add add and requirements

app.py

@@ -0,0 +1,108 @@
+
+import pinecone
+import streamlit as st
+from sentence_transformers import SentenceTransformer
+from transformers import BartTokenizer, BartForConditionalGeneration
+
+
+class BartGenerator:
+    def __init__(self, model_name):
+        self.tokenizer = BartTokenizer.from_pretrained(model_name)
+        self.generator = BartForConditionalGeneration.from_pretrained(model_name)
+
+    def tokenize(self, query, max_length=1024):
+        inputs = self.tokenizer([query], max_length=max_length, return_tensors="pt")
+        return inputs
+
+    def generate(self, query, min_length=20, max_length=40):
+        inputs = self.tokenize(query)
+        ids = self.generator.predict(inputs["input_ids"], num_beams=1, min_length=int(min_length), max_length=int(max_length))
+        answer = self.tokenizer.batch_decode(ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        return answer
+    
+@st.experimental_singleton
+def init_models():
+    retriever = SentenceTransformer("flax-sentence-embeddings/all_datasets_v3_mpnet-base")  
+    generator = BartGenerator("vblagoje/bart_lfqa")
+    return retriever, generator
+
+PINECONE_KEY = st.secrets["PINECONE_KEY"]
+
+@st.experimental_singleton
+def init_pinecone():
+    pinecone.init(api_key=PINECONE_KEY, environment="us-west1-gcp")  # get a free api key from app.pinecone.io
+    return pinecone.Index("abstractive-question-answering")
+
+retriever, generator = init_models()
+index = init_pinecone()
+
+def display_answer(answer):
+    return st.markdown(f"""
+    <div class="container-fluid">
+        <div class="row align-items-start">
+             <div  class="col-md-12 col-sm-12">
+                 <span style="color: #808080;">
+                    {answer}
+                 </span>
+             </div>
+        </div>
+     </div>
+        """, unsafe_allow_html=True)
+
+def display_context(title, context, url):
+    return st.markdown(f"""
+    <div class="container-fluid">
+        <div class="row align-items-start">
+             <div  class="col-md-12 col-sm-12">
+                 <a href={url}>{title}</a>
+                 <br>
+                 <span style="color: #808080;">
+                     <small>{context}</small>
+                 </span>
+             </div>
+        </div>
+     </div>
+        """, unsafe_allow_html=True)
+
+st.write("""
+# Abstractive Question Answering
+Ask me a question!
+""")
+
+st.markdown("""
+<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bootstrap@4.0.0/dist/css/bootstrap.min.css" integrity="sha384-Gn5384xqQ1aoWXA+058RXPxPg6fy4IWvTNh0E263XmFcJlSAwiGgFAW/dAiS6JXm" crossorigin="anonymous">
+""", unsafe_allow_html=True)
+
+def format_query(query, context):
+    context = [f"<P> {m['metadata']['passage_text']}" for m in context]
+    context = " ".join(context)
+    query = f"question: {query} context: {context}"
+    return query
+    
+st.sidebar.subheader("Retriever parameters:")
+top_k = st.sidebar.slider("Top K", min_value=1, max_value=10, value=5)
+
+st.sidebar.subheader("Generator parameters:")
+min_length = st.sidebar.slider("Minimum Length", min_value=1, max_value=50, value=20)
+max_length = st.sidebar.slider("Maximum Length", min_value=1, max_value=100, value=50)
+
+query = st.text_input("Search!", "")
+
+if query != "":
+    with st.spinner(text="Fetching context passages 🚀🚀🚀"):
+        xq = retriever.encode([query]).tolist()
+        xc = index.query(xq, top_k=int(top_k), include_metadata=True)
+        query = format_query(query, xc["matches"])
+
+    with st.spinner(text="Generating answer ✍️✍️✍️"):
+        answer = generator.generate(query, min_length=min_length, max_length=max_length)
+
+    st.write("#### Generated answer:")
+    display_answer(answer)
+    st.write("#### Answer was generated based on the following passages:")
+
+    for m in xc["matches"]:
+        title = m["metadata"]["article_title"]
+        url = "https://en.wikipedia.org/wiki/" + title.replace(" ", "_")
+        context = m["metadata"]["passage_text"]
+        display_context(title, context, url)

requirements.txt

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+pinecone-client
+sentence-transformers