app.py

# Import and class names setup
import gradio as gr
import os
import torch
import random
#import nltk_u
import pandas as pd
from sklearn.model_selection import train_test_split
import time

#from model import RNN_model
from timeit import default_timer as timer
from typing import Tuple, Dict

################################################################################
import argparse
import numpy as np
import pprint
import os
import copy
from str2bool import str2bool
from typing import Dict, Sequence
from sentence_transformers import SentenceTransformer

import torch
import json

import transformers
from modeling_phi import PhiForCausalLM
from tokenization_codegen import CodeGenTokenizer
################################################################################

parser = argparse.ArgumentParser()
#############################################################################################################################

parser.add_argument('--device_id', type=str, default="0")
parser.add_argument('--model', type=str, default="microsoft/phi-2", help="") ## /phi-1.5
parser.add_argument('--embedder', type=str, default="BAAI/bge-small-en-v1.5") ## /bge-small-en-v1.5  # bge-m3
parser.add_argument('--output_path', type=str, default="/home/henry/Desktop/HKU-DASC7606-A2/Outputs/ARC-Challenge-test", help="") ## -bge-m3
parser.add_argument('--start_index', type=int, default=0, help="")
parser.add_argument('--end_index', type=int, default=9999, help="")
parser.add_argument('--N', type=int, default=8, help="")
parser.add_argument('--max_len', type=int, default=1024, help="")
parser.add_argument('--prompt_type', type=str, default="v2.0", help="")
parser.add_argument('--top_k', type=str2bool, default=True, help="")
#############################################################################################################################
args = parser.parse_args()

if torch.cuda.is_available():
    device = "cuda"
    print(f'################################################################# device: {device}#################################################################')
else:
    device = "cpu"

def get_model(base_model: str = "bigcode/starcoder",):
    tokenizer = CodeGenTokenizer.from_pretrained(base_model)
    tokenizer.pad_token_id = tokenizer.eos_token_id
    tokenizer.pad_token = tokenizer.eos_token

    model = PhiForCausalLM.from_pretrained(
        base_model,
        device_map="auto",
    )
    model.config.pad_token_id = tokenizer.pad_token_id

    model.eval()

    return tokenizer, model

################################################################################

'''
# Import data
df= pd.read_csv('Symptom2Disease.csv')
df.drop('Unnamed: 0', axis= 1, inplace= True)

# Preprocess data
df.drop_duplicates(inplace= True)
train_data, test_data= train_test_split(df, test_size=0.15, random_state=42 )
'''

howto= """Welcome to the <b>Medical Chatbot</b>, powered by Gradio.
Currently, the chatbot can WELCOME YOU, PREDICT DISEASE based on your symptoms and SUGGEST POSSIBLE SOLUTIONS AND RECOMENDATIONS, and BID YOU FAREWELL.
<b>How to Start:</b> Simply type your messages in the textbox to chat with the Chatbot and press enter!<br><br>
The bot will respond based on the best possible answers to your messages.

"""
# Create the gradio demo
with gr.Blocks(css = """#col_container { margin-left: auto; margin-right: auto;} #chatbot {height: 520px; overflow: auto;}""") as demo:
  gr.HTML('<h1 align="center">Medical Chatbot: ARIN 7102')
  #gr.HTML('<h3 align="center">To know more about this project')
  with gr.Accordion("Follow these Steps to use the Gradio WebUI", open=True):
      gr.HTML(howto)
  chatbot = gr.Chatbot()
  msg = gr.Textbox()
  clear = gr.ClearButton([msg, chatbot])

  def respond(message, chat_history):
    # Create couple of if-else statements to capture/mimick peoples's Interaction
    embedder = SentenceTransformer(args.embedder, device=device)
    tokenizer, model = get_model(base_model=args.model)
    message_embeddings = embedder.encode(message)
    bot_message = model(message_embeddings)
    chat_history.append((message, bot_message))
    time.sleep(2)
    return "", chat_history
    
  msg.submit(respond, [msg, chatbot], [msg, chatbot])
  

# Launch the demo
demo.launch()