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Dockerfile

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+FROM python:3.12
+
+## set the working directory to /code
+WORKDIR /code
+
+## Copy the current directory contents in the container at /code
+COPY ./requirements.txt /code/requirements.txt
+
+## Install the requirements.txt
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# Set up a new user named "user" 
+RUN useradd user
+# Switch to the "user" user
+USER user
+
+# Set home to the user's home directory
+
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+# Set the working directory to the user's home directory
+WORKDIR $HOME/app
+
+# Copy the current directory contents into the container at $HOME/app setting the owner to the user
+COPY --chown=user . $HOME/app
+
+## Start the FASTAPI App on port 7860
+CMD ["gunicorn", "--bind", "0.0.0.0:7860", "app:app"]

app.py

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+from flask import Flask, render_template, request, jsonify
+from src.pipelines.prediction_pipeline import CustomData, PredictPipeline
+from src.exception import CustomException
+
+app = Flask(__name__)
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+@app.route('/predict', methods=['POST'])
+def predict_route():
+    try:
+        # Get the form data from the request
+        data = request.form
+        
+        # Extract values safely from the incoming form data
+        custom_data = CustomData(
+            age=data.get("age"),
+            sex=data.get("sex"),
+            chest_pain_type=data.get("chestPainType"),
+            resting_bp=data.get("restingBP"),
+            cholesterol=data.get("cholesterol"),
+            fasting_bs=data.get("fastingBS"),
+            resting_ecg=data.get("restingECG"),
+            max_hr=data.get("maxHR"),
+            oldpeak=data.get("oldpeak"),
+            exercise_angina=data.get("exerciseAngina"),
+            st_slope=data.get("stSlope")
+        )
+
+        # Convert to DataFrame
+        input_df = custom_data.get_data_as_dataframe()
+
+        # Create a PredictPipeline instance and make a prediction
+        prediction_pipeline = PredictPipeline()
+        prediction = prediction_pipeline.predict(input_df)
+
+        # Condition to check if prediction equals 1
+        if prediction[0] == 1:
+            result_message = "You are at moderate risk of experiencing a heart attack"
+        else:
+            result_message = "There are no immediate risk factors for a heart attack"
+
+        # Pass the result message back to the template for display
+        return render_template('index.html', results=result_message)
+
+    except ValueError as ve:
+        return jsonify({"error": str(ve)}), 400
+    except CustomException as ce:
+        return jsonify({"error": str(ce)}), 500
+    except Exception as e:
+        return jsonify({"error": "An error occurred: " + str(e)}), 500
+
+if __name__ == '__main__':
+    app.run(debug=True)

requirements.txt

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+numpy
+pandas
+matplotlib
+seaborn
+scikit-learn
+ipykernel
+catboost
+xgboost
+dill
+flask
+flask_cors
+lime
+gunicorn
+#-e .