Update app.py

app.py

@@ -4,74 +4,17 @@
 # In[1]:
 
 
-import pandas as pd
-import numpy as np
-
-# Örnek veri seti oluşturma
-np.random.seed(42)  # Tekrarlanabilir sonuçlar için
-
-# Özellikler ve etiketler oluşturma
-data = pd.DataFrame({
-    'feature1': np.random.rand(100),
-    'feature2': np.random.rand(100),
-    'target': np.random.randint(0, 2, 100)
-})
-
-# Veri setini kaydetme
-data.to_csv('data.csv', index=False)
-print("Örnek veri seti 'data.csv' dosyasına kaydedildi.")
-
-
-# In[2]:
-
-
-from sklearn.ensemble import RandomForestClassifier
-import pickle
-import numpy as np
-import pandas as pd
-
-# Örnek veri seti ve model eğitimi
-data = pd.read_csv('data.csv')
-X = data.drop('target', axis=1)
-y = data['target']
-
-model = RandomForestClassifier()
-model.fit(X, y)
-
-# Modeli kaydedin
-with open('static/model/ai_model.pkl', 'wb') as file:
-    pickle.dump(model, file)
-
-
-# In[3]:
-
-
-from flask import Flask, render_template, request, jsonify
-import pickle
-import numpy as np
-import pandas as pd
+from flask import Flask, send_from_directory
 
 app = Flask(__name__)
 
-# Yapay zeka modelini yükleyin
-with open('static/model/ai_model.pkl', 'rb') as file:
-    ai_model = pickle.load(file)
-
 @app.route('/')
 def index():
-    return render_template('index.html')
+    return send_from_directory('.', 'index.html')
 
-@app.route('/simulate', methods=['POST'])
-def simulate():
-    user_input = request.json
-    # Yapay zeka modelini kullanarak tahmin yapın
-    features = np.array([user_input['data']]).reshape(1, -1)
-    prediction = ai_model.predict(features)[0]
-    return jsonify({'prediction': prediction})
-
-@app.route('/result')
-def result():
-    return render_template('result.html')
+@app.route('/<path:path>')
+def static_files(path):
+    return send_from_directory('.', path)
 
 if __name__ == '__main__':
     app.run(debug=True)
@@ -81,4 +24,3 @@ if __name__ == '__main__':
 
 
 
-