import tensorflow as tf
from transformers import pipeline
import gradio as gr
# importing necessary libraries
from transformers import AutoTokenizer, TFAutoModelForQuestionAnswering


tokenizer = AutoTokenizer.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")
model = TFAutoModelForQuestionAnswering.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad",return_dict=False)

nlp = pipeline("question-answering", model=model, tokenizer=tokenizer)

context = "My name is Hema Raikhola, i am a data scientist and machine learning engineer.मेरो नाम हेमा हो। म नेपालीमा बोल्न जान्दछु। मशीन लर्निंग आर्टिफिशियल इंटेलिजेंस की एक शाखा है। यह एक लोकप्रिय प्रमुख है।"
question = "what is my profession?"
result = nlp(question = question, context=context)
print(f"QUESTION: {question}")
print(f"ANSWER: {result['answer']}") 

# creating the function
def func(context, question):
  result = nlp(question = question, context=context)
  return result['answer']


example_1 ="Linear regression is one of the easiest and most popular Machine Learning algorithms. It is a statistical method that is used for predictive analysis. Linear regression makes predictions for continuous/real or numeric variables such as sales, salary, age, product price, etc."
qst_1 =  "What is Linear Regression?"

example_2 = "(2) Natural Language Processing (NLP) allows machines to break down and interpret human language. It's at the core of tools we use every day – from translation software, chatbots, spam filters, and search engines, to grammar correction software, voice assistants, and social media monitoring tools."
qst_2 =  "What is NLP used for?"

example_3 = "(3) मशीन लर्निंग आर्टिफिशियल इंटेलिजेंस की एक शाखा है। यह एक लोकप्रिय प्रमुख है।"
qst_3 = "मशीन लर्निंग किसकी एक शाखा है?"

example_4 = "(4) माउन्ट एवरेस्ट विश्वको सबैभन्दा अग्लो हिमाल हो, जसको शिखर समुन्द्री सतहबाट 8,848 मिटर(29,029 फिट) उचाइमा छ। यो हिमालयमा नेपाल र तिब्बत (चीन) बीचको सीमामा रहेको महालंगुर पर्वतमालामा अवस्थित छ।"
qst_4 ="सगरमाथाको उचाई कति छ ?"

# creating the interface
app = gr.Interface(fn=func,
                   inputs = ['textbox', 'text'],
                   outputs = gr.Textbox( lines=10), 
                   title = 'Question Answering bot',
                   description = 'Input context and question, then get answers!',
                   examples = [[example_1, qst_1],
                               [example_2, qst_2],
                               [example_3, qst_3],
                               [example_4, qst_4]],
                   
                   allow_flagging="manual",
              
                   ).queue()
# launching the app
app.launch(auth = ('user','saitmhpsk'), auth_message = "Check your Login details sent to your email")