Create model_deployment

model_deployment

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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()