Update main.py

main.py

@@ -1,59 +1,16 @@
 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()