Update app.py

app.py

@@ -1,7 +1,6 @@
-import streamlit as st
 import pandas as pd
 import numpy as np
-import sklearn
+from flask import Flask, request, jsonify
 from sklearn.model_selection import train_test_split
 from sklearn.feature_extraction.text import CountVectorizer
 from sklearn.neighbors import KNeighborsClassifier
@@ -10,16 +9,13 @@ from sklearn.tree import DecisionTreeClassifier
 from sklearn.linear_model import LogisticRegression
 from sklearn.svm import SVC
 from sklearn.metrics import accuracy_score
-from fastapi import FastAPI
-from fastapi.responses import JSONResponse
-import threading
+
+# Initialize Flask app
+app = Flask(__name__)
 
 # Read dataset
 df = pd.read_csv(r"spam.csv")
 
-# Initialize Streamlit app
-st.title("Identifying Spam and Ham Emails")
-
 # Define feature and target variables
 x = df["Message"]
 y = df["Category"]
@@ -40,54 +36,38 @@ models = {
     "SVM": SVC()
 }
 
-# Model selection
-model_choice = st.selectbox("Choose a Classification Algorithm", list(models.keys()))
-
-# Train the selected model
+# Choose and train a model
+model_choice = "Naive Bayes"  # Default model
 obj = models[model_choice]
 obj.fit(x_train, y_train)
 y_pred = obj.predict(x_test)
 accuracy = accuracy_score(y_test, y_pred)
 
-# Display accuracy
-if st.button("Show Accuracy"):
-    st.write(f"Accuracy of {model_choice}: {accuracy:.4f}")
+# Print accuracy for initial check
+print(f"Accuracy of {model_choice}: {accuracy:.4f}")
 
-# Email input and prediction function
-email_input = st.text_input("Enter an Email for Prediction")
-
-def predict_email(email):
-    data = bow.transform([email]).toarray() 
-    prediction = obj.predict(data)[0]
-    st.write(f"Prediction: {prediction}")
-
-if st.button("Predict Email"):
-    if email_input:
-        predict_email(email_input)
-    else:
-        st.write(":red[Please enter an email to classify]")
-
-# FastAPI app to handle GET requests
-app = FastAPI()
-
-@app.get("/predict")
-def predict_spam(email: str):
+@app.route('/predict', methods=['GET'])
+def predict_spam():
     """
     This endpoint predicts whether the email is Spam or Ham.
     Query parameter: email (str) - The email text to be classified.
     """
-    data = bow.transform([email]).toarray()
-    prediction = obj.predict(data)[0]
-    return JSONResponse(content={"prediction": prediction})
-
-# Running FastAPI in a separate thread to work alongside Streamlit
-def run_api():
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=8000)
+    email = request.args.get('email')
+    
+    if email:
+        data = bow.transform([email]).toarray()  # Transform email using the Bag of Words vectorizer
+        prediction = obj.predict(data)[0]  # Get the prediction (Spam or Ham)
+        return jsonify({"prediction": prediction})  # Return prediction as JSON
+    else:
+        return jsonify({"error": "Please provide an 'email' query parameter."}), 400
 
-# Start FastAPI in a separate thread
-api_thread = threading.Thread(target=run_api, daemon=True)
-api_thread.start()
+@app.route('/accuracy', methods=['GET'])
+def get_accuracy():
+    """ 
+    Endpoint to check the accuracy of the selected model on the test data.
+    """
+    return jsonify({"accuracy": accuracy})
 
-# You can also check API response using the link below:
-# http://localhost:8000/predict/?email=Your_email_text_here
+# Run Flask app
+if __name__ == '__main__':
+    app.run(host='127.0.0.1', port=5001, debug=True)