Update app.py

app.py

@@ -6,10 +6,10 @@ from langchain.embeddings import HuggingFaceEmbeddings
 from langchain.vectorstores import FAISS
 from langchain.llms import CTransformers
 from langchain.chains import ConversationalRetrievalChain
+from dl_hf_model import dl_hf_model
 from ctransformers import AutoModelForCausalLM
 from langchain_g4f import G4FLLM
 from g4f import Provider, models
-# import spacy
 import requests
 # Define the path for generated embeddings
 DB_FAISS_PATH = 'vectorstore/db_faiss'
@@ -37,8 +37,8 @@ def load_llm():
         
     # )
     llm = G4FLLM(
-        model=models.gpt_4,
-        provider=Provider.ChatBase,
+        model=models.gpt_35_turbo,
+        provider=Provider.DeepAi,
     )
     return llm
 hide_streamlit_style = """
@@ -50,9 +50,9 @@ hide_streamlit_style = """
 st.markdown(hide_streamlit_style, unsafe_allow_html=True) 
 
 # Set the title for the Streamlit app
-st.title("Zendo美女チャットボックス")
+st.title("Coloring Anime ChatBot")
 
-csv_url = "https://huggingface.co/spaces/uyen13/chatgirl2/raw/main/testchatdata.csv"
+csv_url = "https://huggingface.co/spaces/uyen13/chatbot/raw/main/testchatdata.csv"
 # csv_url="https://docs.google.com/uc?export=download&id=1fQ2v2n9zQcoi6JoOU3lCBDHRt3a1PmaE"
 
 # Define the path where you want to save the downloaded file
@@ -83,8 +83,6 @@ llm = load_llm()
 
 # Create a conversational chain
 chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=db.as_retriever())
-# Initialize spaCy with the Japanese model
-# nlp = spacy.load("ja_core_news_sm")
 
 # Function for conversational chat
 def conversational_chat(query):
@@ -98,9 +96,10 @@ if 'history' not in st.session_state:
 
 # Initialize messages
 if 'generated' not in st.session_state:
-    st.session_state['generated'] = ["こんにちは！zendo美女です。何かお探しですか？... 🤗"]
+    st.session_state['generated'] = ["Hello ! Ask me about this page like coloring book,how to buy ... 🤗"]
+
 if 'past' not in st.session_state:
-    st.session_state['past'] = ["チャットはここから"]
+    st.session_state['past'] = ["your chat here"]
 
 # Create containers for chat history and user input
 response_container = st.container()
@@ -109,7 +108,7 @@ container = st.container()
 # User input form
 with container:
     with st.form(key='my_form', clear_on_submit=True):
-        user_input = st.text_input("ChatBox", placeholder="質問をご記入ください...  ", key='input')
+        user_input = st.text_input("ChatBox", placeholder="Ask anything...  ", key='input')
         submit_button = st.form_submit_button(label='Send')
 
     if submit_button and user_input:
@@ -122,6 +121,4 @@ if st.session_state['generated']:
     with response_container:
         for i in range(len(st.session_state['generated'])):
             message(st.session_state["past"][i], is_user=True, key=str(i) + '_user', avatar_style="big-smile")
-            message(st.session_state["generated"][i], key=str(i), avatar_style="thumbs")
-            
-
+            message(st.session_state["generated"][i], key=str(i), avatar_style="thumbs")