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+web: python app.py

app.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# 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
+
+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')
+
+@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')
+
+if __name__ == '__main__':
+    app.run(debug=True)
+
+
+# In[ ]:
+
+
+
+

data.csv

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+feature1,feature2,target
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requirements.txt

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+Flask==2.0.3
+scikit-learn==1.0.2
+pandas==1.3.3
+numpy==1.21.2