Delete app_backend.py

app_backend.py

@@ -1,69 +0,0 @@
-from flask import Flask, request, jsonify
-from flask_cors import CORS
-import openai
-import os
-import json
-
-app = Flask(__name__)
-CORS(app)
-
-# Configure DeepSeek API
-openai.api_key = os.getenv("DEEPSEEK_API_KEY")
-openai.api_base = "https://api.deepseek.com/v1"
-
-# System prompt for the AI assistant
-SYSTEM_PROMPT = '''
-You are Neural Analyst, an AI assistant for the Neural-Vision Enhanced analytics platform. 
-Your capabilities include:
-1. Explaining model metrics and evaluation visualizations
-2. Interpreting dataset statistics and EDA reports
-3. Guiding users through app functionality
-4. Providing data science insights
-5. Comparing different model performances
-Always consider:
-- Current dataset statistics: {dataset_stats}
-- Active problem type: {problem_type}
-- Model metrics: {metrics}
-- App state: {active_page}
-'''
-
-@app.route('/analyze', methods=['POST'])
-def analyze():
-    try:
-        data = request.json
-        context = json.loads(data['context'])
-        
-        # Construct the prompt for DeepSeek
-        prompt = f'''
-        User Query: {data['prompt']}
-        
-        Current Context:
-        - Active Page: {context['current_state']['active_page']}
-        - Problem Type: {context['current_state']['problem_type']}
-        - Target Variable: {context['current_state']['target']}
-        - Dataset Shape: {context['current_state']['dataset_stats'].get('rows', 0)} rows, 
-          {context['current_state']['dataset_stats'].get('columns', 0)} columns
-        - Model Metrics: {json.dumps(context['current_state']['model_metrics'])}
-        '''
-        
-        # Call DeepSeek API
-        response = openai.ChatCompletion.create(
-            model="deepseek-chat",
-            messages=[{
-                "role": "system",
-                "content": SYSTEM_PROMPT.format(**context['current_state'])
-            }, {
-                "role": "user",
-                "content": prompt
-            }],
-            temperature=0.3,
-            max_tokens=500
-        )
-        
-        return jsonify({"analysis": response.choices[0].message.content})
-    
-    except Exception as e:
-        return jsonify({"error": str(e)}), 500
-
-if __name__ == '__main__':
-    app.run(host='0.0.0.0', port=5001)