Update app.py

app.py

@@ -8,7 +8,6 @@ from langchain.chains import LLMChain
 from langchain_community.llms import HuggingFacePipeline
 from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
 from dotenv import load_dotenv
-from htmlTemplates import css
 
 # Set Streamlit page configuration
 st.set_page_config(page_title="Chat with Notes and AI", page_icon=":books:", layout="wide")
@@ -113,15 +112,16 @@ def handle_question(question, vectorstore=None):
     return llm_chain.invoke({"instruction": question})
 
 def main():
-    st.write(css, unsafe_allow_html=True)
+    st.title("Ask AI :books:")
 
     # Initialize session state
     if "vectorstore" not in st.session_state:
         st.session_state.vectorstore = None
 
-    st.header("Chat with Notes and AI :books:")
+    # Folder for subject data
+    data_folder = "data"
 
-    # Subject selection dropdown
+    # Subject selection
     subjects = [
         "A Trumped World", "Agri Tax in Punjab", "Assad's Fall in Syria", "Elusive National Unity", "Europe and Trump 2.0",
         "Going Down with Democracy", "Indonesia's Pancasila Philosophy", "Pakistan in Choppy Waters",
@@ -130,13 +130,10 @@ def main():
         "The Power of Big Oil", "Trump 2.0 and Pakistan's Emerging Foreign Policy", "Trump and the World 2.0",
         "Trump vs BRICS", "US-China Trade War", "War on Humanity", "Women's Suppression in Afghanistan"
     ]
-    data_folder = "data"
     subject_folders = {subject: os.path.join(data_folder, subject.replace(' ', '_')) for subject in subjects}
     selected_subject = st.sidebar.selectbox("Select a Subject:", subjects)
 
-    st.sidebar.info(f"You have selected: {selected_subject}")
-
-    # Process data folder for notes preview and question answering
+    # Process data folder for vectorstore
     subject_folder_path = subject_folders[selected_subject]
     if os.path.exists(subject_folder_path):
         raw_text = get_text_files_content(subject_folder_path)
@@ -145,21 +142,11 @@ def main():
             vectorstore = get_vectorstore(text_chunks)
             st.session_state.vectorstore = vectorstore
 
-            # Display preview of notes
-            st.subheader("Preview of Notes")
-            st.text_area("Preview Content:", value=raw_text[:2000], height=300, disabled=True)  # Show a snippet of the text
-        else:
-            st.error("No content found for the selected subject.")
-    else:
-        st.error(f"Folder not found for {selected_subject}.")
-
     # Chat interface
-    st.subheader("Ask Your Question")
     question = st.text_input("Ask a question about your selected subject:")
     if question:
         if st.session_state.vectorstore:
             response = handle_question(question, st.session_state.vectorstore)
-            st.subheader("Response:")
             st.write(response)
         else:
             st.warning("Please load the content for the selected subject before asking a question.")