Create runner.py

runner.py

@@ -0,0 +1,62 @@
+import os
+from openai import OpenAI
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+# Initialize OpenAI client with API key from .env
+openai_client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))
+
+def run_gpt4o_mini(question):
+    try:
+        # Check if the question is a dictionary and extract the prompt
+        if isinstance(question, dict) and 'prompt' in question:
+            question_text = question['prompt']
+        else:
+            question_text = str(question)
+
+        response = openai_client.chat.completions.create(
+            model="gpt-4o-mini",
+            messages=[
+                {"role": "system", "content": "You are a helpful assistant. Answer the question to the best of your ability."},
+                {"role": "user", "content": question_text}
+            ]
+        )
+        return response.choices[0].message.content
+    except Exception as e:
+        print(f"Error running GPT-4o-mini: {str(e)}")
+        return None
+
+def run_gpt4o(question):
+    try:
+        # Check if the question is a dictionary and extract the prompt
+        if isinstance(question, dict) and 'prompt' in question:
+            question_text = question['prompt']
+        else:
+            question_text = str(question)
+
+        response = openai_client.chat.completions.create(
+            model="gpt-4o-mini",
+            messages=[
+                {"role": "system", "content": "You are a helpful assistant. Answer the question to the best of your ability."},
+                {"role": "user", "content": question_text}
+            ]
+        )
+        return response.choices[0].message.content
+    except Exception as e:
+        print(f"Error running GPT-4o-mini: {str(e)}")
+        return None
+
+def run_custom_model(model_name, question):
+    # Placeholder for custom model logic
+    # You'll need to implement this based on how your custom models work
+    return f"Custom model {model_name} response: This is a placeholder answer for the question provided."
+
+def run_model(model_name, question):
+    if model_name == "gpt-4o-mini":
+        return run_gpt4o_mini(question)
+    elif model_name == "gpt-4o":
+        return run_gpt4o(question)
+    else:
+        return run_custom_model(model_name, question)