Update app.py

app.py

@@ -1,87 +1,72 @@
-import os
-import warnings
-import nest_asyncio
-import streamlit as st
-from dotenv import load_dotenv
-from DataLoading.Data import get_data
-from llama_index.core import Settings
-from llama_index.llms.groq import Groq
-from llama_index.vector_stores.faiss import FaissVectorStore
-from llama_index.embeddings.huggingface import HuggingFaceEmbedding
-from llama_index.core import StorageContext, load_index_from_storage
-
-nest_asyncio.apply()
-load_dotenv()
-warnings.filterwarnings("ignore")
-
-def init_llm(model_name):
-    return Groq(model=model_name, api_key=os.getenv("GROQ_API_KEY"))
-
-@st.cache_resource
-def load_index(selected_model):
-    curr_direc = os.getcwd()
-    file_path = os.path.join(curr_direc, 'processed_data.csv')
-    # print(file_path)
-    get_data(file_path)
-    model = init_llm(selected_model)
-    embedding_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
-
-    Settings.embed_model = embedding_model
-    Settings.llm = model
-
-    vector_store = FaissVectorStore.from_persist_dir('storage')
-    storage_context = StorageContext.from_defaults(
-        vector_store=vector_store, persist_dir='storage'
-    )
-    index = load_index_from_storage(storage_context=storage_context)
-    return index.as_query_engine()
-
-st.title("Chatbot from ClienterAI")
-
-st.sidebar.header("Settings")
-selected_model = st.sidebar.selectbox(
-    "Select Groq Model:",
-    options=["mixtral-8x7b-32768", "gemma2-9b-it", "llama-3.1-70b-versatile", "llama3-8b-8192", "llava-v1.5-7b-4096-preview"],
-    index=0
-)
-
-query_engine = load_index(selected_model)
-
-if "messages" not in st.session_state:
-    st.session_state["messages"] = []
-
-
-with st.form("chat_form", clear_on_submit=True):
-    user_input = st.text_input("Ask a question based on your data:", "")
-    submitted = st.form_submit_button("Send")
-
-if submitted and user_input:
-    st.session_state["messages"].append({"role": "user", "content": user_input})
-    response = query_engine.query(user_input)
-    ai_response = response
-    st.session_state["messages"].append({"role": "assistant", "content": ai_response})
-
-for message in st.session_state["messages"]:
-    if message["role"] == "user":
-        st.markdown(f"**You:** {message['content']}")
-    else:
-        st.markdown(f"**Assistant:** {message['content']}")
-
-if st.sidebar.button("Clear Chat"):
-    st.session_state["messages"] = []
-    st.sidebar.success("Chat cleared!")
-
-
-st.markdown("""
-    <style>
-            .stForm {
-            position: fixed;
-            align-self: center;
-            bottom: 0;
-            width: 50%;
-            left: 25%;
-            right: 50%;
-            padding: 10px;
-        }
-    <style>
-""", unsafe_allow_html=True)
+import os
+import warnings
+import nest_asyncio
+import streamlit as st
+from dotenv import load_dotenv
+from DataLoading.Data import get_data
+from llama_index.core import Settings
+from llama_index.llms.groq import Groq
+from llama_index.vector_stores.faiss import FaissVectorStore
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from llama_index.core import StorageContext, load_index_from_storage
+
+nest_asyncio.apply()
+load_dotenv()
+warnings.filterwarnings("ignore")
+
+def init_llm(model_name):
+    return Groq(model=model_name, api_key=os.getenv("GROQ_API_KEY"))
+
+@st.cache_resource
+def load_index(selected_model):
+    curr_direc = os.getcwd()
+    file_path = os.path.join(curr_direc, 'processed_data.csv')
+    # print(file_path)
+    get_data(file_path)
+    model = init_llm(selected_model)
+    embedding_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
+
+    Settings.embed_model = embedding_model
+    Settings.llm = model
+
+    vector_store = FaissVectorStore.from_persist_dir('storage')
+    storage_context = StorageContext.from_defaults(
+        vector_store=vector_store, persist_dir='storage'
+    )
+    index = load_index_from_storage(storage_context=storage_context)
+    return index.as_query_engine()
+
+st.title("Chatbot from ClienterAI")
+
+st.sidebar.header("Settings")
+selected_model = st.sidebar.selectbox(
+    "Select Groq Model:",
+    options=["mixtral-8x7b-32768", "gemma2-9b-it", "llama-3.1-70b-versatile", "llama3-8b-8192", "llava-v1.5-7b-4096-preview"],
+    index=0
+)
+
+query_engine = load_index(selected_model)
+
+if "messages" not in st.session_state:
+    st.session_state["messages"] = []
+
+
+with st.form("chat_form", clear_on_submit=True):
+    user_input = st.text_input("Ask a question based on your data:", "")
+    submitted = st.form_submit_button("Send")
+
+if submitted and user_input:
+    st.session_state["messages"].append({"role": "user", "content": user_input})
+    response = query_engine.query(user_input)
+    ai_response = response
+    st.session_state["messages"].append({"role": "assistant", "content": ai_response})
+
+for message in st.session_state["messages"]:
+    if message["role"] == "user":
+        st.markdown(f"**You:** {message['content']}")
+    else:
+        st.markdown(f"**Assistant:** {message['content']}")
+
+if st.sidebar.button("Clear Chat"):
+    st.session_state["messages"] = []
+    st.sidebar.success("Chat cleared!")