app.py

import os 

import google.generativeai as palm
import streamlit as st
import pandas as pd
import faiss
import hdbscan
from sklearn.feature_extraction.text import CountVectorizer

from src.modelling.topics.topic_extractor import (
    TopicExtractionConfig, TopicExtractor
)
from src.modelling.topics.class_tf_idf import ClassTfidfTransformer
from src import deploy_utils


def get_prompt(title, reviews):
    return f"""We are doing a marketing research analysis, in particular we are trying to understand what users thing about a particular market in order to generate tips for future sellers.
In particular, we are interesting to analyze the market for "{title}"

This is what amazon customers are saying about similar products:
{reviews}

Can you write some recomendations about how can we disrupt this market? Try to propose the necesary methodology to create a breaking product."""


def setup_palm():
    palm.configure(api_key=os.environ.get('PALM_TOKEN'))

@st.cache_data
def load_data():
    reviews = pd.read_csv("data/filtered_reviews.csv").set_index("reviewID")
    products = pd.read_csv("data/products.csv")

    return reviews, products


def load_uncached_models():
    topic_extraction_config = TopicExtractionConfig(
        vectorizer_model=CountVectorizer(
            ngram_range=(1, 3), stop_words="english"),
        ctfidf_model=ClassTfidfTransformer(reduce_frequent_words=True),
        number_of_representative_documents=5,
        review_text_key="summary",
    )

    topic_extractor = TopicExtractor(topic_extraction_config)

    clusterer = hdbscan.HDBSCAN(
        min_cluster_size=5, min_samples=5, metric="precomputed")

    return topic_extractor, clusterer


@st.cache_resource
def load_models():
    product_model = deploy_utils.load_model("all-MiniLM-L6-v2")
    reviews_model = deploy_utils.load_model(
        "https://tfhub.dev/google/universal-sentence-encoder/4"
    )
    product_indexer = faiss.read_index("vectordb/populated.index")

    return reviews_model, product_model, product_indexer


def render_cta_link(url, label, font_awesome_icon):
    st.markdown(
        '<link rel="stylesheet" href="<https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css>">',
        unsafe_allow_html=True,
    )
    button_code = f"""<a href="{url}" target=_blank><i class="fa {font_awesome_icon}"></i> {label}</a>"""
    return st.markdown(button_code, unsafe_allow_html=True)


def handler_review_query():
    relevant_products = deploy_utils.query_relevant_documents(
        product_model=product_model,
        indexer=product_indexer,
        products=products,
        query_text=st.session_state.user_search_query,
    )

    # TODO: check if there are relevant products

    relevant_reviews = deploy_utils.get_relevant_reviews(
        relevant_products, reviews)

    raw_topic_assigment = deploy_utils.clusterize_reviews(
        relevant_reviews, reviews_model, clusterer)
    relevant_reviews["topic"] = raw_topic_assigment
    reviews_with_topics = relevant_reviews[relevant_reviews["topic"] != -1]

    # TODO: check if there are still topics

    extracted_topics = topic_extractor(reviews_with_topics)

    key_reviews = deploy_utils.get_key_reviews(
        reviews_with_topics,
        extracted_topics,
    )

    reviews_prompt = deploy_utils.key_reviews_to_prompt(key_reviews)
    prompt = get_prompt(st.session_state.user_search_query, reviews_prompt)
    st.session_state.user_prompt = prompt



def palm_handler():
    response = palm.generate_text(prompt=st.session_state.user_prompt)
    st.session_state.palm_output = response


def render_search():
    """
    Render the search form in the sidebar.
    """
    with st.sidebar:
        query = st.text_input(
            label="What kind of product are you trying to sell?",
            placeholder="Your magic idea goes here ✨",
            key="user_search_query",
        )

        if query:
            handler_review_query()

        st.text_area(
            label="test env",
            placeholder="prompt here",
            key="user_prompt"
        )

        st.button(
            label="Generate",
            key="location_search",
            on_click=palm_handler)

        st.write("---")
        render_cta_link(
            url="https://github.com/CamiVasz/factored-datathon-2023-almond",
            label="Check the code",
            font_awesome_icon="fa-github",
        )


def render_palm_results():
    # TODO: temporal
    st.write("# ALMond recommendations")
    st.write(st.session_state.palm_output.result)

# Execution start here!

st.set_page_config(
    page_title="almond - demo",
    page_icon="🔍",
    layout="wide",
    initial_sidebar_state="expanded",
)

setup_palm()
reviews, products = load_data()
reviews_model, product_model, product_indexer = load_models()
topic_extractor, clusterer = load_uncached_models()

render_search()
if "palm_output" in st.session_state:
    render_palm_results()