Delete stm.py

stm.py

@@ -1,70 +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 conversation history in session state
-if "conversation_history" not in st.session_state:
-    st.session_state.conversation_history = []
-
-# 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"
-    
-    # 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
-
-        file_paths = []  # List to store saved file paths
-        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}")
-
-# Chat interface
-st.write("### Chat Interface")
-
-chat_display = "\n".join(st.session_state.conversation_history)
-# st.text_area("Conversation History", chat_display, height=300, disabled=True)
-
-# Input for user question
-user_input = st.text_input("Ask a question:")
-llm_model = llm_option
-
-if st.button("Retrieve and Answer"):
-    if user_input or uploaded_files:
-        answer = multimodelrag(user_input, file_paths, embeding,  llm_model,conversation)
-
-        # Update conversation history
-        st.session_state.conversation_history.append(f"User: {user_input}")
-        st.session_state.conversation_history.append(f"AI: {answer}")
-
-        # Refresh chat display
-        chat_display = "\n".join(st.session_state.conversation_history)
-        st.text_area("Conversation History", chat_display, height=400, disabled=True)
-
-        st.write("### Answer:")
-        st.write(answer)
-
-