added the chat interface and chat-memory

app.py

@@ -3,9 +3,11 @@ from llama_index.core import StorageContext, load_index_from_storage, VectorStor
 from llama_index.llms.huggingface import HuggingFaceInferenceAPI
 from dotenv import load_dotenv
 from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from llama_index.memory import ChatMemoryBuffer
 from llama_index.core import Settings
 import os
 import base64
+import datetime
 
 
 # Load environment variables
@@ -32,8 +34,80 @@ DATA_DIR = "data"
 os.makedirs(DATA_DIR, exist_ok=True)
 os.makedirs(PERSIST_DIR, exist_ok=True)
 
+
+# Here, a memory token limit of 1500 is set
+memory = ChatMemoryBuffer.from_defaults(token_limit=1500)
+
+
+def displayPDF(file):
+    with open(file, "rb") as f:
+        base64_pdf = base64.b64encode(f.read()).decode('utf-8')
+    pdf_display = f'<iframe src="data:application/pdf;base64,{base64_pdf}" width="100%" height="600" type="application/pdf"></iframe>'
+    st.markdown(pdf_display, unsafe_allow_html=True)
+
+
+def data_ingestion():
+    documents = SimpleDirectoryReader(DATA_DIR).load_data()
+    storage_context = StorageContext.from_defaults()
+    index = VectorStoreIndex.from_documents(documents)
+    index.storage_context.persist(persist_dir=PERSIST_DIR)
+
+
+def handle_query(query):
+    storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)
+    index = load_index_from_storage(storage_context)
+    chat_text_qa_msgs = [
+        (
+            "user",
+            """You are a Q&A assistant. Created by Abraham Paul [linkedin](https://www.linkedin.com/in/abraham-paul-16317a235/) a Software / AI Engineer. 
+            Your primary objective is to provide accurate and helpful answers based on the instructions and context provided. 
+            If a question falls outside the given context or scope, kindly guide the user to ask questions that align with the provided context.
+            Context:
+            {context_str}
+            Question:
+            {query_str}
+            """
+        )
+    ]
+    text_qa_template = ChatPromptTemplate.from_messages(chat_text_qa_msgs)
+    query_engine = index.as_query_engine(text_qa_template=text_qa_template, memory=memory)
+    answer = query_engine.query(query)
+    
+    if hasattr(answer, 'response'):
+        return answer.response
+    elif isinstance(answer, dict) and 'response' in answer:
+        return answer['response']
+    else:
+        return "Sorry, I couldn't find an answer."
+
+
 # Streamlit app initialization
-st.title("Chat with your Doc - PDF Assistant 📄")
+st.title("PDF Chatbot - with your doc")
+st.markdown("Get insights from your data – just ask!👇")
+
+if 'messages' not in st.session_state:
+    st.session_state.messages = [{'role': 'assistant', "content": 'Upload your pdf doc and ask me anything about it, Lets chat!!'}]
+
+with st.sidebar:
+    st.markdown("**Created by [Abraham](https://www.linkedin.com/in/abraham-paul-16317a235/)**")
+    st.title(':blue[Get Started]:')
+    uploaded_file = st.file_uploader("Upload your PDF and Click Submit")
+    if st.button("Submit"):
+        with st.spinner("Processing..."):
+            filepath = "data/saved_pdf.pdf"
+            with open(filepath, "wb") as f:
+                f.write(uploaded_file.getbuffer())
+            data_ingestion()  # Process PDF every time new file is uploaded
+            st.success("Done")
+
+user_prompt = st.chat_input("Ask me anything from the uploaded document:")
+
+if user_prompt:
+    st.session_state.messages.append({'role': 'user', "content": user_prompt})
+    response = handle_query(user_prompt)
+    st.session_state.messages.append({'role': 'assistant', "content": response})
 
-st.markdown("Get insights from your data – just chat!👇")
+for message in st.session_state.messages:
+    with st.chat_message(message['role']):
+        st.write(message['content'])