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app.py

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+import pandas as pd
+from tqdm import tqdm
+import pinecone
+import torch
+from sentence_transformers import SentenceTransformer
+from transformers import (
+    pipeline,
+    AutoTokenizer,
+    AutoModelForCausalLM,
+    AutoModelForSeq2SeqLM,
+)
+import streamlit as st
+import openai
+
+
+# Initialize models from HuggingFace
+
+
+@st.experimental_singleton
+def get_t5_model():
+    return pipeline("summarization", model="t5-small", tokenizer="t5-small")
+
+
+@st.experimental_singleton
+def get_flan_t5_model():
+    return pipeline(
+        "summarization", model="google/flan-t5-small", tokenizer="google/flan-t5-small"
+    )
+
+
+@st.experimental_singleton
+def get_mpnet_embedding_model():
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    model = SentenceTransformer(
+        "sentence-transformers/all-mpnet-base-v2", device=device
+    )
+    model.max_seq_length = 512
+    return model
+
+
+@st.experimental_singleton
+def get_sgpt_embedding_model():
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    model = SentenceTransformer(
+        "Muennighoff/SGPT-125M-weightedmean-nli-bitfit", device=device
+    )
+    model.max_seq_length = 512
+    return model
+
+
+@st.experimental_memo
+def save_key(api_key):
+    return api_key
+
+
+def query_pinecone(query, top_k, model, index):
+    # generate embeddings for the query
+    xq = model.encode([query]).tolist()
+    # search pinecone index for context passage with the answer
+    xc = index.query(xq, top_k=top_k, include_metadata=True)
+    return xc
+
+
+def format_query(query_results):
+    # extract passage_text from Pinecone search result
+    context = [result["metadata"]["Text"] for result in query_results["matches"]]
+    return context
+
+
+def gpt3_summary(text):
+    response = openai.Completion.create(
+        model="text-davinci-003",
+        prompt=text + "\n\nTl;dr",
+        temperature=0.1,
+        max_tokens=512,
+        top_p=1.0,
+        frequency_penalty=0.0,
+        presence_penalty=1,
+    )
+    return response.choices[0].text
+
+
+def gpt3_qa(query, answer):
+    response = openai.Completion.create(
+        model="text-davinci-003",
+        prompt="Q: " + query + "\nA: " + answer,
+        temperature=0,
+        max_tokens=512,
+        top_p=1,
+        frequency_penalty=0.0,
+        presence_penalty=0.0,
+        stop=["\n"],
+    )
+    return response.choices[0].text
+
+
+st.title("Abstractive Question Answering - APPL")
+
+query_text = st.text_input("Input Query", value="Who is the CEO of Apple?")
+
+num_results = int(st.number_input("Number of Results to query", 1, 5, value=2))
+
+
+# Choose encoder model
+
+encoder_models_choice = ["MPNET", "SGPT"]
+
+encoder_model = st.selectbox("Select Encoder Model", encoder_models_choice)
+
+
+# Choose decoder model
+
+decoder_models_choice = ["GPT3 (QA_davinci)", "GPT3 (text_davinci)", "T5", "FLAN-T5"]
+
+decoder_model = st.selectbox("Select Decoder Model", decoder_models_choice)
+
+
+if encoder_model == "MPNET":
+    # Connect to pinecone environment
+    pinecone.init(
+        api_key="ea9fd320-6f8a-4edd-bf41-9e972b95cbf9", environment="us-east1-gcp"
+    )
+    pinecone_index_name = "week2-all-mpnet-base"
+    pinecone_index = pinecone.Index(pinecone_index_name)
+    retriever_model = get_mpnet_embedding_model()
+
+elif encoder_model == "SGPT":
+    # Connect to pinecone environment
+    pinecone.init(
+        api_key="0d8215d7-4ad5-4c76-8c45-4a40c0f6a1b7", environment="us-east1-gcp"
+    )
+    pinecone_index_name = "week2-sgpt-125m"
+    pinecone_index = pinecone.Index(pinecone_index_name)
+    retriever_model = get_sgpt_embedding_model()
+
+
+query_results = query_pinecone(query_text, num_results, retriever_model, pinecone_index)
+
+context_list = format_query(query_results)
+
+
+st.subheader("Answer:")
+
+
+if decoder_model == "GPT3 (text_davinci)":
+    openai_key = st.text_input(
+        "Enter OpenAI key",
+        value="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",
+        type="password",
+    )
+    api_key = save_key(openai_key)
+    openai.api_key = api_key
+    output_text = []
+    for context_text in context_list:
+        output_text.append(gpt3_summary(context_text))
+    generated_text = " ".join(output_text)
+    st.write(gpt3_summary(generated_text))
+
+elif decoder_model == "GPT3 - QA":
+    openai_key = st.text_input(
+        "Enter OpenAI key",
+        value="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",
+        type="password",
+    )
+    api_key = save_key(openai_key)
+    openai.api_key = api_key
+    output_text = []
+    for context_text in context_list:
+        output_text.append(gpt3_qa(query_text, context_text))
+    generated_text = " ".join(output_text)
+    st.write(gpt3_qa(query_text, generated_text))
+
+elif decoder_model == "T5":
+    t5_pipeline = get_t5_model()
+    output_text = []
+    for context_text in context_list:
+        output_text.append(t5_pipeline(context_text)[0]["summary_text"])
+    generated_text = " ".join(output_text)
+    st.write(t5_pipeline(generated_text)[0]["summary_text"])
+
+elif decoder_model == "FLAN-T5":
+    flan_t5_pipeline = get_flan_t5_model()
+    output_text = []
+    for context_text in context_list:
+        output_text.append(flan_t5_pipeline(context_text)[0]["summary_text"])
+    generated_text = " ".join(output_text)
+    st.write(flan_t5_pipeline(generated_text)[0]["summary_text"])
+
+show_retrieved_text = st.checkbox("Show Retrieved Text", value=False)
+
+if show_retrieved_text:
+
+    st.subheader("Retrieved Text:")
+
+    for context_text in context_list:
+        st.markdown(f"- {context_text}")

requirements.txt

@@ -0,0 +1,8 @@
+pandas
+tqdm
+pinecone-client
+torch
+sentence_transformers
+transformers
+streamlit
+openai