Update app_backend.py

app_backend.py

@@ -1,87 +1,71 @@
 from flask import Flask, request, jsonify
 from flask_cors import CORS
-import os
 import openai
-import pandas as pd
+import os
 import json
-from functools import lru_cache
 
 app = Flask(__name__)
-CORS(app)  # Enable CORS for cross-origin requests
+CORS(app)
 
 # Configure DeepSeek API
 openai.api_key = os.getenv("DEEPSEEK_API_KEY")
 openai.api_base = "https://api.deepseek.com/v1"
 
-# Cache for expensive computations (e.g., dataset stats)
-@lru_cache(maxsize=128)
-def get_dataset_stats(df_json):
-    df = pd.read_json(df_json)
-    stats = {
-        "rows": df.shape[0],
-        "columns": df.shape[1],
-        "missing_values": df.isna().sum().sum(),
-        "column_types": {col: str(df[col].dtype) for col in df.columns},
-    }
-    return stats
-
-@app.route('/chat', methods=['POST'])
-def chat():
-    try:
-        data = request.json
-        user_input = data.get('message')
-        df_json = data.get('dataset', None)
-        problem_type = data.get('problem_type', None)
-        target = data.get('target', None)
-        best_model = data.get('best_model', None)
+# 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:
 
-        # Get dataset stats if available
-        context = ""
-        if df_json:
-            stats = get_dataset_stats(df_json)
-            context += f"Dataset Stats:\n- Rows: {stats['rows']}\n- Columns: {stats['columns']}\n- Missing Values: {stats['missing_values']}\n"
-            context += "Column Types:\n"
-            for col, dtype in stats['column_types'].items():
-                context += f"- {col}: {dtype}\n"
+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
 
-        if problem_type:
-            context += f"Problem Type: {problem_type}\n"
-        if target:
-            context += f"Target Column: {target}\n"
-        if best_model:
-            context += f"Best Model: {best_model}\n"
-
-        # Create enhanced prompt with context
-        system_prompt = (
-            "You are an AI assistant in Neural-Vision Enhanced, a data analysis and modeling app. "
-            "The app has three pages:\n"
-            "- **Data Upload**: Upload CSV files, view stats, or generate EDA reports.\n"
-            "- **Model Training**: Train classification, regression, or clustering models using PyCaret.\n"
-            "- **Validation & Exploration**: Evaluate and visualize trained models.\n"
-            f"Current context:\n{context}"
-        )
+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},
-                {"role": "user", "content": user_input}
-            ],
-            temperature=0.7,
+            messages=[{
+                "role": "system",
+                "content": SYSTEM_PROMPT.format(**context['current_state'])
+            }, {
+                "role": "user",
+                "content": prompt
+            }],
+            temperature=0.3,
             max_tokens=500
         )
-
-        return jsonify({
-            "response": response.choices[0].message.content,
-            "status": "success"
-        })
-
+        
+        return jsonify({"analysis": response.choices[0].message.content})
+    
     except Exception as e:
-        return jsonify({
-            "response": f"Error: {str(e)}",
-            "status": "error"
-        }), 500
+        return jsonify({"error": str(e)}), 500
 
 if __name__ == '__main__':
     app.run(host='0.0.0.0', port=5001)