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Upload app.py
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app.py
CHANGED
@@ -131,7 +131,9 @@ def gpt3_qa(query, answer):
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st.title("Abstractive Question Answering")
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st.write(
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query_text = st.text_input("Input Query", value="Who is the CEO of Apple?")
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@@ -147,25 +149,21 @@ encoder_model = st.selectbox("Select Encoder Model", encoder_models_choice)
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# Choose decoder model
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decoder_models_choice = ["GPT3 (QA_davinci)", "GPT3 (summary_davinci)"
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decoder_model = st.selectbox("Select Decoder Model", decoder_models_choice)
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if encoder_model == "MPNET":
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# Connect to pinecone environment
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pinecone.init(
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api_key=st.secrets["pinecone_mpnet"], environment="us-east1-gcp"
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)
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pinecone_index_name = "week2-all-mpnet-base"
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pinecone_index = pinecone.Index(pinecone_index_name)
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retriever_model = get_mpnet_embedding_model()
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elif encoder_model == "SGPT":
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# Connect to pinecone environment
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pinecone.init(
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api_key=st.secrets["pinecone_sgpt"], environment="us-east1-gcp"
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)
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pinecone_index_name = "week2-sgpt-125m"
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pinecone_index = pinecone.Index(pinecone_index_name)
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retriever_model = get_sgpt_embedding_model()
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st.title("Abstractive Question Answering")
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st.write(
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"The app uses the quarterly earnings call transcripts for 10 companies (Apple, AMD, Amazon, Cisco, Google, Microsoft, Nvidia, ASML, Intel, Micron) for the years 2016 to 2020."
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)
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query_text = st.text_input("Input Query", value="Who is the CEO of Apple?")
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# Choose decoder model
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decoder_models_choice = ["FLAN-T5", "T5", "GPT3 (QA_davinci)", "GPT3 (summary_davinci)"]
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decoder_model = st.selectbox("Select Decoder Model", decoder_models_choice)
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if encoder_model == "MPNET":
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# Connect to pinecone environment
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pinecone.init(api_key=st.secrets["pinecone_mpnet"], environment="us-east1-gcp")
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pinecone_index_name = "week2-all-mpnet-base"
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pinecone_index = pinecone.Index(pinecone_index_name)
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retriever_model = get_mpnet_embedding_model()
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elif encoder_model == "SGPT":
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# Connect to pinecone environment
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pinecone.init(api_key=st.secrets["pinecone_sgpt"], environment="us-east1-gcp")
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pinecone_index_name = "week2-sgpt-125m"
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pinecone_index = pinecone.Index(pinecone_index_name)
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retriever_model = get_sgpt_embedding_model()
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