Update app.py

app.py

@@ -1,28 +1,33 @@
 import gradio as gr
-
 # from langchain.llms import GooglePalm
-# from langchain.document_loaders.csv_loader import CSVLoader
-# from langchain_huggingface import HuggingFaceEmbeddings
-# from langchain.vectorstores import FAISS
-
-
-from langchain_community.llms import GooglePalm
-from langchain_community.document_loaders import CSVLoader
-from langchain_community.vectorstores import FAISS
-from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_google_genai import GoogleGenerativeAI
 
 
 api_key = "AIzaSyCdM_aAIsW_nPbjarOF83mbX1_z1cVX2_M"
 
-llm = GooglePalm(google_api_key = api_key, temperature=0.7)
-
+llm = GoogleGenerativeAI(model="models/text-bison-001", google_api_key=api_key)
+# llm = GooglePalm(google_api_key = api_key, temperature=0.7)
 
 loader = CSVLoader(file_path='aiotsmartlabs_faq.csv', source_column = 'prompt')
 data = loader.load()
 
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+import warnings
+
+# Suppress specific warnings if they are not critical
+warnings.filterwarnings("ignore", category=UserWarning, message="TypedStorage is deprecated")
+warnings.filterwarnings("ignore", category=FutureWarning, message="`resume_download` is deprecated")
+
+# Define the embedding model
+# Using a smaller model for demonstration purposes; adjust according to your needs
+model_name = "BAAI/bge-m3"
 
-instructor_embeddings = HuggingFaceEmbeddings(model_name = "BAAI/bge-m3")
-vectordb = FAISS.from_documents(documents = data, embedding = instructor_embeddings)
+# Initialize HuggingFace embeddings
+instructor_embeddings = HuggingFaceEmbeddings(model_name=model_name)
+
+# Create FAISS vector store from documents
+vectordb = FAISS.from_documents(documents=data, embedding=instructor_embeddings)
 
 retriever = vectordb.as_retriever()
 
@@ -58,6 +63,67 @@ def chatresponse(message, history):
 gr.ChatInterface(chatresponse).launch()
 
 
+
+# import gradio as gr
+
+# # from langchain.llms import GooglePalm
+# # from langchain.document_loaders.csv_loader import CSVLoader
+# # from langchain_huggingface import HuggingFaceEmbeddings
+# # from langchain.vectorstores import FAISS
+
+
+# from langchain_community.llms import GooglePalm
+# from langchain_community.document_loaders import CSVLoader
+# from langchain_community.vectorstores import FAISS
+# from langchain_huggingface import HuggingFaceEmbeddings
+
+
+# api_key = "AIzaSyCdM_aAIsW_nPbjarOF83mbX1_z1cVX2_M"
+
+# llm = GooglePalm(google_api_key = api_key, temperature=0.7)
+
+
+# loader = CSVLoader(file_path='aiotsmartlabs_faq.csv', source_column = 'prompt')
+# data = loader.load()
+
+
+# instructor_embeddings = HuggingFaceEmbeddings(model_name = "BAAI/bge-m3")
+# vectordb = FAISS.from_documents(documents = data, embedding = instructor_embeddings)
+
+# retriever = vectordb.as_retriever()
+
+# from langchain.prompts import PromptTemplate
+
+# prompt_template = """Given the following context and a question, generate an answer based on the context only.
+
+# In the answer try to provide as much text as possible from "response" section in the source document context without making much changes.
+# If somebody asks "Who are you?" or a similar phrase, state "I am Rishi's assistant built using a Large Language Model!"
+# If the answer is not found in the context, kindly state "I don't know. Please ask Rishi on Discord. Discord Invite Link: https://discord.gg/6ezpZGeCcM. Or email at [email protected]" Don't try to make up an answer.
+
+# CONTEXT: {context}
+
+# QUESTION: {question}"""
+
+# PROMPT = PromptTemplate(
+#     template = prompt_template, input_variables = ["context", "question"]
+# )
+
+# from langchain.chains import RetrievalQA
+
+# chain = RetrievalQA.from_chain_type(llm = llm,
+#             chain_type="stuff",
+#             retriever=retriever,
+#             input_key="query",
+#             return_source_documents=True,
+#             chain_type_kwargs = {"prompt": PROMPT})
+
+# def chatresponse(message, history):
+#     output = chain(message)
+#     return output['result']
+
+# gr.ChatInterface(chatresponse).launch()
+
+
 # import gradio as gr
 # from langchain.llms import GooglePalm