Fix bug of ppt and googlescholar

.gitignore

@@ -160,3 +160,4 @@ cython_debug/
 #.idea/
 .vscode/
 docs/*/_build/
+tmp_dir/

examples/internlm2_agent_web_demo.py

@@ -142,7 +142,7 @@ def initialize_chatbot(self, model, plugin_action):
                     belong='assistant',
                     end='<|action_end|>\n',
                 ), ),
-        )
+            max_turn=7)
 
     def render_user(self, prompt: str):
         with st.chat_message('user'):

lagent/actions/ppt.py

@@ -8,7 +8,7 @@
         'template': None,
         'title': 'Title Slide',
         'single': 'Title and Content',
-        'two': 'Tow content',
+        'two': 'Two Content',
     }
 }
 
@@ -31,7 +31,7 @@ def create_file(self, theme: str, abs_location: str) -> dict:
         """Create a pptx file with specific themes.
 
         Args:
-            theme (:class:`str`): the theme used
+            theme (:class:`str`): the theme used. The value should be one of ['Default'].
             abs_location (:class:`str`): the ppt file's absolute location
 
         Returns:
@@ -115,13 +115,15 @@ def add_text_image_page(self, title: str, bullet_items: str,
             :class:`dict`: operation status
                 * status: the result of the execution
         """
+        from PIL import Image
         layout_name = self.theme_mapping[self.pointer.slide_master.name]['two']
         layout = next(i for i in self.pointer.slide_master.slide_layouts
                       if i.name == layout_name)
         slide = self.pointer.slides.add_slide(layout)
         ph_title, ph_body1, ph_body2 = slide.placeholders
         ph_title.text = title
         ph = ph_body2
+        image = Image.open(image)
         image_pil = image.to_pil()
         left = ph.left
         width = ph.width

lagent/agents/internlm2_agent.py

@@ -141,6 +141,12 @@ def format(self,
                     tool_name = api_info['name'].split('.')[0]
                     plugin['description'] = API_PREFIX.format(
                         tool_name=tool_name, description=plugin['description'])
+                    # only keep required parameters
+                    required_parameters = [
+                        param for param in plugin['parameters']
+                        if param['name'] in plugin['required']
+                    ]
+                    plugin['parameters'] = required_parameters
                 plugin_descriptions.append(plugin)
             plugin_prompt = self.plugin_prompt.format(
                 prompt=json.dumps(

lagent/llms/huggingface.py

@@ -50,8 +50,8 @@ def __init__(self,
         self.gen_params.update(stop_words_id=stop_words_id)
         if self.gen_params['stop_words'] is not None and \
                 self.gen_params['stop_words_id'] is not None:
-            logger.warning("Both stop_words and stop_words_id are specified,"
-                           "only stop_words_id will be used.")
+            logger.warning('Both stop_words and stop_words_id are specified,'
+                           'only stop_words_id will be used.')
 
         self._load_tokenizer(
             path=path,
@@ -80,7 +80,7 @@ def _load_tokenizer(self, path: str, tokenizer_path: Optional[str],
             tokenizer_path if tokenizer_path else path,
             trust_remote_code=True,
             **tokenizer_kwargs)
-            
+
         if self.tokenizer.pad_token_id is None:
             if self.tokenizer.eos_token is not None:
                 logger.warning(
@@ -101,7 +101,7 @@ def _load_tokenizer(self, path: str, tokenizer_path: Optional[str],
                         'pad_token_id is not set for this tokenizer. Try to '
                         'set pad_token_id via passing '
                         '`pad_token_id={PAD_TOKEN_ID}` in model_cfg.')
-            
+
     def _load_model(self, path: str, model_kwargs: dict):
         import torch
         from transformers import AutoModel
@@ -302,13 +302,16 @@ def _load_model(self, path: str, model_kwargs: dict):
             path, trust_remote_code=True, **model_kwargs)
         self.model.eval()
 
+
 class HFTransformerChat(HFTransformerCasualLM):
-    def __init__(self, 
-                 template_parser=APITemplateParser,
-                 **kwargs):
+
+    def __init__(self, template_parser=APITemplateParser, **kwargs):
         super().__init__(template_parser=template_parser, **kwargs)
 
-    def chat(self, inputs: Union[List[dict], List[List[dict]]], do_sample: bool = True, **kwargs):
+    def chat(self,
+             inputs: Union[List[dict], List[List[dict]]],
+             do_sample: bool = True,
+             **kwargs):
         """Return the chat completions in stream mode.
 
         Args:
@@ -327,12 +330,10 @@ def chat(self, inputs: Union[List[dict], List[List[dict]]], do_sample: bool = Tr
         query = prompt[-1]['content']
         history = prompt[:-1]
         try:
-            response, history = self.model.chat(self.tokenizer,
-                                                    query,
-                                                    history=history)
+            response, history = self.model.chat(
+                self.tokenizer, query, history=history)
         except Exception as e:
             # handle over-length input error
             logger.warning(str(e))
-            response = ""
+            response = ''
         return response
-