Update app.py

app.py

@@ -221,73 +221,7 @@ def ai_assistant():
         response = handle_ai_query(prompt)
         
         st.session_state.chat_history.append({"role": "assistant", "content": response})
-        st.chat_message("assistant").write(response)
-
-# Flask Backend (app_backend.py)
-"""
-from flask import Flask, request, jsonify
-from flask_cors import CORS
-import openai
-import os
-
-app = Flask(__name__)
-CORS(app)
-
-openai.api_key = os.getenv("DEEPSEEK_API_KEY")
-openai.api_base = "https://api.deepseek.com/v1"
-
-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():
-    data = request.json
-    context = json.loads(data['context'])
-    
-    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'])}
-    '''
-    
-    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})
-
-if __name__ == '__main__':
-    app.run(port=5001)
-"""
+        st.chat_message("assistant").write(response
 
 # App Layout
 with st.sidebar: