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app.py

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+import os
+import uuid
+import joblib
+import json
+
+import gradio as gr
+import pandas as pd
+
+from huggingface_hub import CommitScheduler
+from pathlib import Path
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from warnings import filterwarnings
+filterwarnings('ignore')
+
+
+log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
+log_folder = log_file.parent
+
+scheduler = CommitScheduler(
+    repo_id="machine-failure-logs",
+    repo_type="dataset",
+    folder_path=log_folder,
+    path_in_repo="data",
+    every=2
+)
+
+health_status_predictor = joblib.load('model.joblib')
+
+Latitude = gr.Number(label='Latitude')
+Longitude = gr.Number(label='Longitude')
+DBH = gr.Number(label='DBH')
+Tree_Height = gr.Number(label='Tree_Height')
+Crown_Width_North_South = gr.Number(label='Crown_Width_North_South')
+Crown_Width_East_West = gr.Number(label='Crown_Width_East_West')
+Slope = gr.Number(label='Slope')
+Elevation = gr.Number(label='Elevation')
+Temperature = gr.Number(label='Temperature')
+Humidity = gr.Number(label='Humidity')
+Soil_TN = gr.Number(label='Soil_TN')
+Soil_TP = gr.Number(label='Soil_TP')
+Soil_AP = gr.Number(label='Soil_AP')
+Soil_AN = gr.Number(label='Soil_AN')
+Menhinick_Index = gr.Number(label='Menhinick_Index')
+Gleason_Index = gr.Number(label='Gleason_Index')
+Fire_Risk_Index = gr.Number(label='Fire_Risk_Index')
+
+model_output = gr.Label(label="Health Status")
+
+def predict_health_status(Latitude, Longitude, DBH, Tree_Height, Crown_Width_North_South, Crown_Width_East_West, Slope, Elevation, Temperature, Humidity, Soil_TN, Soil_TP, Soil_AP, Soil_AN, Menhinick_Index, Gleason_Index, Fire_Risk_Index):
+    sample = {
+        'Latitude': Latitude,
+        'Longitude': Longitude,
+        'DBH': DBH,
+        'Tree_Height': Tree_Height,
+        'Crown_Width_North_South': Crown_Width_North_South,
+        'Crown_Width_East_West': Crown_Width_East_West,
+        'Slope': Slope,
+        'Elevation': Elevation,
+        'Temperature': Temperature,
+        'Humidity': Humidity,
+        'Soil_TN': Soil_TN,
+        'Soil_TP': Soil_TP,
+        'Soil_AP': Soil_AP,
+        'Soil_AN': Soil_AN,
+        'Menhinick_Index': Menhinick_Index,
+        'Gleason_Index': Gleason_Index.
+        'Fire_Risk_Index': Fire_Risk_Index
+        
+    }
+    data_point = pd.DataFrame([sample])
+    prediction = health_status_predictor.predict(data_point).tolist()
+
+    with scheduler.lock:
+        with log_file.open("a") as f:
+            f.write(json.dumps(
+                {
+                    'Latitude': Latitude,
+                    'Longitude': Longitude,
+                    'DBH': DBH,
+                    'Tree_Height': Tree_Height,
+                    'Crown_Width_North_South': Crown_Width_North_South,
+                    'Crown_Width_East_West': Crown_Width_East_West,
+                    'Slope': Slope,
+                    'Elevation': Elevation,
+                    'Temperature': Temperature,
+                    'Humidity': Humidity,
+                    'Soil_TN': Soil_TN,
+                    'Soil_TP': Soil_TP,
+                    'Soil_AP': Soil_AP,
+                    'Soil_AN': Soil_AN,
+                    'Menhinick_Index': Menhinick_Index,
+                    'Gleason_Index': Gleason_Index,
+                    'Fire_Risk_Index': Fire_Risk_Index,
+                    'prediction': prediction[0]
+                }
+            ))
+            f.write("\n")
+            
+    return prediction[0]
+
+demo = gr.Interface(
+    fn=predict_health_status,
+    inputs=[Latitude, Longitude, DBH, Tree_Height, Crown_Width_North_South, Crown_Width_East_West, Slope, Elevation, Temperature, Humidity, Soil_TN, Soil_TP, Soil_AP, Soil_AN, Menhinick_Index, Gleason_Index, Fire_Risk_Index],
+    outputs=model_output,
+    title="Health Status Predictor",
+    description="This API allows you to predict the health status of a Tree",
+    allow_flagging="auto",
+    concurrency_limit=8
+)
+
+demo.queue()
+demo.launch(share=False)

model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:cd88a95d898fdfeaa52814163167680b1069d479b399777f32c33fb800a9e6c2
+size 4550

requirements.txt

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+scikit-learn==1.2.2