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| 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 | |
| title="""<center> | |
| <H3 style="background-color:powderblue;">SEARCH FOR SCIENCE RELATED(BIO,PHY AND CHEM)</H3></center>""" | |
| description="""<center><h4>This app is created to help give answers to high school science related questions</h4></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="Search"),outputs=gr.Textbox(label="Support 1")) | |
| answer2=gr.Interface(input_text2,inputs=gr.Textbox(label="Search"),outputs=gr.Textbox(label="Support 2")) | |
| answer3=gr.Interface(input_text3,inputs=gr.Textbox(label="Search"),outputs=gr.Textbox(label="Support 3")) | |
| answer4=gr.Interface(input_text4,inputs=gr.Textbox(label="Search"),outputs=gr.Textbox(label="Support 4")) | |
| answer5=gr.Interface(input_text5,inputs=gr.Textbox(label="Search"),outputs=gr.Textbox(label="Support 5")) | |
| demo=gr.Parallel(answer1,answer2,answer3,answer4,answer5,description=description,title=title) | |
| if __name__ == "__main__": | |
| demo.launch(debug=True) | |