fun_advaitbert.py

import pandas as pd
import numpy as np
import tensorflow as tf
import tensorflow_hub as hub
import sys
import random
sys.path.append('models')
from official.nlp.data import classifier_data_lib
from official.nlp.bert import tokenization
from official.nlp import optimization
tf.get_logger().setLevel('ERROR')
from huggingface_hub import InferenceClient
import math
import gradio as gr
from datetime import datetime

num_warmup_steps=1
num_train_steps=1
init_lr = 3e-5
optimizer = optimization.create_optimizer(init_lr=init_lr,num_train_steps=num_train_steps,num_warmup_steps=num_warmup_steps,optimizer_type='adamw')

###    Load Model
checkpoint_filepath=r'./Checkpoint'
model = tf.keras.models.load_model(checkpoint_filepath, custom_objects={'KerasLayer':hub.KerasLayer , 'AdamWeightDecay': optimizer})

df_report = pd.read_csv('./CTH_Description.csv')
df_report['CTH Code'] = df_report['CTH Code'].astype(str).str.zfill(8)

df_report_DUTY = pd.read_csv('./CTH_WISE_DUTY_RATE.csv')
df_report_DUTY['CTH'] = df_report_DUTY['CTH'].astype(str).str.zfill(8)

df = pd.read_csv("./CTH_CODE_MAP.csv")
df['CTH'] = df['CTH'].astype(str).str.zfill(8)
df = df[['CTH', 'code']]

client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")



class_names=df[['CTH','code']].drop_duplicates(subset='CTH').sort_values(by='code',ignore_index=True)['CTH'].values.tolist()
label_list=list(range(0,len(class_names)))
max_seq_length = 200 # maximum length of (token) input sequences . it can be any number
train_batch_size = 32 # batch size ( 16 choosen to avoid Out-Of-Memory errors)

# Get BERT layer and tokenizer:
# More details here: https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/4
bert_layer = hub.KerasLayer("https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/4" , trainable = True)
vocab_file = bert_layer.resolved_object.vocab_file.asset_path.numpy()
do_lower_case = bert_layer.resolved_object.do_lower_case.numpy()
tokenizer = tokenization.FullTokenizer(vocab_file , do_lower_case)

# This provides a function to convert each row to input features and label ( as required by BERT)

max_seq_length = 200 # maximum length of (token) input sequences . it can be any number
def to_feature(text, label, label_list=label_list, max_seq_length=max_seq_length, tokenizer=tokenizer):
    example = classifier_data_lib.InputExample(guid = None,
                                             text_a = text.numpy(),
                                             text_b = None,
                                             label = label.numpy())
    feature = classifier_data_lib.convert_single_example(0 , example , label_list , max_seq_length , tokenizer)
  
    return (feature.input_ids , feature.input_mask , feature.segment_ids , feature.label_id)


def to_feature_map(text, label):
    input_ids , input_mask , segment_ids , label_id = tf.py_function(to_feature , inp = [text , label],
                                                                   Tout = [tf.int32 , tf.int32 , tf.int32 , tf.int32])
  
    input_ids.set_shape([max_seq_length])
    input_mask.set_shape([max_seq_length])
    segment_ids.set_shape([max_seq_length])
    label_id.set_shape([])

    x = {
      "input_word_ids": input_ids,
       "input_mask": input_mask,
       "input_type_ids": segment_ids
    }

    return(x,label_id)

def print3largest(arr, arr_size): 
    third = first = second = -sys.maxsize 
    for i in range(0, arr_size):
     
        if (arr[i] > first):        
            third = second
            second = first
            first = arr[i]        
        elif (arr[i] > second):       
            third = second
            second = arr[i]         
        elif (arr[i] > third):
            third = arr[i]
    pred_value_max_three=[first, second, third]  
    return pred_value_max_three

def count_special_character(string): 
    special_char= 0   
    for i in range(len(string)):  
        ch = string[i]
        if (string[i].isalpha()):  
            continue
        else: 
            special_char += 1

    if len(string)==special_char:
        return False
    else:
        return True

def format_prompt(message, history):
    prompt = "<s>"
    for user_prompt, bot_response in history:
        prompt += f"[INST] {user_prompt} [/INST]"
        prompt += f" {bot_response}</s> "
    prompt += f"[INST] {message} [/INST]"
    return prompt


additional_inputs=[
    gr.Textbox(
        label="System Prompt",
        max_lines=1,
        interactive=True,
    ),
    gr.Slider(
        label="Temperature",
        value=0.9,
        minimum=0.0,
        maximum=1.0,
        step=0.05,
        interactive=True,
        info="Higher values produce more diverse outputs",
    ),
    gr.Slider(
        label="Max new tokens",
        value=1024,
        minimum=0,
        maximum=4096,
        step=64,
        interactive=True,
        info="The maximum numbers of new tokens",
    ),
    gr.Slider(
        label="Top-p (nucleus sampling)",
        value=0.50,
        minimum=0.0,
        maximum=1,
        step=0.05,
        interactive=True,
        info="Higher values sample more low-probability tokens",
    ),
    gr.Slider(
        label="Repetition penalty",
        value=1.2,
        minimum=1.0,
        maximum=2.0,
        step=0.05,
        interactive=True,
        info="Penalize repeated tokens",
    )
]

def predict_CTH(txt):
    print('Desc: ',txt)
    now = datetime.now()
    print("Time =", now)
    if (txt!='') and len(txt)>=3 and (count_special_character(txt)):
        valid_data = tf.data.Dataset.from_tensor_slices(([txt] , [1])) # 1 refers to 'entertainment' and 2 refers to 'sport'
        valid_data = (valid_data.map(to_feature_map).batch(1))
        preds = model.predict(valid_data)
        predicted_values = tf.nn.softmax(preds)
        arr = predicted_values.numpy().tolist()[0]
        n = len(arr)
        pred_value_max_three=print3largest(arr, n)

        sum_all = pred_value_max_three[0] + pred_value_max_three[1] + pred_value_max_three[2]

        val_1 = pred_value_max_three[0]/sum_all
        val_2 = pred_value_max_three[1]/sum_all
        val_3 = pred_value_max_three[2]/sum_all

        if pred_value_max_three[0]<=0.000131:
            Var_CTH=[]
            Var_desc=[]
            Var_duty=[]
            pred_duty=''
            pred_desc=''
            pred_CTH=''

            return{'Not a adequate description':float(1.0)}
        else:
            Var_CTH=[]
            Var_desc=[]
            Var_duty=[]
            pred_duty=''
            pred_desc=''
            pred_CTH=''

            for i in pred_value_max_three:
                predicted_code=np.where(predicted_values.numpy()==i)[1][0]
                pred_CTH=df[df['code'] == predicted_code]['CTH'].iloc[0]  

                try:
                    pred_duty=df_report_DUTY[df_report_DUTY['CTH']==str(pred_CTH)]['DUTY_RATE'].iloc[0]
                except:
                    pred_duty=''
                    pass

                try:
                    pred_desc=df_report[df_report['CTH Code']==str(pred_CTH)]['Concat Description'].iloc[0]
                except:
                    pred_desc=''
                    pass

                Var_CTH.append(pred_CTH)
                Var_desc.append(pred_desc)
                Var_duty.append(pred_duty)

            P1 ='CTH: '+str(Var_CTH[0])+'   Duty Rate(%): '+ str(Var_duty[0]) 
            P2 ='CTH: '+str(Var_CTH[1])+'   Duty Rate(%): '+ str(Var_duty[1])
            P3 ='CTH: '+str(Var_CTH[2])+'   Duty Rate(%): '+ str(Var_duty[2]) 

            Q1='Desc: '+str(Var_desc[0])
            Q2='Desc: '+str(Var_desc[1])
            Q3='Desc: '+str(Var_desc[2])
  
            return {str(P1):float(val_1),str(Q1):float(val_1),
                    str(P2):float(val_2),str(Q2):float(val_2),
                    str(P3):float(val_3),str(Q3):float(val_3),}
    else:
        return{'Enter Correct Description':float(1.0)}

def llm_model_function(txt,history,chatbot=[], temperature=0.9, max_new_tokens=1024, top_p=0.95, repetition_penalty=1.0,):
    system_prompt=[]
    chatbot=[]
    if (txt!='') and len(txt)>=3 and (count_special_character(txt)):
        valid_data = tf.data.Dataset.from_tensor_slices(([txt] , [1])) # 1 refers to 'entertainment' and 2 refers to 'sport'
        valid_data = (valid_data.map(to_feature_map).batch(1))
        preds = model.predict(valid_data)
        predicted_values = tf.nn.softmax(preds)
        arr = predicted_values.numpy().tolist()[0]
        n = len(arr)
        pred_value_max_three=print3largest(arr, n)

        sum_all = pred_value_max_three[0] + pred_value_max_three[1] + pred_value_max_three[2]

        val_1 = pred_value_max_three[0]/sum_all
        val_2 = pred_value_max_three[1]/sum_all
        val_3 = pred_value_max_three[2]/sum_all

        if pred_value_max_three[0]<=0.000131:
            Var_CTH=[]
            Var_desc=[]
            Var_duty=[]
            pred_duty=''
            pred_desc=''
            pred_CTH=''

            #return{'Not a adequate description':float(1.0)}
            chatbot.append(('Not a adequate description', 'Not a adequate description'))
            return "", chatbot
        else:
            Var_CTH=[]
            Var_desc=[]
            Var_duty=[]
            pred_duty=''
            pred_desc=''
            pred_CTH=''

            for i in pred_value_max_three:
                predicted_code=np.where(predicted_values.numpy()==i)[1][0]
                pred_CTH=df[df['code'] == predicted_code]['CTH'].iloc[0]    

                try:
                    pred_duty=df_report_DUTY[df_report_DUTY['CTH']==str(pred_CTH)]['DUTY_RATE'].iloc[0]
                    pred_desc=df_report[df_report['CTH Code']==str(pred_CTH)]['Concat Description'].iloc[0]
                except:
                    pred_duty=''
                    pred_desc=''
                    pass

                Var_CTH.append(pred_CTH)
                Var_desc.append(pred_desc)
                Var_duty.append(pred_duty)

            P1 ='CTH: '+str(Var_CTH[0])+'   Duty Rate(%): '+ str(Var_duty[0]) 
            P2 ='CTH: '+str(Var_CTH[1])+'   Duty Rate(%): '+ str(Var_duty[1])
            P3 ='CTH: '+str(Var_CTH[2])+'   Duty Rate(%): '+ str(Var_duty[2]) 

            Q1='Desc: '+str(Var_desc[0])
            Q2='Desc: '+str(Var_desc[1])
            Q3='Desc: '+str(Var_desc[2])

            output_str_msg='1. '+str(P1)+' '+str(Q1)+' '+'2. '+str(P2)+' '+str(Q2)+' '+'3. '+str(P3)+' '+str(Q3)

            prompt=f'First Explain What is the product- {txt}. Which is the most appropriate 8 Digit classification code out of the three given below classes. Explain the reason step by step. if none of the three classification is applicable more precisely due to lack of any additional information, tell you need additional information and what is the that additional information. {output_str_msg} ?'

            temperature = float(temperature)
            if temperature < 1e-2:
                temperature = 1e-2
            top_p = float(top_p)

            generate_kwargs = dict(
                temperature=temperature,
                max_new_tokens=max_new_tokens,
                top_p=top_p,
                repetition_penalty=repetition_penalty,
                do_sample=True,
                seed=42,
            )

            formatted_prompt = format_prompt(f", {prompt}", history)
            stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
            output = ""
            for response in stream:
                output += response.token.text

            chatbot.append((txt, output))
            return "", chatbot 
    else:
        # warning_msg = f"Unexpected response"
        # raise gr.Error(warning_msg)
        chatbot.append(('Not a adequate description', 'Not a adequate description'))
        return "", chatbot

def product_explaination(txt,history,chatbot=[], temperature=0.9, max_new_tokens=1024, top_p=0.95, repetition_penalty=1.0,):
    print('Input Descrption is:',txt)
    chatbot=[]
    prompt=f'What is the product- {txt}?'
    print('prompt',prompt)
    temperature = float(temperature)
    if temperature < 1e-2:
        temperature = 1e-2
    top_p = float(top_p)

    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        do_sample=True,
        seed=42,
    )

    formatted_prompt = format_prompt(f", {prompt}", history)
    
    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
    output = ""

    for response in stream:
        output += response.token.text

    chatbot.append((txt, output))
    return "", chatbot