| import numpy as np |
| import pickle |
| import pandas as pd |
| |
| 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() |
|
|
