Spaces:
Sleeping
Sleeping
button added
Browse files
app.py
CHANGED
@@ -59,7 +59,7 @@ 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.
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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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@@ -103,11 +103,11 @@ 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 ... π"):
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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"]
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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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@@ -132,8 +132,8 @@ if prompt := st.chat_input(initial_input):
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ref = pd.DataFrame(
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{
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"idx": idx,
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"questions": [dataset["train"]["questions"][
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"answers": [dataset["train"]["answers"][
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"distances": results["distances"][0],
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}
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)
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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.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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# 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 ... π"):
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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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ref = pd.DataFrame(
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{
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"idx": idx,
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"questions": [dataset["train"]["questions"][i] for i in idx],
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"answers": [dataset["train"]["answers"][i] for i in idx],
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"distances": results["distances"][0],
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}
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)
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