Added new streamlit app

app.py

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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"]
+
+llm = HuggingFaceInferenceAPI(
+    model_name="mistralai/Mistral-7B-Instruct-v0.2", token=inference_api_key)
+
+embed_model = HuggingFaceInferenceAPIEmbedding(
+    model_name="Gooly/gte-small-en-fine-tuned-e-commerce",
+    token=inference_api_key,
+    model_kwargs={"device": ""},
+    encode_kwargs={"normalize_embeddings": True},
+)
+
+service_context = ServiceContext.from_defaults(
+    embed_model=embed_model, llm=llm)
+
+html_file = st.file_uploader("Upload a html file", type=["html"])
+
+if html_file is not None:
+    stringio = StringIO(html_file.getvalue().decode("utf-8"))
+    st.write(stringio)
+
+    string_data = stringio.read()
+    st.write(string_data)
+
+    document_id = 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)
+
+    query_engine = index.as_query_engine(
+        filters=filters, service_context=service_context)
+
+    response = query_engine.query("What is the current price of this product?")
+
+    st.write(response)

requirements.txt

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+streamlit
+llama_index
+uuid