Tried different approach

app.py

@@ -10,52 +10,100 @@ 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_name = st.text_input(
+    'Embed Model name', "Gooly/gte-small-en-fine-tuned-e-commerce")
 
-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)
+llm_model_name = st.text_input(
+    'Embed Model name', "mistralai/Mistral-7B-Instruct-v0.2")
 
 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"))
-    string_data = stringio.read()
-    with st.expander("Uploaded HTML"):
-        st.write(string_data)
+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:
+        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(
+            "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)
+
+    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_id = str(uuid.uuid4())
 
-    document = Document(text=string_data)
-    document.metadata["id"] = document_id
-    documents = [document]
+#     document = Document(text=string_data)
+#     document.metadata["id"] = document_id
+#     documents = [document]
 
-    filters = MetadataFilters(
-        filters=[ExactMatchFilter(key="id", value=document_id)])
+#     filters = MetadataFilters(
+#         filters=[ExactMatchFilter(key="id", value=document_id)])
 
-    index = VectorStoreIndex.from_documents(
-        documents, show_progress=True, metadata={"source": "HTML"}, service_context=service_context)
+#     index = VectorStoreIndex.from_documents(
+#         documents, show_progress=True, metadata={"source": "HTML"}, service_context=service_context)
 
-    retriever = index.as_retriever()
+#     retriever = index.as_retriever()
 
-    ranked_nodes = retriever.retrieve(
-        "Get me all the information about the product")
+#     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)
+#     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)
+#     query_engine = index.as_query_engine(
+#         filters=filters, service_context=service_context)
 
-    response = query_engine.query(
-        "Get me all the information about the product")
+#     response = query_engine.query(
+#         "Get me all the information about the product")
 
-    st.write(response)
+#     st.write(response)