Create backend/app.py

backend/app.py

@@ -0,0 +1,85 @@
+from flask import Flask, request, jsonify
+import mlflow
+import pandas as pd
+from pycaret.classification import *
+from pycaret.regression import *
+from pycaret.clustering import *
+import json
+import os
+import groq
+
+app = Flask(__name__)
+
+# Initialize GROQ client
+groq_client = groq.Client(api_key=os.getenv("GROQ_API_KEY"))
+
+# MLflow Configuration
+mlflow.set_tracking_uri("http://127.0.0.1:5000")
+mlflow.set_experiment("Neural-Vision Enhanced")
+
+@app.route('/analyze', methods=['POST'])
+def analyze():
+    try:
+        data = request.json
+        prompt = data.get('prompt')
+        context = json.loads(data.get('context'))
+        metrics = data.get('metrics', {})
+        
+        # Create GROQ prompt with context
+        system_prompt = f"""
+        You are a data science assistant analyzing model metrics and data.
+        Context: {json.dumps(context, indent=2)}
+        Metrics: {json.dumps(metrics, indent=2)}
+        """
+        
+        # Get GROQ response
+        response = groq_client.chat.completions.create(
+            messages=[
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": prompt}
+            ],
+            model="mixtral-8x7b-32768",
+            temperature=0.7,
+            max_tokens=1024
+        )
+        
+        return jsonify({
+            "analysis": response.choices[0].message.content
+        })
+    
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+@app.route('/train', methods=['POST'])
+def train_model():
+    try:
+        data = request.json
+        df = pd.DataFrame(data['data'])
+        problem_type = data['problem_type']
+        target = data.get('target')
+        
+        if problem_type == "Classification":
+            setup(df, target=target, session_id=42)
+        elif problem_type == "Regression":
+            setup(df, target=target, session_id=42)
+        else:
+            setup(df, session_id=42)
+        
+        best_model = compare_models()
+        metrics = pull().to_dict()
+        
+        # Log to MLflow
+        with mlflow.start_run():
+            mlflow.log_metrics(metrics)
+            mlflow.sklearn.log_model(best_model, "model")
+        
+        return jsonify({
+            "model": str(best_model),
+            "metrics": metrics
+        })
+    
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+if __name__ == '__main__':
+    app.run(host='127.0.0.1', port=5001)