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import torch
from transformers import pipeline
class DummyLLM:
"""A simple dummy LLM for testing."""
def __init__(self):
"""Initialize the dummy LLM."""
pass
def complete(self, prompt):
"""Complete a prompt with a simple response."""
class Response:
def __init__(self, text):
self.text = text
# For testing only - return a simple response
return Response("This is a placeholder response. The actual model is not loaded to save resources.")
def setup_llm():
"""Set up a simple LLM for testing."""
try:
# Try to load a very small model for text generation
generator = pipeline(
"text-generation",
model="distilgpt2", # A very small model
max_length=100
)
# Create a wrapper class that matches the expected interface
class SimpleTransformersLLM:
def complete(self, prompt):
class Response:
def __init__(self, text):
self.text = text
try:
result = generator(prompt, max_length=len(prompt) + 50, do_sample=True)[0]
generated_text = result["generated_text"]
response_text = generated_text[len(prompt):].strip()
if not response_text:
response_text = "I couldn't generate a proper response."
return Response(response_text)
except Exception as e:
print(f"Error generating response: {e}")
return Response("Error generating response.")
return SimpleTransformersLLM()
except Exception as e:
print(f"Error setting up model: {e}")
return DummyLLM()
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