app.py

import gradio as gr
from transformers import pipeline
import numpy as np
import pandas as pd
from sentence_transformers import SentenceTransformer, util
import nltk
from nltk import sent_tokenize
nltk.download("punkt")

# Loading in dataframes
krishnamurti_df = pd.read_json("krishnamurti_df.json")
stoic_df = pd.read_json("stoic_df.json")

# Loading in sentence_similarity model
sentence_similarity_model = "all-mpnet-base-v2"
model = SentenceTransformer(sentence_similarity_model) 

# Loading in text-generation models
stoic_generator = pipeline("text-generation", model="eliwill/stoic-generator-10e")
krishnamurti_generator =  pipeline("text-generation", model="distilgpt2")

# Creating philosopher dictionary
philosopher_dictionary = {
    "stoic": {
        "generator": stoic_generator,
        "dataframe": stoic_df
        },

    "krishnamurti": {
        "generator": krishnamurti_generator,
        "dataframe": krishnamurti_df
        }
}

############### DEFINING FUNCTIONS ###########################

def ask_philosopher(philosopher, question):
  """ Return first 5 sentences generated by question for the given philosopher model """

  generator = philosopher_dictionary[philosopher]['generator']
  answer = generator(question, min_length=100, max_length=120)[0]['generated_text'] # generate about 50 word tokens
  answer = " ".join(sent_tokenize(answer)[:6]) # Get the first five sentences
  return answer

def get_similar_quotes(philosopher, question):
  """ Return top 5 most similar quotes to the question from a philosopher's dataframe """
  df = philosopher_dictionary[philosopher]['dataframe']
  question_embedding = model.encode(question)
  sims = [util.dot_score(question_embedding, quote_embedding) for quote_embedding in df['Embedding']]
  ind = np.argpartition(sims, -5)[-5:]
  similar_sentences = [df['quote'][i] for i in ind]
  top5quotes = pd.DataFrame(data = similar_sentences, columns=["Quotes"], index=range(1,6))
  top5quotes['Quotes'] = top5quotes['Quotes'].str[:-1].str[:250] + "..."
  return top5quotes

def main(question, philosopher):
  out_image = "marcus-aurelius.jpg"
  return ask_philosopher(philosopher, question), get_similar_quotes(philosopher, question), out_image
  
with gr.Blocks(css=".gradio-container {background-image: url('file=mountains_resized.jpg')} # title {color: #F0FFFF}") as demo:
    gr.Markdown("""
    # Ask a Philsopher
    """, 
    elem_id="title"
    )
    
    with gr.Row():
        with gr.Column():
            inp1 = gr.Textbox(placeholder="Place your question here...", label="Ask a question", elem_id="title")
            inp2 = gr.Dropdown(choices=["stoic", "krishnamurti"], value="stoic", label="Choose a philosopher")
          
        out1 = gr.Textbox(
                    lines=3, 
                    max_lines=10,
                    label="Answer"
                )
          
    with gr.Row():
        out_image = gr.Image(label="Picture", image_mode="L")
        out2 = gr.DataFrame(
                    headers=["Quotes"],
                    max_rows=5,
                    interactive=False,
                    wrap=True
                    value=[["When you arise in the morning, think of what a precious privilege it is to be alive – to breathe, to think, to enjoy, to love.",
                             "Each day provides its own gifts.",
                             "Only time can heal what reason cannot.",
                             "He who is brave is free.",
                             "First learn the meaning of what you say, and then speak."]]
                        
    btn = gr.Button("Run")
    btn.click(fn=main, inputs=[inp1,inp2], outputs=[out1,out2,out_image])

demo.launch()