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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 = """
<div style="background-color:tomato;padding:10px">
<h2 style="color:white;text-align:center;">eDNA Habitat Classification App </h2>
</div>
"""
"""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()
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