import gradio as gr
from data_library import embedded_form
import pandas as pd
from embed import sample_embedding

import faiss
embedded_form=embedded_form["train"]
embedded_form.add_faiss_index("embedding")



# gradio function

description="""<center>
<H1 style="background-color:powderblue;">ASK ME SCIENCE RELATED QUESTIONS(BIOLOGY,PHYSICS AND CHEMISTRY)</H1></center>"""


def input_text1(text):

  
  question_embedding =sample_embedding([text])
  question_embedding=question_embedding["embedding"]
  scores, samples = embedded_form.get_nearest_examples(
      "embedding", question_embedding, k=5
    )
  dataframe=pd.DataFrame(samples)
  dataframe["scores"]=scores
  dataframe=dataframe.sort_values("scores",ascending=False).reset_index(drop=True)

  return dataframe.loc[0,"support"]



def input_text2(text):

  
  question_embedding =sample_embedding([text])
  question_embedding=question_embedding["embedding"]
  scores, samples = embedded_form.get_nearest_examples(
      "embedding", question_embedding, k=5
    )
  dataframe=pd.DataFrame(samples)
  dataframe["scores"]=scores
  dataframe=dataframe.sort_values("scores",ascending=False).reset_index(drop=True)

  return dataframe.loc[1,"support"]


def input_text3(text):

  
  question_embedding =sample_embedding([text])
  question_embedding=question_embedding["embedding"]
  scores, samples = embedded_form.get_nearest_examples(
      "embedding", question_embedding, k=5
    )
  dataframe=pd.DataFrame(samples)
  dataframe["scores"]=scores
  dataframe=dataframe.sort_values("scores",ascending=False).reset_index(drop=True)

  return dataframe.loc[2,"support"]


def input_text4(text):

  
  question_embedding =sample_embedding([text])
  question_embedding=question_embedding["embedding"]
  scores, samples = embedded_form.get_nearest_examples(
      "embedding", question_embedding, k=5
    )
  dataframe=pd.DataFrame(samples)
  dataframe["scores"]=scores
  dataframe=dataframe.sort_values("scores",ascending=False).reset_index(drop=True)

  return dataframe.loc[3,"support"]

def input_text5(text):

  
  question_embedding =sample_embedding([text])
  question_embedding=question_embedding["embedding"]
  scores, samples = embedded_form.get_nearest_examples(
      "embedding", question_embedding, k=5
    )
  dataframe=pd.DataFrame(samples)
  dataframe["scores"]=scores
  dataframe=dataframe.sort_values("scores",ascending=False).reset_index(drop=True)

  return dataframe.loc[4,"support"]


answer1=gr.Interface(input_text1,inputs=gr.Textbox(label="Question"),outputs=gr.Textbox(label="Support 1"))

answer2=gr.Interface(input_text2,inputs=gr.Textbox(label="Question"),outputs=gr.Textbox(label="Support 2"))

answer3=gr.Interface(input_text3,inputs=gr.Textbox(label="Question"),outputs=gr.Textbox(label="Support 3"))


answer4=gr.Interface(input_text4,inputs=gr.Textbox(label="Question"),outputs=gr.Textbox(label="Support 4"))

answer5=gr.Interface(input_text5,inputs=gr.Textbox(label="Question"),outputs=gr.Textbox(label="Support 5"))

demo=gr.Parallel(answer1,answer2,answer3,answer4,answer5,description=description)


if __name__ == "__main__":
    demo.launch(debug=True)