import numpy as np import pickle import pandas as pd #import streamlit as st import gradio as gr with open("DTHabitatClassifier.pkl","rb") as pickle_in: classifier=pickle.load(pickle_in) def welcome(): return "Welcome All" def habitat(species, processid, marker_code, gb_acs, nucraw , levenshtein_distance): """Let's load in the features as argument This is using docstrings for specifications. --- parameters: - name: species in: query type: number required: true - name: processid in: query type: number required: true - name: marker_code in: query type: number required: true - name: gb_acs in: query type: number required: true - name: nucraw in: query type: number required: true - name: levenshtein_distance in: query type: number required: true responses: 200: description: The output values """ prediction=classifier.predict([[species, processid, marker_code, gb_acs, nucraw, levenshtein_distance]]) print(prediction) return prediction def main(): st.title("eDNA Habitat Classification") html_temp = """

eDNA Habitat Classification App

""" """Proudly, Team SpaceM!""" st.markdown(html_temp,unsafe_allow_html=True) species = st.text_input("Species") processid = st.text_input("Processid") marker_code = st.text_input("Marker Code") gb_acs = st.text_input("GB_ACS") nucraw = st.text_input("Nucraw") levenshtein_distance = st.text_input("Levenshtein Distance") result="" if st.button("Classify"): result=habitat(species, processid, marker_code, gb_acs, nucraw, levenshtein_distance) st.success(f'The output is {result}') if st.button("About"): st.text("Many thanks") if __name__=='__main__': main()