moved from app.app_config

core/config/config.py

@@ -0,0 +1,72 @@
+from dataclasses import dataclass
+import json
+import os
+from pathlib import Path
+from dotenv import load_dotenv
+
+@dataclass
+class InputConfig:
+    model_name: str = 'openai/gpt-3.5-turbo'
+    model_api_key: str = 'sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx'
+    max_tokens: int = 1024
+    max_steps: int = 10
+    temperature: float = 0.2
+    timeout: float = 30.0
+    sleeptime: float = 0.0
+    force_max_steps: bool = True
+
+    @classmethod
+    def load(cls, env_file, config_file):
+        # Load env variables
+        load_dotenv(env_file)
+        env_dict = {
+            'model_name': os.getenv('MODEL_NAME', 'not set'),
+            'model_api_key': os.getenv('MODEL_API_KEY', 'not set')
+        }
+
+        # Load config JSON
+        with open(config_file, 'r') as f:
+            config_dict = json.load(f)
+
+        # Combine both
+        return cls(
+            model_name=env_dict.get('model_name', cls.model_name),
+            model_api_key=env_dict.get('model_api_key', cls.model_api_key),
+            max_tokens=config_dict.get('max_tokens', cls.max_tokens),
+            max_steps=config_dict.get('max_steps', cls.max_steps),
+            temperature=config_dict.get('temperature', cls.temperature),
+            timeout=config_dict.get('timeout', cls.timeout),
+            sleeptime=config_dict.get('sleeptime', cls.sleeptime),
+            force_max_steps=config_dict.get('force_max_steps', cls.force_max_steps)
+        )
+
+    def save(self, env_file, config_file):
+        # Read existing env content if it exists
+        env_vars = {}
+        if os.path.exists(env_file):
+            with open(env_file, 'r') as f:
+                for line in f:
+                    if line.strip():  # Ignore empty lines
+                        key, value = line.strip().split('=', 1)
+                        env_vars[key] = value
+        
+        # Update the necessary keys
+        env_vars['MODEL_API_KEY'] = self.model_api_key
+        env_vars['MODEL_NAME'] = self.model_name
+
+        # Write back to the .env file
+        with open(env_file, 'w') as f:
+            for key, value in env_vars.items():
+                f.write(f'{key}={value}\n')
+
+        # Save other parameters to input_config.json
+        config_dict = {
+            'max_tokens': self.max_tokens,
+            'max_steps': self.max_steps,
+            'temperature': self.temperature,
+            'timeout': self.timeout,
+            'sleeptime': self.sleeptime,
+            'force_max_steps': self.force_max_steps
+        }
+        with open(config_file, 'w') as f:
+            json.dump(config_dict, f, indent=4)