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import streamlit as st
import pinecone
import openai
from openai.embeddings_utils import get_embedding
import json

OPENAI_KEY = st.secrets["OPENAI_KEY"]
PINECONE_KEY = st.secrets["PINECONE_KEY"]
INDEX = 'openai-ml-qa'
instructions = {
    "conservative q&a": "Answer the question based on the context below, and if the question can't be answered based on the context, say \"I don't know\"\n\nContext:\n{0}\n\n---\n\nQuestion: {1}\nAnswer:",
    "paragraph about a question":"Write a paragraph, addressing the question, and use the text below to obtain relevant information\"\n\nContext:\n{0}\n\n---\n\nQuestion: {1}\nParagraph long Answer:",
    "bullet points": "Write a bullet point list of possible answers, addressing the question, and use the text below to obtain relevant information\"\n\nContext:\n{0}\n\n---\n\nQuestion: {1}\nBullet point Answer:",
    "summarize problems given a topic": "Write a summary of the problems addressed by the questions below\"\n\n{0}\n\n---\n\n",
    "extract key libraries and tools": "Write a list of libraries and tools present in the context below\"\n\nContext:\n{0}\n\n---\n\n",
    "simple instructions": "{1} given the common questions and answers below \n\n{0}\n\n---\n\n",
    "summarize": "Write an elaborate, paragraph long summary about \"{1}\" given the questions and answers from a public forum on this topic\n\n{0}\n\n---\n\nSummary:",
}

@st.experimental_singleton(show_spinner=False)
def init_openai():
    # initialize connection to OpenAI
    openai.api_key = OPENAI_KEY

@st.experimental_singleton(show_spinner=False)
def init_pinecone(index_name):
    # initialize connection to Pinecone vector DB (app.pinecone.io for API key)
    pinecone.init(
        api_key=PINECONE_KEY,
        environment='us-west1-gcp'
    )
    index = pinecone.Index(index_name)
    stats = index.describe_index_stats()
    dims = stats['dimension']
    count = stats['namespaces']['']['vector_count']
    return index, dims, count

def create_context(question, index, lib_meta, max_len=3750, top_k=5):
    """
    Find most relevant context for a question via Pinecone search
    """
    q_embed = get_embedding(question, engine=f'text-embedding-ada-002')
    res = index.query(
        q_embed, top_k=top_k,
        include_metadata=True, filter={
            'docs': {'$in': lib_meta}
        })
    

    cur_len = 0
    contexts = []
    sources = []

    for row in res['matches']:
        meta = row['metadata']
        text = (
            f"Topic: {meta['thread']}\n"+
            f"Answer: {meta['context']}"
        )
        cur_len += len(text)
        if cur_len < max_len:
            contexts.append(text)
            sources.append(row['metadata'])
        else:
            cur_len -= len(text) + 4
            if max_len - cur_len < 200:
                break
    return "\n\n###\n\n".join(contexts), sources

def answer_question(
    index,
    fine_tuned_qa_model="text-davinci-003",
    question="Am I allowed to publish model outputs to Twitter, without a human review?",
    instruction="Answer the question based on the context below, and if the question can't be answered based on the context, say \"I don't know\"\n\nContext:\n{0}\n\n---\n\nQuestion: {1}\nAnswer:",
    max_len=3550,
    size="curie",
    top_k=5,
    debug=False,
    max_tokens=400,
    stop_sequence=None,
    domains=["huggingface", "tensorflow", "streamlit", "pytorch"],
):
    """
    Answer a question based on the most similar context from the dataframe texts
    """
    context, sources = create_context(
        question,
        index,
        lib_meta=domains,
        max_len=max_len,
        top_k=top_k
    )
    if debug:
        print("Context:\n" + context)
        print("\n\n")
    try:
        # fine-tuned models requires model parameter, whereas other models require engine parameter
        model_param = (
            {"model": fine_tuned_qa_model}
            if ":" in fine_tuned_qa_model
            and fine_tuned_qa_model.split(":")[1].startswith("ft")
            else {"engine": fine_tuned_qa_model}
        )
        #print(instruction.format(context, question))
        response = openai.Completion.create(
            prompt=instruction.format(context, question),
            temperature=0,
            max_tokens=max_tokens,
            top_p=1,
            frequency_penalty=0,
            presence_penalty=0,
            stop=stop_sequence,
            **model_param,
        )
        return response["choices"][0]["text"].strip(), sources
    except Exception as e:
        print(e)
        return ""

def search(index, query, style, top_k, lib_filters):
    if query != "":
        with st.spinner("Retrieving, please wait..."):
            answer, sources = answer_question(
                index,
                question=query,
                instruction=instructions[style],
                top_k=top_k
            )
            # lowercase relevant lib filters
            lib_meta = [lib.lower() for lib in lib_filters.keys() if lib_filters[lib]]
            lower_libs = [lib.lower() for lib in libraries]
        # display the answer
        st.write(answer)
        with st.expander("Sources"):
            for source in sources:
                st.write(f"""
                {source['docs']} > {source['category']} > [{source['thread']}]({source['href']})
                """)

st.markdown("""
<link
  rel="stylesheet"
  href="https://fonts.googleapis.com/css?family=Roboto:300,400,500,700&display=swap"
/>
""", unsafe_allow_html=True)

#model_name = 'mpnet-discourse'

libraries = [
    "Streamlit",
    "HuggingFace",
    "PyTorch",
    "TensorFlow"
]

with st.spinner("Connecting to OpenAI..."):
    retriever = init_openai()

with st.spinner("Connecting to Pinecone..."):
    index, dims, count = init_pinecone(INDEX)

st.write("# ML Q&A")
search = st.container()
query = search.text_input('Ask a framework-specific question!', "")

with search.expander("Search Options"):
    style = st.radio(label='Style', options=[
        'Paragraph about a question', 'Conservative Q&A',
        'Bullet points', 'Summarize problems given a topic',
        'Extract key libraries and tools', 'Simple instructions',
        'Summarize'
    ])
    # add section for filters
    st.write("""
    #### Metadata Filters

    **Libraries**
    """)
    # create two cols
    cols = st.columns(2)
    # add filtering based on library
    lib_filters = {}
    for lib in libraries:
        i = len(lib_filters.keys()) % 2
        with cols[i]:
            lib_filters[lib] = st.checkbox(lib, value=True)
    st.write("---")
    top_k = st.slider(
        "top_k",
        min_value=1,
        max_value=20,
        value=5
    )

st.sidebar.write(f"""
### Info

**Pinecone index name**: {INDEX}

**Pinecone index size**: {count}

**OpenAI embedding model**: *text-embedding-ada-002*

**Vector dimensionality**: {dims}

**OpenAI generation model**: *text-davinci-003*

---

### How it Works

The Q&A tool takes discussions and docs from some of the best Python ML
libraries and collates their content into a natural language search and Q&A tool.

Ask questions like **"How do I use the gradient tape in tensorflow?"** or **"What is the difference
between Tensorflow and PyTorch?"**, choose a answer style, and return relevant results!

The app is powered using OpenAI's embedding service with Pinecone's vector database. The whole process consists
of *three* steps:

**1**. Questions are fed into OpenAI's embeddings service to generate a {dims}-dimensional query vector.

**2**. We use Pinecone to identify similar context vectors (previously encoded from Q&A pages).

**3**. Relevant pages are passed in a new question to OpenAI's generative model, returning our answer.

**How do I make something like this?**

It's easy! Check out the [source code](https://github.com/pinecone-io/examples/tree/master/integrations/openai/beyond_search_webinar) and learn how to [integrate OpenAI and Pinecone in the docs](https://www.pinecone.io/docs/integrations/openai/)!

---

### Usage

If you'd like to restrict your search to a specific library (such as PyTorch or
Streamlit) you can with the *Advanced Options* dropdown. The source of information
can be switched between official docs and forum discussions too!

If you'd like OpenAI to consider more or less pages, try changing the `top_k` slider.

Want to see the original sources that GPT-3 is using to generate the answer? No problem, just click on the **Sources** box.
""")

#if style.lower() == 'conservative q&a':
#    search.info("*Access search options above.*")

if search.button("Go!") or query != "":
    with st.spinner("Retrieving, please wait..."):
        # lowercase relevant lib filters
        lib_meta = [lib.lower() for lib in lib_filters.keys() if lib_filters[lib]]
        # ask the question
        answer, sources = answer_question(
            index,
            question=query,
            instruction=instructions[style.lower()],
            top_k=top_k,
            domains=lib_meta
        )
    # display the answer
    st.write(answer)
    with st.expander("Sources"):
        for source in sources:
            st.write(f"""
            {source['docs']} > {source['category']} > [{source['thread']}]({source['href']})
            """)