Update app.py

app.py

@@ -26,10 +26,12 @@ from fastapi.middleware.cors import CORSMiddleware
 from fastapi.templating import Jinja2Templates
 import json
 from deep_translator import GoogleTranslator
+from langchain.embeddings import HuggingFaceEmbeddings
+from sentence_transformers import SentenceTransformer
 import re
 GOOGLE_API_KEY= os.getenv("HF_TOKEN")
 warnings.filterwarnings("ignore")
-model = ChatGoogleGenerativeAI(model="gemini-pro",google_api_key=GOOGLE_API_KEY,
+model = ChatGoogleGenerativeAI(model="gemini-2.0-flash-experimental",google_api_key=GOOGLE_API_KEY,
                              temperature=0.2,convert_system_message_to_human=True,system_message="You are the Hotel voice chatbot and your name is Hotel Helper. Your goal is to provide accurate, professional, and helpful answers to user queries based on the hotel's data. Always ensure your responses are clear and concise. Give response within 10-15 words only. Respond in the same language as the user's query."
                               )
 pdf_loader = PyPDFLoader("data/ITCGrandChola.pdf")
@@ -37,7 +39,9 @@ pages = pdf_loader.load_and_split()
 text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
 context = "\n\n".join(str(p.page_content) for p in pages)
 texts = text_splitter.split_text(context)
-embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001",google_api_key=GOOGLE_API_KEY)
+#embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001",google_api_key=GOOGLE_API_KEY)
+sentence_transformer_model = SentenceTransformer('BAAI/bge-small-en-v1.5')
+embeddings = HuggingFaceEmbeddings(model=sentence_transformer_model)
 vector_index = Chroma.from_texts(texts, embeddings).as_retriever(search_kwargs={"k":10})
 qa_chain = RetrievalQA.from_chain_type(
     model,