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()