[MRG] ask users to provide absolute path and test deepseek

mle/agents/planner.py

@@ -94,7 +94,7 @@ def plan(self, user_prompt):
                 self.chat_history,
                 response_format={"type": "json_object"}
             )
-
+            
             self.chat_history.append({"role": "assistant", "content": text})
 
         try:

mle/model/ollama.py

@@ -1,4 +1,5 @@
 import importlib.util
+import re
 
 from .common import Model
 
@@ -27,29 +28,64 @@ def __init__(self, model, host_url=None):
                 "More information, please refer to: https://github.com/ollama/ollama-python"
             )
 
+    def _clean_think_tags(self, text):
+        """
+        Remove content between <think> tags and empty think tags from the text.
+        Args:
+            text (str): The input text to clean.
+        Returns:
+            str: The cleaned text with think tags and their content removed.
+        """
+        # Remove content between <think> tags
+        text = re.sub(r'<think>.*?</think>', '', text, flags=re.DOTALL)
+        # Remove empty think tags
+        text = re.sub(r'<think></think>', '', text)
+        return text.strip()
+
+    def _process_message(self, message, **kwargs):
+        """
+        Process the message before sending to the model.
+        Args:
+            message: The message to process.
+            **kwargs: Additional arguments.
+        Returns:
+            dict: The processed message.
+        """
+        if isinstance(message, dict) and 'content' in message:
+            message['content'] = self._clean_think_tags(message['content'])
+        return message
+
     def query(self, chat_history, **kwargs):
         """
         Query the LLM model.
         Args:
             chat_history: The context (chat history).
+            **kwargs: Additional arguments for the model.
+        Returns:
+            str: The model's response.
         """
 
         # Check if 'response_format' exists in kwargs
         format = None
         if 'response_format' in kwargs and kwargs['response_format'].get('type') == 'json_object':
             format = 'json'
 
-        return self.client.chat(model=self.model, messages=chat_history, format=format)['message']['content']
+        response = self.client.chat(model=self.model, messages=chat_history, format=format)
+        return self._clean_think_tags(response['message']['content'])
 
     def stream(self, chat_history, **kwargs):
         """
         Stream the output from the LLM model.
         Args:
             chat_history: The context (chat history).
+            **kwargs: Additional arguments for the model.
+        Yields:
+            str: Chunks of the model's response.
         """
+
         for chunk in self.client.chat(
                 model=self.model,
                 messages=chat_history,
                 stream=True
         ):
-            yield chunk['message']['content']
+            yield self._clean_think_tags(chunk['message']['content'])

mle/workflow/baseline.py

@@ -46,7 +46,7 @@ def baseline(work_dir: str, model=None):
         dataset = ca.resume("dataset")
         if dataset is None:
             advisor = AdviseAgent(model, console)
-            dataset = ask_text("Please provide your dataset information (a public dataset name or a local file path)")
+            dataset = ask_text("Please provide your dataset information (a public dataset name or a local absolute filepath)")
             if not dataset:
                 print_in_box("The dataset is empty. Aborted", console, title="Error", color="red")
                 return