Update app.py

app.py

@@ -2,67 +2,70 @@ 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"
+res = st.button('Submit')
+if res = True:
+    # connect to pinecone environment
+    pinecone.init(
+        api_key="API",
+        environment="us-central1-gcp"  # find next to API key in console
     )
-
-# 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)
+    
+    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:')
+    s = st.button('Submit')
+    if s = True:
+        context = query_pinecone(query, top_k=5)
+        query = format_query(query, context["matches"])
+        generate_answer(query)