Create app.py

app.py

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+import pinecone
+import streamlit as st
+
+API = st.text_area('Enter API key:')
+
+# connect to pinecone environment
+pinecone.init(
+    api_key="API",
+    environment="us-central1-gcp"  # find next to API key in console
+)
+
+index_name = "abstractive-question-answering"
+
+# check if the abstractive-question-answering index exists
+if index_name not in pinecone.list_indexes():
+    # create the index if it does not exist
+    pinecone.create_index(
+        index_name,
+        dimension=768,
+        metric="cosine"
+    )
+
+# connect to abstractive-question-answering index we created
+index = pinecone.Index(index_name)
+
+import torch
+from sentence_transformers import SentenceTransformer
+
+# set device to GPU if available
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+# load the retriever model from huggingface model hub
+retriever = SentenceTransformer("flax-sentence-embeddings/all_datasets_v3_mpnet-base", device=device)
+
+from transformers import BartTokenizer, BartForConditionalGeneration
+
+# load bart tokenizer and model from huggingface
+tokenizer = BartTokenizer.from_pretrained('vblagoje/bart_lfqa')
+generator = BartForConditionalGeneration.from_pretrained('vblagoje/bart_lfqa').to('cpu')
+
+def query_pinecone(query, top_k):
+    # generate embeddings for the query
+    xq = retriever.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, context):
+    # extract passage_text from Pinecone search result and add the <P> tag
+    context = [f"<P> {m['metadata']['text']}" for m in context]
+    # concatinate all context passages
+    context = " ".join(context)
+    # contcatinate the query and context passages
+    query = f"question: {query} context: {context}"
+    return query
+
+def generate_answer(query):
+    # tokenize the query to get input_ids
+    inputs = tokenizer([query], trunication=True, max_length=1024, return_tensors="pt")
+    # use generator to predict output ids
+    ids = generator.generate(inputs["input_ids"], num_beams=2, min_length=20, max_length=64)
+    # use tokenizer to decode the output ids
+    answer = tokenizer.batch_decode(ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+    return pprint(answer)
+
+query = st.text_area('Enter your question:')
+context = query_pinecone(query, top_k=5)
+query = format_query(query, context["matches"])
+generate_answer(query)