Delete app.py

app.py

@@ -1,80 +0,0 @@
-import streamlit as st
-import os
-import shutil
-from hybrid_search import multimodelrag
-import warnings
-
-warnings.filterwarnings("ignore")
-
-# Streamlit UI
-st.set_page_config(layout="wide")
-st.title("AI Document Processor with Conversational RAG")
-
-# Initialize chat history in session state
-if "messages" not in st.session_state:
-    st.session_state.messages = []
-
-# Sidebar for file upload and settings
-with st.sidebar:
-    uploaded_files = st.file_uploader(
-        "Upload multiple files (PDF, DOCX, Excel, CSV)",
-        type=["pdf", "docx", "xlsx", "csv"],
-        accept_multiple_files=True
-    )
-
-    embeding = st.selectbox("Select Memory Mode", ["open_source", "openai"], index=0)
-    conversation = st.selectbox("Number of conversation", [2, 4, 6], index=0)
-    llm_option = st.selectbox("Select LLM Model", ["GPT-4o", "GPT-4o-mini"], index=1)
-
-    temp_dir = "temp_uploaded_files"
-
-    # Initialize file_paths to prevent undefined variable errors
-    file_paths = []  
-
-    # Clear the previous uploads when new files are uploaded
-    if uploaded_files:
-        if os.path.exists(temp_dir):
-            shutil.rmtree(temp_dir)  # Delete the old directory and its contents
-        os.makedirs(temp_dir)  # Create a fresh directory
-
-        for file in uploaded_files:
-            file_path = os.path.join(temp_dir, file.name)
-            with open(file_path, "wb") as f:
-                f.write(file.read())  # Save file locally
-            file_paths.append(file_path)
-            st.write(f"✅ Saved: {file.name}")
-
-# Button to clear conversation history
-if st.button("Clear Conversation History"):
-    st.session_state.messages = []
-
-# Display previous chat messages
-for message in st.session_state.messages:
-    with st.chat_message(message["role"]):
-        st.markdown(message["content"], unsafe_allow_html=True)  # Allow better formatting
-
-# User input
-if user_input := st.chat_input("Ask a question:"):
-    # Display user message
-    with st.chat_message("user"):
-        st.markdown(user_input)
-    st.session_state.messages.append({"role": "user", "content": user_input})
-
-    # Get AI response
-    answer = multimodelrag(user_input, file_paths, embeding, llm_option, conversation)
-
-    # Check if answer is valid
-    if isinstance(answer, dict):
-        chat_display = "\n\n".join(
-            [f"📂 <b>File:</b> <code>{key}</code><br><br>📝 <b>Extracted Text:</b><br><br>{value}<br>{'-'*50}" 
-            for key, value in answer.items()]
-        )
-    else:
-        chat_display = f"❌ **Error:** Unexpected response format from AI model.\n\n{answer}"
-
-    # Display AI response in chat
-    with st.chat_message("assistant"):
-        st.markdown(chat_display, unsafe_allow_html=True)  # Allow HTML formatting for bold text
-
-    # Store assistant response in conversation history
-    st.session_state.messages.append({"role": "assistant", "content": chat_display})