Update app.py

app.py

@@ -1,42 +1,12 @@
-# -*- coding: utf-8 -*-
-"""mahabharata-chatbot.ipynb
-
-Automatically generated by Colaboratory.
-
-Original file is located at
-    https://colab.research.google.com/drive/14PZqWZU0cD12pm4gVs2BT4VpWlr1CHKx
-
-# Graph Powered NLP Workshop Python Notebook
-
-### Installing necessary drivers - Google Generative AI, Neo4j and Gradio.
-
-**Instruction:** Don't forget to restart the runtime after running the below cell
-"""
-
-### pip install google-generativeai
-### pip install -q neo4j-driver
-### pip install -q gradio
-
-"""### Import necessary libraries from installed packages/drivers - PaLM, base64, json, gradio and GraphDatabase"""
-
 import google.generativeai as genai
 import base64
 import json
+import re
 import gradio as gr
 from neo4j import GraphDatabase
 
-"""### Add PaLM2 API Key from Google MakerSuite
-
-**Instruction:** Replace "API_KEY" with the value of API Key copied from MakerSuite as mentioned in Step #6 of Part #2 of [Step-by-Step Guide](https://github.com/sidagarwal04/graph-powered-nlp-workshop/blob/main/step-by-step-guide.md#part-2-create-google-makersuite-account-train--test-prompt-in-google-makersuite-and-get-google-palm-2-api-key). Don't forget to add to include the key in double-quotes (" ")
-"""
-
 genai.configure(api_key = "AIzaSyA2eO7Mc1d_yTXBcJSH8w3XqdcVZD3Pl6s")
 
-"""### Include the generated prompt from MakerSuite.
-
-**Instruction:** Remove the initial part of installing drivers and configuring the API key as it has already been done in previous steps. Also, put the entire code as a function with output to be returned instead of printing it.
-"""
-
 def get_answer(input):
 
   # Set up the model
@@ -85,25 +55,10 @@ def get_answer(input):
 
   return response.text
 
-"""### Testing the output of get_answer() function with a test input"""
-
-get_answer("Who killed Ghatotakach?")
-
-"""### Initialize GraphDatabase driver
-
-**Instruction:** Replace URI, username and password before running the cell with the values from the txt file downloaded when creating Neo4j AuraDB instance in Step #4 of Part #1 of [Step By Step Guide](https://github.com/sidagarwal04/graph-powered-nlp-workshop/blob/main/step-by-step-guide.md#part-1-create-and-load-a-neo4j-instance) of this workshop.
-"""
-
 driver = GraphDatabase.driver("neo4j+s://9bebfeb5.databases.neo4j.io",
                               auth=("neo4j",
                                     "SQsRVyWfLuq8dl24WjcvlMhw7P20-TTT30Ywb-2miZM"))
 
-"""### Import required library for processing regular expressions"""
-
-import re
-
-"""### Function to clean the output query from get_answer() function by removing slash n's (\n) and substituting it with a space if it exists. Also, extract the string after RETURN expression in the output cypher query and utilize as a separate key to be used for printing the output in chatbot in later steps"""
-
 def extract_query_and_return_key(input_query_result):
     slash_n_pattern = r'[ \n]+'
     ret_pattern = r'RETURN\s+(.*)'
@@ -118,12 +73,6 @@ def extract_query_and_return_key(input_query_result):
             extracted_string = ""
     return cleaned_query, extracted_string
 
-"""### Testing the extract_query_and_return_key() function with a test input in natural language"""
-
-extract_query_and_return_key(get_answer("Who killed Ghatotakach?"))
-
-"""### format_names_with_ampersand() to return results as a comma-separated string of values with last value having '&' (ampersand/and) in case the output is a list of values."""
-
 def format_names_with_ampersand(names):
     if len(names) == 0:
         return ""
@@ -133,12 +82,6 @@ def format_names_with_ampersand(names):
         formatted_names = ", ".join(names[:-1]) + " & " + names[-1]
         return formatted_names
 
-"""### Testing format_names_with_ampersand() with sample input having list of values"""
-
-format_names_with_ampersand(["Karna"])
-
-"""### run_cypher_on_neo4j() to pass the output query from get_answer() to the Neo4j Database. If the length of output list is more than 1, format_name_with_ampersand() will further format the list and if the length of output list is equal to 1, output list is returned as it is. In case the output list is empty, an empty string is returned"""
-
 def run_cypher_on_neo4j(inp_query, inp_key):
   out_list = []
   with driver.session() as session:
@@ -153,26 +96,17 @@ def run_cypher_on_neo4j(inp_query, inp_key):
   else:
       return ""
 
-"""### Additional generate_and_exec_cypher() to parse and format the output of get_answer() and pass it to run_cypher_on_neo4j()"""
-
 def generate_and_exec_cypher(input_query):
     gen_query, gen_key = extract_query_and_return_key(get_answer(input_query))
     return run_cypher_on_neo4j(gen_query, gen_key)
 
 generate_and_exec_cypher("Who killed Ghatotakach?")
 
-"""### chatbot() to initiliaze the chatbot and pass the output of generate_and_exec_cypher to be displayed in the chatbot"""
-
 def chatbot(input, history=[]):
     output = str(generate_and_exec_cypher(input))
     history.append((input, output))
     return history, history
 
-"""### Initializing Gradio interface to run the chatbot.
-
-**Instruction:** Run the chatbot in the localhost url generated after running the cell and play aroung with input and output in natural language while fetching the results from the Neo4j Database using PaLM 2 API for converting input text into cypher code.
-"""
-
 gr.Interface(fn = chatbot,
              inputs = ["text",'state'],
              outputs = ["chatbot",'state']).launch(debug = True, share=True)