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Dockerfile

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+# Menggunakan base image Python 3.9
+FROM python:3.9
+
+# Mengatur direktori kerja ke /code
+WORKDIR /code
+
+# Menyalin requirements.txt ke /code
+COPY ./requirements.txt /code/requirements.txt
+
+# Menginstal dependensi dari requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+# Menyalin seluruh konten proyek Anda ke /code
+COPY . /code
+
+# CMD untuk menjalankan Gunicorn
+CMD ["gunicorn", "main:app", "-b", "0.0.0.0:7860"]

main.py

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+from flask import Flask, request
+import joblib
+import pandas as pd
+from flask_cors import CORS
+
+
+app = Flask(__name__)
+app.static_folder = 'static'
+app.static_url_path = '/static'
+
+app.secret_key = "roadsense-abhi-2023"
+
+CORS(app)
+
+    
+# Load the model
+model = joblib.load('accident_prediction_model_Final.m5')
+
+# Load the encoder
+encoder = joblib.load('encoder.pkl')
+
+@app.route('/', methods=['GET'])
+def main():
+    return {'message': 'Hello, World'}
+
+
+@app.route('/prediction', methods=['POST'])
+def prediction():
+    data = request.get_json()
+    
+    num_input = {'Latitude': data['Latitude'], 'Longitude': data['Longitude'], 'person_count': data['personCount']}
+    cat_input = {'weather_conditions': data['selectedWeatherCondition'], 'impact_type': data['selectedImpactType'],
+                 'traffic_voilations': data['selectedTrafficViolationType'],
+                 'road_features': data['selectedRoadFeaturesType'],
+                 'junction_types': data['selectedRoadJunctionType'],
+                 'traffic_controls': data['selectedTrafficControl'], 'time_day': data['selectedTimeOfDay'],
+                 'age_group': data['selectedAge'], 'safety_features': data['selectedSafetyFeature'],
+                 'injury': data['selectedInjuryType']}
+
+    input_df = pd.DataFrame([cat_input])
+
+    encoded_input = encoder['encoder'].transform(input_df)
+    encoded_input_df = pd.DataFrame(encoded_input, columns=encoder['encoded_columns'])
+
+    num_df = pd.DataFrame([num_input])
+    input_with_coords = pd.concat([num_df, encoded_input_df], axis=1)
+
+    # Make a prediction using the trained model
+    prediction = model.predict(input_with_coords)
+
+    temp = False
+    if prediction[0] == 1:
+        temp = True
+    
+    return {'prediction': temp}
+
+
+if __name__ == '__main__':
+    app.run()

requirements.txt

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+flask
+flask-cors
+gunicorn
+Jinja2
+pandas
+scikit-learn