Update app.py

app.py

@@ -5,7 +5,8 @@ import logging
 from langchain.document_loaders import PDFPlumberLoader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.prompts import ChatPromptTemplate
-from langchain.llms import HuggingFaceLLM
+from langchain.chains import LLMChain  # This is used for chaining prompts and models
+from langchain.llms import HuggingFacePipeline
 from transformers import pipeline
 
 # Configure logging
@@ -13,7 +14,7 @@ logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
 # Page configuration
-st.set_page_config(page_title="DeepSeek Chatbot RAG", page_icon="🤖", layout="centered")
+st.set_page_config(page_title="DeepSeek Chatbot - ruslanmv.com", page_icon="🤖", layout="centered")
 
 # Initialize session state for chat history
 if "messages" not in st.session_state:
@@ -71,13 +72,15 @@ def generate_response_with_langchain(question, context):
 
     prompt = ChatPromptTemplate.from_template(prompt_template)
 
-    # Initialize HuggingFace model with LangChain's HuggingFaceLLM
+    # Initialize HuggingFace pipeline
     hf_pipeline = pipeline("text-generation", model=selected_model)
-    llm = HuggingFaceLLM(pipeline=hf_pipeline)
+    huggingface_llm = HuggingFacePipeline(pipeline=hf_pipeline)
 
-    # Use LangChain to generate an answer
-    chain = prompt | llm
-    response = chain.invoke({"question": question, "context": context})
+    # Set up LangChain's LLMChain
+    chain = LLMChain(prompt=prompt, llm=huggingface_llm)
+
+    # Use the chain to invoke the model with context and question
+    response = chain.run({"question": question, "context": context})
     return response
 
 # Chat interface