page_config(page_title="Document Genie", layout="wide")
 
-st.markdown("""
-## PDFChat: Get instant insights from your PDF
+#st.markdown("""
+### PDFChat: Get instant insights from your PDF
 
-This chatbot is built using the Retrieval-Augmented Generation (RAG) framework, leveraging Google's Generative AI model Gemini-PRO. It processes uploaded PDF documents by breaking them down into manageable chunks, creates a searchable vector store, and generates accurate answers to user queries. This advanced approach ensures high-quality, contextually relevant responses for an efficient and effective user experience.
+#This chatbot is built using the Retrieval-Augmented Generation (RAG) framework, leveraging Google's Generative AI model Gemini-PRO. It processes uploaded PDF documents by breaking them down into manageable chunks, creates a searchable vector store, and generates accurate answers to user queries. This advanced approach ensures high-quality, contextually relevant responses for an efficient and effective user experience.
 
-### How It Works
+#### How It Works
 
-Follow these simple steps to interact with the chatbot:
+#Follow these simple steps to interact with the chatbot:
 
-1. **Upload Your Document**: The system accepts a PDF file at one time, analyzing the content to provide comprehensive insights.
+#1. **Upload Your Document**: The system accepts a PDF file at one time, analyzing the content to provide comprehensive insights.
 
-2. **Ask a Question**: After processing the document, ask any question related to the content of your uploaded document for a precise answer.
-""")
+#2. **Ask a Question**: After processing the document, ask any question related to the content of your uploaded document for a precise answer.
+#""")
 
 #def get_pdf(pdf_docs):
 #   loader = PyPDFLoader(pdf_docs)
@@ -86,8 +86,11 @@ def embedding(chunk,query):
     chain = get_conversational_chain()
     response = chain({"input_documents": doc, "question": query}, return_only_outputs=True)
     print(response)
-    st.write("Reply: ", response["output_text"])
+    return response["output_text"]
+    #st.write("Reply: ", response["output_text"])
 
+if 'messages' not in st.session_state:
+    st.session_state.messages = [{'role': 'assistant', "content": 'Hello! Upload a PDF and ask me anything about its content.'}]
 
     
 st.header("Chat with your pdf💁")
@@ -103,3 +106,11 @@ if st.button("Submit & Process", key="process_button"):
         if query:
             embedding(text_chunks,query)
         st.success("Done")
+if query:
+    st.session_state.messages.append({'role': 'user', "content": query})
+    response = embedding(text_chunks,query)
+    st.session_state.messages.append({'role': 'assistant', "content": response})
+
+for message in st.session_state.messages:
+    with st.chat_message(message['role']):
+        st.write(message['content'])