Delete llm_tutor.py

llm_tutor.py

@@ -1,146 +0,0 @@
-import time
-import os
-import joblib
-import streamlit as st
-from utils.questions_dataset import system_instruction, get_model_tools
-from google.genai import types
-from google import genai
-
-# GOOGLE_API_KEY=os.environ.get('GOOGLE_API_KEY')
-GEMINI_API_KEY = "AIzaSyAjpHA08BUwLhK-tIlORxcB18RAp3541-M"
-client = genai.Client(api_key=GEMINI_API_KEY)
-
-new_chat_id = f'{time.time()}'
-MODEL_ROLE = 'ai'
-AI_AVATAR_ICON = '✨'
-
-# Create a data/ folder if it doesn't already exist
-try:
-    os.mkdir('data/')
-except:
-    # data/ folder already exists
-    pass
-
-# Load past chats (if available)
-try:
-    past_chats: dict = joblib.load('data/past_chats_list')
-except:
-    past_chats = {}
-
-# Sidebar allows a list of past chats
-with st.sidebar:
-    st.write('# Past Chats')
-    if st.session_state.get('chat_id') is None:
-        st.session_state.chat_id = st.selectbox(
-            label='Pick a past chat',
-            options=[new_chat_id] + list(past_chats.keys()),
-            format_func=lambda x: past_chats.get(x, 'New Chat'),
-            placeholder='_',
-        )
-    else:
-        # This will happen the first time AI response comes in
-        st.session_state.chat_id = st.selectbox(
-            label='Pick a past chat',
-            options=[new_chat_id, st.session_state.chat_id] + list(past_chats.keys()),
-            index=1,
-            format_func=lambda x: past_chats.get(x, 'New Chat' if x != st.session_state.chat_id else st.session_state.chat_title),
-            placeholder='_',
-        )
-
-    # Save new chats after a message has been sent to AI
-    st.session_state.chat_title = f'ChatSession-{st.session_state.chat_id}'
-    
-
-st.write('# Chat with LSAT Tutor')
-
-# Chat history (allows to ask multiple questions)
-try:
-    st.session_state.messages = joblib.load(
-        f'data/{st.session_state.chat_id}-st_messages'
-    )
-    st.session_state.gemini_history = joblib.load(
-        f'data/{st.session_state.chat_id}-gemini_messages'
-    )
-except:
-    st.session_state.messages = []
-    st.session_state.gemini_history = []
-    print('new_cache made')
-
-
-st.session_state.chat = client.chats.create(model='gemini-2.0-flash',
-                                            config=types.GenerateContentConfig(
-                                            tools=[get_model_tools()],
-                                            system_instruction=system_instruction),
-                                            history=st.session_state.gemini_history
-                                               )
-
-# Display chat messages from history on app rerun
-for message in st.session_state.messages:
-    with st.chat_message(
-        name=message['role'],
-        avatar=message.get('avatar'),
-    ):
-        st.markdown(message['content'])
-
-# React to user input
-if prompt := st.chat_input('Your message here...'):
-    # Save this as a chat for later
-    if st.session_state.chat_id not in past_chats.keys():
-        past_chats[st.session_state.chat_id] = st.session_state.chat_title
-        joblib.dump(past_chats, 'data/past_chats_list')
-    # Display user message in chat message container
-    with st.chat_message('user'):
-        st.markdown(prompt)
-    # Add user message to chat history
-    st.session_state.messages.append(
-        dict(
-            role='user',
-            content=prompt,
-        )
-    )
-    ## Send message to AI
-    response = st.session_state.chat.send_message_stream(
-        prompt,
-    )
-    # Display assistant response in chat message container
-    with st.chat_message(
-        name=MODEL_ROLE,
-        avatar=AI_AVATAR_ICON,
-    ):
-        message_placeholder = st.empty()
-        full_response = ''
-        assistant_response = response
-        # Streams in a chunk at a time
-        for chunk in response:
-            # Simulate stream of chunk
-            if chunk.text == None:
-                full_response = "No response!! Report to admin!"
-
-            for ch in chunk.text.split(' '):
-                full_response += ch + ' '
-                time.sleep(0.05)
-                # Rewrites with a cursor at end
-                message_placeholder.write(full_response + '▌')
-        # Write full message with placeholder
-        message_placeholder.write(full_response)
-
-            # Add assistant response to chat history
-        st.session_state.messages.append(
-            dict(
-                role=MODEL_ROLE,
-                content=full_response,
-                avatar=AI_AVATAR_ICON,
-            )
-        )
-
-    st.session_state.gemini_history = st.session_state.chat.get_history()
-    
-    # Save to file
-    joblib.dump(
-        st.session_state.messages,
-        f'data/{st.session_state.chat_id}-st_messages',
-    )
-    joblib.dump(
-        st.session_state.gemini_history,
-        f'data/{st.session_state.chat_id}-gemini_messages',
-    )