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import streamlit as st
import os
from io import StringIO
from llama_index.llms import HuggingFaceInferenceAPI
from llama_index.embeddings import HuggingFaceInferenceAPIEmbedding
from llama_index import ServiceContext, VectorStoreIndex
from llama_index.schema import Document
import uuid
from llama_index.vector_stores.types import MetadataFilters, ExactMatchFilter
inference_api_key = st.secrets["INFRERENCE_API_TOKEN"]
embed_model_name = st.text_input(
'Embed Model name', "Gooly/gte-small-en-fine-tuned-e-commerce")
llm_model_name = st.text_input(
'Embed Model name', "mistralai/Mistral-7B-Instruct-v0.2")
query = st.text_input(
'Query', "What is the price of the product?")
html_file = st.file_uploader("Upload a html file", type=["html"])
if st.button('Start Pipeline'):
if html_file is not None and embed_model_name is not None and llm_model_name is not None and query is not None:
st.write('Running Pipeline')
llm = HuggingFaceInferenceAPI(
model_name=llm_model_name, token=inference_api_key)
embed_model = HuggingFaceInferenceAPIEmbedding(
model_name=embed_model_name,
token=inference_api_key,
model_kwargs={"device": ""},
encode_kwargs={"normalize_embeddings": True},
)
service_context = ServiceContext.from_defaults(
embed_model=embed_model, llm=llm)
stringio = StringIO(html_file.getvalue().decode("utf-8"))
string_data = stringio.read()
with st.expander("Uploaded HTML"):
st.write(string_data)
document_id = str(uuid.uuid4())
document = Document(text=string_data)
document.metadata["id"] = document_id
documents = [document]
filters = MetadataFilters(
filters=[ExactMatchFilter(key="id", value=document_id)])
index = VectorStoreIndex.from_documents(
documents, show_progress=True, metadata={"source": "HTML"}, service_context=service_context)
retriever = index.as_retriever()
ranked_nodes = retriever.retrieve(
query)
with st.expander("Ranked Nodes"):
for node in ranked_nodes:
st.write(node.node.get_content(), "-> Score:", node.score)
query_engine = index.as_query_engine(
filters=filters, service_context=service_context)
response = query_engine.query(query)
st.write(response.response_txt)
st.write(response.source_nodes)
else:
st.error('Please fill in all the fields')
else:
st.write('Press start to begin')
# if html_file is not None:
# stringio = StringIO(html_file.getvalue().decode("utf-8"))
# string_data = stringio.read()
# with st.expander("Uploaded HTML"):
# st.write(string_data)
# document_id = str(uuid.uuid4())
# document = Document(text=string_data)
# document.metadata["id"] = document_id
# documents = [document]
# filters = MetadataFilters(
# filters=[ExactMatchFilter(key="id", value=document_id)])
# index = VectorStoreIndex.from_documents(
# documents, show_progress=True, metadata={"source": "HTML"}, service_context=service_context)
# retriever = index.as_retriever()
# ranked_nodes = retriever.retrieve(
# "Get me all the information about the product")
# with st.expander("Ranked Nodes"):
# for node in ranked_nodes:
# st.write(node.node.get_content(), "-> Score:", node.score)
# query_engine = index.as_query_engine(
# filters=filters, service_context=service_context)
# response = query_engine.query(
# "Get me all the information about the product")
# st.write(response)
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