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import os
from typing import Union, List, Dict, Optional, Any
from huggingface_hub import InferenceClient
from transformers import AutoTokenizer
class Message:
"""Simple message class to mimic OpenAI's message format"""
def __init__(self, content):
self.content = content
self.model = ""
self.created = 0
self.choices = []
class HfApiModel:
"""HuggingFace API Model interface for smolagents CodeAgent"""
def __init__(self,
model_id='Qwen/Qwen2.5-Coder-32B-Instruct',
max_tokens=2096,
temperature=0.5,
custom_role_conversions=None):
"""Initialize the HuggingFace API Model.
Args:
model_id: The model ID on Hugging Face Hub
max_tokens: Maximum number of tokens to generate
temperature: Sampling temperature (0.0 to 1.0)
custom_role_conversions: Custom role mappings if needed
"""
self.model_id = model_id
self.max_tokens = max_tokens
self.temperature = temperature
self.custom_role_conversions = custom_role_conversions or {}
# Initialize the client
self.client = InferenceClient(model=model_id, token=os.environ.get("HF_TOKEN"))
# Try to load tokenizer for token counting (optional)
try:
self.tokenizer = AutoTokenizer.from_pretrained(model_id)
except:
self.tokenizer = None
print(f"Warning: Could not load tokenizer for {model_id}")
def __call__(self, prompt: Union[str, dict, List[Dict]]) -> Message:
"""Make the class callable as required by smolagents"""
try:
# Handle different prompt formats
if isinstance(prompt, (dict, list)):
# Format as chat if it's a list of messages
if isinstance(prompt, list) and all(isinstance(msg, dict) for msg in prompt):
messages = self._format_messages(prompt)
return self._generate_chat_response_message(messages)
else:
# Convert to string if it's not a well-formed chat message list
prompt_str = str(prompt)
return self._generate_text_response_message(prompt_str)
else:
# String prompt
prompt_str = str(prompt)
return self._generate_text_response_message(prompt_str)
except Exception as e:
error_msg = f"Error generating response: {str(e)}"
print(error_msg)
return Message(error_msg)
def generate(self,
prompt: Union[str, dict, List[Dict]],
stop_sequences: Optional[List[str]] = None,
seed: Optional[int] = None,
max_tokens: Optional[int] = None,
temperature: Optional[float] = None,
**kwargs) -> Message:
"""
Generate a response from the model.
This method is required by smolagents and provides a more complete interface
with support for all parameters needed by smolagents.
Args:
prompt: The prompt to send to the model.
Can be a string, dict, or list of message dicts
stop_sequences: List of sequences where the model should stop generating
seed: Random seed for reproducibility
max_tokens: Maximum tokens to generate (overrides instance value if provided)
temperature: Sampling temperature (overrides instance value if provided)
**kwargs: Additional parameters that might be needed in the future
Returns:
Message: A Message object with the response content
"""
# Apply override parameters if provided
if max_tokens is not None:
old_max_tokens = self.max_tokens
self.max_tokens = max_tokens
if temperature is not None:
old_temperature = self.temperature
self.temperature = temperature
try:
# Handle different prompt formats
if isinstance(prompt, (dict, list)):
# Format as chat if it's a list of messages
if isinstance(prompt, list) and all(isinstance(msg, dict) for msg in prompt):
messages = self._format_messages(prompt)
result = self._generate_chat_response_message(messages, stop_sequences)
return result
else:
# Convert to string if it's not a well-formed chat message list
prompt_str = str(prompt)
result = self._generate_text_response_message(prompt_str, stop_sequences)
return result
else:
# String prompt
prompt_str = str(prompt)
result = self._generate_text_response_message(prompt_str, stop_sequences)
return result
except Exception as e:
error_msg = f"Error generating response: {str(e)}"
print(error_msg)
return Message(error_msg)
finally:
# Restore original parameters if they were overridden
if max_tokens is not None:
self.max_tokens = old_max_tokens
if temperature is not None:
self.temperature = old_temperature
def _format_messages(self, messages: List[Dict]) -> List[Dict]:
"""Format messages for the chat API"""
formatted_messages = []
for msg in messages:
role = msg.get("role", "user")
content = msg.get("content", "")
# Map custom roles if needed
if role in self.custom_role_conversions:
role = self.custom_role_conversions[role]
formatted_messages.append({"role": role, "content": content})
return formatted_messages
def _generate_chat_response(self, messages: List[Dict], stop_sequences: Optional[List[str]] = None) -> str:
"""Generate a response from the chat API and return string content"""
# Prepare parameters
params = {
"messages": messages,
"max_tokens": self.max_tokens,
"temperature": self.temperature,
}
# Add stop sequences if provided
if stop_sequences:
# Note: Some HF models may not support the stop_sequences parameter
# We'll try without it if it fails
try:
params["stop_sequences"] = stop_sequences
response = self.client.chat_completion(**params)
content = response.choices[0].message.content
except:
# Try again without stop_sequences
del params["stop_sequences"]
print("Warning: stop_sequences parameter not supported, continuing without it")
response = self.client.chat_completion(**params)
content = response.choices[0].message.content
else:
# Call the API
response = self.client.chat_completion(**params)
content = response.choices[0].message.content
# Check if this is for smolagents by examining if the user message has certain key words
is_smolagents_format = False
for msg in messages:
if msg.get("role") == "system" and isinstance(msg.get("content"), str):
system_content = msg.get("content", "")
if "Thought:" in system_content and "Code:" in system_content and "<end_code>" in system_content:
is_smolagents_format = True
break
# If using with smolagents, format response properly if it doesn't already have the right format
if is_smolagents_format and not ("Thought:" in content and "Code:" in content and "<end_code>" in content):
# Typical instruction extraction to create a better smolagents-compatible response
user_message = ""
for msg in messages:
if msg.get("role") == "user":
user_message = msg.get("content", "")
break
# Extract mission type based on user message
mission_type = "custom"
duration = 15
if "survey" in user_message.lower():
mission_type = "survey"
duration = 20
elif "inspect" in user_message.lower():
mission_type = "inspection"
duration = 15
elif "delivery" in user_message.lower():
mission_type = "delivery"
duration = 10
elif "square" in user_message.lower():
mission_type = "survey"
duration = 10
# Format properly for smolagents
formatted_content = f"""Thought: I will create a {mission_type} mission plan for {duration} minutes and execute it on the simulator.
Code:
```py
mission_plan = generate_mission_plan(mission_type="{mission_type}", duration_minutes={duration})
print(f"Generated mission plan: {{mission_plan}}")
final_answer(f"I've created a {mission_type} mission plan that will take approximately {duration} minutes to execute. The plan includes waypoints for a square pattern around your current position.")
```<end_code>"""
return formatted_content
return content
def _generate_chat_response_message(self, messages: List[Dict], stop_sequences: Optional[List[str]] = None) -> Message:
"""Generate a response from the chat API and return a Message object"""
content = self._generate_chat_response(messages, stop_sequences)
return Message(content)
def _generate_text_response(self, prompt: str, stop_sequences: Optional[List[str]] = None) -> str:
"""Generate a response from the text completion API and return string content"""
# For models that don't support the chat format, we can use text generation
# But Qwen2.5 supports chat, so we'll convert to chat format
messages = [{"role": "user", "content": prompt}]
return self._generate_chat_response(messages, stop_sequences)
def _generate_text_response_message(self, prompt: str, stop_sequences: Optional[List[str]] = None) -> Message:
"""Generate a response from the text completion API and return a Message object"""
content = self._generate_text_response(prompt, stop_sequences)
return Message(content) |