Upload 4 files

DockerFile

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+# Use the official Python image
+FROM python:3.9-slim
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy the current directory contents into the container at /app
+COPY . /app
+
+# Install any needed dependencies specified in requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Make port 5000 available to the world outside this container
+EXPOSE 7820
+
+# Run app.py when the container launches
+CMD ["python", "app.py"]
+

app.py

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+from flask import Flask, jsonify, request, render_template
+import numpy as np 
+import joblib
+
+app  = Flask(__name__)
+
+try:
+    model = joblib.load('random_forest_model.joblib')
+    status = 'Loaded'
+except:
+    status= 'Not loaded'
+
+@app.route('/', methods = ['GET'])
+def health_check():
+    return render_template('index.html')
+
+@app.route('/predict', methods = ['POST'])
+def predict():
+    try:
+        
+        data = np.array([[float(request.form.get('input1', 0)),
+            float(request.form.get('input2', 0)),
+            float(request.form.get('input3', 0)),
+            float(request.form.get('input4', 0)),
+            float(request.form.get('input5', 0)),
+            float(request.form.get('input6', 0)),
+            float(request.form.get('input7', 0))]])
+
+        prediction = model.predict(data).tolist()
+        return render_template('output.html', prediction = prediction)
+    except Exception as e:
+        return jsonify({'data': e})
+
+
+if __name__ == '__main__':
+    app.run(debug=True, port = 7820)

random_forest_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:659ff6618b8604bafb159401806901f91d9ca0fc62311fa8b5785982a15ed76a
+size 6781057

requirements.txt

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+flask
+numpy
+joblib