Update app.py

app.py

@@ -7,9 +7,10 @@ from llama_index.core import Settings
 import os
 import base64
 
-
+# Load environment variables
 load_dotenv()
 
+# Configure LLM and Embedding settings
 Settings.llm = HuggingFaceInferenceAPI(
     model_name="google/gemma-1.1-7b-it",
     tokenizer_name="google/gemma-1.1-7b-it",
@@ -22,21 +23,21 @@ Settings.embed_model = HuggingFaceEmbedding(
     model_name="BAAI/bge-small-en-v1.5"
 )
 
-# Define the directory for persistent storage and data
+# Define directory paths
 PERSIST_DIR = "./db"
 DATA_DIR = "data"
 
-# Ensure data directory exists
+# Create directories if they don't exist
 os.makedirs(DATA_DIR, exist_ok=True)
 os.makedirs(PERSIST_DIR, exist_ok=True)
 
-def displayPDF(file):
+def display_pdf(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():
+def ingest_data():
     documents = SimpleDirectoryReader(DATA_DIR).load_data()
     storage_context = StorageContext.from_defaults()
     index = VectorStoreIndex.from_documents(documents)
@@ -48,7 +49,7 @@ def handle_query(query):
     chat_text_qa_msgs = [
     (
         "user",
-        """You are a Q&A chatbot named Chatbot, created by Prateek. your main goal is to provide answers as accurately as possible, based on the instructions and context you have been given. If a question does not match the provided context or is outside the scope of the document, kindly advise the user to ask questions within the context of the document.
+        """You are a Q&A chatbot created by Prateek Mohan. Your main goal is to provide accurate answers based on the given context. If a question is outside the scope of the document, kindly advise the user to ask within the context.
         Context:
         {context_str}
         Question:
@@ -68,14 +69,12 @@ def handle_query(query):
     else:
         return "Sorry, I couldn't find an answer."
 
-
-# Streamlit app initialization
-st.title("Chat with your PDF")
-st.markdown("Built by PRATEEK(https://github.com/theSuriya)")
-
+# Streamlit app
+st.title("Talk to your  PDF")
+st.markdown("by Prateek Mohan (https://github.com/prtkmhn/)")
 
 if 'messages' not in st.session_state:
-    st.session_state.messages = [{'role': 'assistant', "content": 'Chat to PDF'}]
+    st.session_state.messages = [{'role': 'system', "content": 'Chat to PDF'}]
 
 with st.sidebar:
     st.title("Menu:")
@@ -85,7 +84,7 @@ with st.sidebar:
             filepath = "data/saved_pdf.pdf"
             with open(filepath, "wb") as f:
                 f.write(uploaded_file.getbuffer())
-            data_ingestion() 
+            ingest_data() 
             st.success("Done")
 
 user_prompt = st.chat_input("Query")
@@ -95,5 +94,4 @@ if user_prompt:
     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'])
+    st.write(message['content'])