Upload app.py with huggingface_hub

app.py

@@ -7,16 +7,23 @@ import gradio as gr
 
 from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 from sentence_transformers import SentenceTransformer
-from langchain.document_loaders import TextLoader
+#from langchain.document_loaders import TextLoader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.vectorstores import FAISS as LangChainFAISS
-from langchain.docstore import InMemoryDocstore
+#from langchain.embeddings import HuggingFaceEmbeddings
+#from langchain.vectorstores import FAISS as LangChainFAISS
+#from langchain.docstore import InMemoryDocstore
 from langchain.schema import Document
-from langchain.llms import HuggingFacePipeline
+#from langchain.llms import HuggingFacePipeline
 from huggingface_hub import login
 from huggingface_hub import upload_file
 
+from langchain_community.document_loaders import TextLoader
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS as LangChainFAISS
+from langchain_community.docstore.in_memory import InMemoryDocstore
+from langchain_community.llms import HuggingFacePipeline
+
+
 # Extract the Knowledge Base ZIP
 if os.path.exists("md_knowledge_base.zip"):
     with zipfile.ZipFile("md_knowledge_base.zip", "r") as zip_ref:
@@ -123,13 +130,20 @@ def answer_fn(question):
 # Gradio Interface
 def chat_fn(user_message, history):
     bot_response = answer_fn(user_message)
-    history = history + [(user_message, bot_response)]
+    history = history + [{"role": "user", "content": user_message}, {"role": "assistant", "content": bot_response}]
     return history, history
 
 
+#def chat_fn(user_message, history):
+ #   bot_response = answer_fn(user_message)
+  #  history = history + [(user_message, bot_response)]
+   # return history, history
+
+
 with gr.Blocks() as demo:
     gr.Markdown("## 📘 University of Hull Assistant")
-    chatbot = gr.Chatbot()
+    #chatbot = gr.Chatbot()
+    chatbot = gr.Chatbot(label="University of Hull Assistant", type="messages")
     state = gr.State([])
 
     user_input = gr.Textbox(placeholder="Ask a question about University of Hull...", show_label=False)