degbu embeddings

app.py

@@ -14,6 +14,7 @@ from llama_index.core import (
     Settings,
 )
 
+os.environ["OPENAI_API_KEY"] = "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"
 
 from llama_parse import LlamaParse
 
@@ -76,7 +77,7 @@ with st.sidebar:
     # Enter LLM Token
     llm_token = st.text_input(
         "Enter your LLM token",
-        value=None
+        value="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"
     )
 
     # Create LLM
@@ -101,7 +102,7 @@ with st.sidebar:
     # Enter parsing Token
     parse_token = st.text_input(
         "Enter your LlamaParse token",
-        value=None
+        value="llx-uxxwLr1gZmDibaHTl99ISQJtpLSjjfhgDvnosGxu92RdRlb7"
     )
 
     uploaded_file = st.file_uploader(
@@ -142,8 +143,8 @@ with col1:
 
     st.divider()
 
-    index = VectorStoreIndex.from_documents(parsed_document)
-    query_engine = index.as_query_engine()
+    # index = VectorStoreIndex.from_documents(parsed_document)
+    # query_engine = index.as_query_engine()
 
     prompt_txt = 'Summarize this document in a 3-5 sentences.'
     prompt = st.text_area(
@@ -152,8 +153,8 @@ with col1:
         value=prompt_txt
     )
 
-    response = qa_engine.query(prompt)
-    st.write(response.response)
+    # response = qa_engine.query(prompt)
+    # st.write(response.response)
 
 with col2:
     tab1, tab2 = st.tabs(["Uploaded File", "Parsed File",])