model load cbm

app.py

@@ -23,7 +23,7 @@ def extract_features_in_parallel(urls):
 # Load the CatBoost model for inference
 def predict_with_catboost(features_df, model_path):
     model = CatBoostClassifier()
-    model.load_model(model_path)
+    model.load_model(model_path, format='cbm')  # Specify the format as 'cbm'
     predictions = model.predict(features_df)
     return predictions
 
@@ -67,72 +67,3 @@ async def index():
 
 if __name__ == "__main__":
     app.run(debug=False,host="0.0.0.0",port=7860)
-
-'''
-import asyncio
-import pandas as pd
-from concurrent.futures import ThreadPoolExecutor
-from flask import Flask, request, render_template
-from catboost import CatBoostClassifier
-from url_process import extract_url_features, predict_urls  # Import necessary functions
-
-# Flask App Setup
-app = Flask(__name__)
-
-# Batch Processing: Ensures URLs are processed in manageable chunks
-def process_urls_in_batches(urls, batch_size=10):
-    for i in range(0, len(urls), batch_size):
-        yield urls[i:i + batch_size]
-
-# Async function for non-blocking DNS lookups and HTTP requests
-async def async_extract_features(url):
-    features = await asyncio.to_thread(extract_url_features, url)
-    return features
-
-# ThreadPoolExecutor for CPU-bound tasks like feature extraction
-def extract_features_in_parallel(urls):
-    with ThreadPoolExecutor(max_workers=5) as executor:
-        return list(executor.map(extract_url_features, urls))
-
-# Load the CatBoost model for inference
-def predict_with_catboost(features_df, model_path):
-    model = CatBoostClassifier()
-    model.load_model(model_path)
-    predictions = model.predict(features_df)
-    return predictions
-
-@app.route("/", methods=["GET", "POST"])
-async def index():
-    result = None
-    url_features = None
-
-    if request.method == "POST":
-        # Get the URL input from the form
-        url = request.form["url"]
-
-        try:
-            # Asynchronously process the URL features
-            features = await async_extract_features(url)
-
-            # Convert the features to a DataFrame for further processing
-            features_df = pd.DataFrame([features])
-
-            # Perform prediction using the CatBoost model
-            model_path = "F:\\pyro guard\\model\\catboost_model.bin"  # Specify your CatBoost model path
-            predictions = predict_with_catboost(features_df, model_path)
-
-            # Determine if the URL is malicious or legitimate
-            if predictions[0] == 1:
-                result = "Malicious"
-            else:
-                result = "Legitimate"
-
-        except Exception as e:
-            result = f"Error processing URL: {str(e)}"
-
-    return render_template("index.html", result=result, url_features=url_features)
-
-if __name__ == "__main__":
-    app.run(debug=True)
-
-'''

verify_model.py

@@ -0,0 +1,7 @@
+from catboost import CatBoostClassifier
+import os
+
+model_path = os.path.join(os.getcwd(), "catboost_model.bin")
+model = CatBoostClassifier()
+model.load_model(model_path, format='cbm')
+print("Model loaded successfully")