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Dockerfile

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+# pull python base image
+FROM python:3.10
+
+COPY requirements.txt requirements.txt
+
+# update pip
+RUN pip install --upgrade pip
+
+# install dependencies
+RUN pip install -r requirements.txt
+
+RUN useradd -m -u 1000 myuser
+
+USER myuser
+
+# copy application files
+COPY --chown=myuser . .
+
+# expose port for application
+EXPOSE 8001
+
+# start fastapi application
+CMD ["python", "app.py"]

app.py

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+import gradio as gr
+import pickle
+import numpy as np
+from fastapi import FastAPI,request,Response
+from sklearn.metrics import accuracy_score, f1_score
+import prometheus_client as prom
+# from transformers import pipeline
+
+
+
+#model
+save_file_name="xgboost-model.pkl"
+loaded_model = pickle.load(open(save_file_name, 'rb'))
+
+app=FastAPI()
+
+username="ashwml"
+repo_name="prometheus_model"
+model=username+'/'+repo_name
+test_data=pd.read_csv("test.csv")
+
+
+f1_metric = prom.Gauge('death_f1_score', 'F1 score for test samples')
+
+# Function for updating metrics
+def update_metrics():
+    test = test_data.sample(100)
+    X = test.iloc[:, :-1].values
+    y = test['DEATH_EVENT'].values
+    
+    # test_text = test['Text'].values
+    test_pred = loaded_model.predict(X)
+    #pred_labels = [int(pred['label'].split("_")[1]) for pred in test_pred]
+
+    f1 = f1_score( y , test_pred).round(3)
+
+    #f1 = f1_score(test['labels'], pred_labels).round(3)
+
+    f1_metric.set(f1)
+
+
+
+def predict_death_event(age,	anaemia,	creatinine_phosphokinase	,diabetes	,ejection_fraction,	high_blood_pressure	,platelets	,serum_creatinine,	serum_sodium,	sex	,smoking	,time):
+   input=[[age,	anaemia,	creatinine_phosphokinase	,diabetes	,ejection_fraction,	high_blood_pressure	,platelets	,serum_creatinine,	serum_sodium,	sex	,smoking	,time]]
+   result=loaded_model.predict(input)
+
+   if result[0]==1:
+      return 'Positive'
+   else:
+      return 'Negative'
+   return result
+
+
+@app.get("/metrics")
+async def get_metrics():
+    update_metrics()
+    return Response(media_type="text/plain", content= prom.generate_latest())
+
+
+
+title = "Patient Survival Prediction"
+description = "Predict survival of patient with heart failure, given their clinical record"
+
+out_response = gr.components.Textbox(type="text", label='Death_event')
+
+iface = gr.Interface(fn=predict_death_event,      
+        inputs=[
+        gr.Slider(18, 100, value=20, label="Age"),
+        gr.Slider(0, 1, value=1, label="anaemia"),
+        gr.Slider(100, 2000, value=20,  label="creatinine_phosphokinase"),
+        gr.Slider(0, 1, value=1, label="diabetes"),
+        gr.Slider(18, 100, value=20, label="ejection_fraction"),
+        gr.Slider(0, 1, value=1, label="high_blood_pressure"),
+        gr.Slider(18, 400000, value=20, label="platelets"),
+        gr.Slider(1, 10, value=20, label="serum_creatinine"),
+        gr.Slider(100, 200, value=20, label="serum_sodium"),
+        gr.Slider(0, 1, value=1, label="sex"),
+        gr.Slider(0, 1, value=1, label="smoking"),
+        gr.Slider(1, 10, value=20, label="time"),
+        ],
+    outputs = [out_response])
+
+
+app = gradio.mount_gradio_app(app, iface, path="/")
+iface.launch(server_name = "0.0.0.0", server_port = 8001)
+
+
+if __name__ == "__main__":
+    # Use this for debugging purposes only
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8001)

requirements.txt

@@ -0,0 +1,13 @@
+gradio
+pickle5
+numpy
+xgboost
+
+
+uvicorn
+fastapi
+python-multipart
+pydantic
+
+scikit-learn
+prometheus-client

test.csv

@@ -0,0 +1,99 @@
+age,anaemia,creatinine_phosphokinase,diabetes,ejection_fraction,high_blood_pressure,platelets,serum_creatinine,serum_sodium,sex,smoking,time,DEATH_EVENT
+55.0,0,7861,0,38,0,263358.03,1.1,136,1,0,6,1
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+82.0,1,379,0,50,0,47000.0,1.3,136,1,0,13,1
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+60.0,0,68,0,20,0,119000.0,2.9,127,1,1,64,1
+85.0,0,5882,0,35,0,243000.0,1.0,132,1,1,72,1
+69.0,0,582,0,20,0,266000.0,1.2,134,1,1,73,1
+60.0,1,47,0,20,0,204000.0,0.7,139,1,1,73,1
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+55.0,0,748,0,45,0,263000.0,1.3,137,1,0,88,0
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+60.0,1,582,0,30,1,127000.0,0.9,145,0,0,95,0
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