eagle0504 commited on
Commit
faaebd2
Β·
1 Parent(s): 746994a

kill submit button

Browse files
Files changed (1) hide show
  1. app.py +25 -28
app.py CHANGED
@@ -59,29 +59,26 @@ special_threshold = st.sidebar.number_input(
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  st.sidebar.success(
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  "The 'distances' score indicates the proximity of your question to our database questions (lower is better). The 'ai_judge' ranks the similarity between user's question and database answers independently (higher is better)."
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  )
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- submit_button = st.sidebar.button("Submit", type="primary")
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  clear_button = st.sidebar.button("Clear Conversation", key="clear")
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  if clear_button:
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  st.session_state.messages = []
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67
 
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  # Load the dataset from a provided source.
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- initial_input = "Please press the submit button!"
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- if submit_button:
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- if option == "YSA":
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- begin_t = time.time()
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- dataset = load_dataset(
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- "eagle0504/youthless-homeless-shelter-web-scrape-dataset-qa-formatted"
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- )
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- end_t = time.time()
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- st.success(f"{option} Database loaded. | Time: {end_t - begin_t} sec")
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- initial_input = "Tell me about YSA"
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- else:
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- begin_t = time.time()
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- dataset = load_dataset("eagle0504/larkin-web-scrape-dataset-qa-formatted")
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- end_t = time.time()
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- st.success(f"{option} Database loaded. | Time: {end_t - begin_t} sec")
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- initial_input = "Tell me about Larkin"
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  # Initialize a new client for ChromeDB.
@@ -103,17 +100,17 @@ collection = client.create_collection(combined_string)
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  # Embed and store the first N supports for this demo
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- if submit_button:
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- with st.spinner("Loading, please be patient with us ... πŸ™"):
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- L = len(dataset["train"]["questions"])
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- begin_t = time.time()
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- collection.add(
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- ids=[str(i) for i in range(0, L)], # IDs are just strings
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- documents=dataset["train"]["questions"], # Enter questions here
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- metadatas=[{"type": "support"} for _ in range(0, L)],
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- )
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- end_t = time.time()
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- st.success(f"Add to VectorDB. | Time: {end_t - begin_t} sec")
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  # React to user input
 
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  st.sidebar.success(
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  "The 'distances' score indicates the proximity of your question to our database questions (lower is better). The 'ai_judge' ranks the similarity between user's question and database answers independently (higher is better)."
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  )
 
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  clear_button = st.sidebar.button("Clear Conversation", key="clear")
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  if clear_button:
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  st.session_state.messages = []
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66
 
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  # Load the dataset from a provided source.
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+ if option == "YSA":
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+ begin_t = time.time()
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+ dataset = load_dataset(
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+ "eagle0504/youthless-homeless-shelter-web-scrape-dataset-qa-formatted"
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+ )
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+ end_t = time.time()
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+ st.success(f"{option} Database loaded. | Time: {end_t - begin_t} sec")
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+ initial_input = "Tell me about YSA"
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+ else:
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+ begin_t = time.time()
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+ dataset = load_dataset("eagle0504/larkin-web-scrape-dataset-qa-formatted")
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+ end_t = time.time()
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+ st.success(f"{option} Database loaded. | Time: {end_t - begin_t} sec")
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+ initial_input = "Tell me about Larkin"
 
 
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83
 
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  # Initialize a new client for ChromeDB.
 
100
 
101
 
102
  # Embed and store the first N supports for this demo
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+ with st.spinner("Loading, please be patient with us ... πŸ™"):
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+ # L = len(dataset["train"]["questions"])
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+ L = 30
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+ begin_t = time.time()
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+ collection.add(
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+ ids=[str(i) for i in range(0, L)], # IDs are just strings
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+ documents=dataset["train"]["questions"], # Enter questions here
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+ metadatas=[{"type": "support"} for _ in range(0, L)],
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+ )
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+ end_t = time.time()
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+ st.success(f"Add to VectorDB. | Time: {end_t - begin_t} sec")
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115
 
116
  # React to user input