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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
+
+
+# connect to pinecone environment
+pinecone.init(
+    api_key="d4f20339-fcc1-4a11-b04f-3800203eacd2",
+    environment="us-east1-gcp"  
+)
+
+index_name = "abstractive-question-answering"
+
+index = pinecone.Index(index_name)
+
+# Initialize models from HuggingFace
+
+@st.cache_resource
+def get_t5_model():
+    return pipeline("summarization", model="t5-base", tokenizer="t5-base")
+
+@st.cache_resource
+def get_flan_t5_model():
+    return pipeline("summarization", model="google/flan-t5-base", tokenizer="google/flan-t5-base")
+    
+@st.cache_resource
+def get_embedding_model():
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    model = SentenceTransformer("flax-sentence-embeddings/all_datasets_v3_mpnet-base", device=device)
+    model.max_seq_length = 512
+    return model
+    
+@st.cache_data()
+def save_key(api_key):
+    return api_key
+
+retriever_model = get_embedding_model()
+
+def query_pinecone(query, top_k, model):
+    # 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']['merged_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))
+
+query_results = query_pinecone(query_text, num_results, retriever_model)
+
+context_list = format_query(query_results)
+
+
+
+# Choose decoder model
+
+models_choice = ["GPT3 (text_davinci)", "GPT3 - QA", "T5", "FLAN-T5"]
+
+decoder_model = st.selectbox(
+    'Select Decoder Model',
+    models_choice)
+
+st.subheader("Answer:")
+
+
+if decoder_model == "GPT3 (text_davinci)":
+  openai_key = st.text_input("Enter OpenAI key")
+  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")
+  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"])
+
+st.subheader("Retrieved Text:")
+
+for context_text in context_list:
+  st.markdown(f"- {context_text}")
+

requirements.txt

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+datasets
+pinecone-client
+sentence-transformers
+torch
+tqdm
+openai