Update main.py

main.py

@@ -1,60 +1,30 @@
-import joblib
-from pydantic import BaseModel
-from fastapi import FastAPI
-import uvicorn
-import logging
+import numpy as np
+from flask import Flask, request, render_template
 import pickle
-import numpy
-
-
-# Save using pickle
-
-logging.basicConfig(level = logging.INFO)
-# 1. Load the trained model
-model = joblib.load('frauddetection.pkl')
-# 2. Define the input data schema using Pydantic BaseModel
-class InputData(BaseModel):
-    Year:int
-    Month:int
-    UseChip:int
-    Amount:int
-    MerchantName:int
-    MerchantCity:int
-    MerchantState:int
-    mcc:int
-    # Add the rest of the input features (feature4, feature5, ..., feature12)
-
-# 3. Create a FastAPI app
-app = FastAPI()
-@app.get('/')
-def welcome():
-    return {"Welcome": "This is the home page of the API"}
-
-
-# 4. Define the prediction route
-@app.post('/predict/')
-async def predict(data: InputData):
-    # Convert the input data to a dictionary
-    input_data = data.dict()
-
-    # Extract the input features from the dictionary
-    feature1 = input_data['Year']
-    feature2=input_data['Month']
-    feature3=input_data['UseChip']
-    feature4=input_data['Amount']
-    feature5=input_data['MerchantName']
-    feature6=input_data['MerchantCity']
-    feature7=input_data['MerchantState']
-    feature8=input_data['mcc']
-    # Extract the rest of the input features (feature4, feature5, ..., feature12)
-
-    # Perform the prediction using the loaded model
-    prediction = model.predict([[feature1, feature2, feature3,feature4,feature5,feature6,feature7,feature8]])  # Replace ... with the rest of the features
-    # Convert the prediction to a string (or any other format you prefer)
-    result = "Fraud" if prediction[0] == 1 else "Not a Fraud"
-    # result = prediction 
-    return {"prediction": result}
-# 4. Run the API with uvicorn
-#    Will run on http://127.0.0.1:8000
-if __name__ == '__main__':
-    uvicorn.run(app, port=8080)
+
+app = Flask(__name__)
+
+model = pickle.load(open('model.pkl', 'rb'))
+
+
+@app.route('/')
+def home():
+    return render_template('index.html')
+
+
+@app.route('/predict', methods=['POST', 'GET'])
+def predict():
+
+    int_features = [float(x) for x in request.form.values()]
+    final_features = [np.array(int_features)]
+    prediction = model.predict(final_features)
+
+    if prediction == 0:
+        return render_template('Low chances of transaction being fraud'),
+                               )
+    else:
+        return render_template('High chances of transaction being fraud')
+
+
+if __name__ == "__main__":
+    app.run(debug=True)