update dependencies, cache_examples=False in gr.ChatInterface

app.py

@@ -3,7 +3,8 @@ import gradio as gr
 from huggingface_hub import get_token
 
 from chatbot import get_retrieval_qa
-from flagging import myHuggingFaceDatasetSaver
+from flagging import myHuggingFaceDatasetSaver as HuggingFaceDatasetSaver
+#from gradio.flagging import HuggingFaceDatasetSaver
 
 
 # get the html data and save it to a file
@@ -22,20 +23,18 @@ qa = get_retrieval_qa(filename)
 
 # dataset callback
 dataset_name = "SEA-AI/seadog-chat-history"
-hf_writer = myHuggingFaceDatasetSaver(get_token(), dataset_name)
+hf_writer = HuggingFaceDatasetSaver(get_token(), dataset_name)
 
 
 def answer_question(message, history, system):
     # concatenate the history, message and system
     query = " ".join([message, system])
     retrieval_qa = qa.invoke(query)
-    result = retrieval_qa["result"]
+    result = retrieval_qa["result"]  # "query" and "source_documents" are also available
     result = result.replace('"', "").strip()  # clean up the result
-    # query = retrieval_qa["query"]
-    # source_documents = retrieval_qa["source_documents"]
 
     # save the query and result to the dataset
-    hf_writer.flag([query, result])
+    hf_writer.flag(flag_data=[query, result])
     return result
 
 
@@ -68,6 +67,7 @@ chatbot = gr.Chatbot(
             "I have memorized the entire SEA.AI FAQ page. Ask me anything about it! 🧠",
         ],
     ],
+    label="SEA Dog",
     show_label=False,
     show_copy_button=True,
     likeable=True,
@@ -88,6 +88,7 @@ with gr.ChatInterface(
         ["Can SEA.AI see at night?", "You are a helpful assistant."],
         ["Can SEA.AI see at night?", "Reply with sailor slang."],
     ],
+    cache_examples=False,
     submit_btn=None,
     retry_btn=None,
     undo_btn=None,

flagging.py

@@ -3,6 +3,7 @@ from pathlib import Path
 from typing import Any
 import gradio as gr
 from gradio.flagging import HuggingFaceDatasetSaver, client_utils
+from gradio import utils
 import huggingface_hub
 
 
@@ -40,13 +41,17 @@ class myHuggingFaceDatasetSaver(HuggingFaceDatasetSaver):
             if isinstance(component, gr.Chatbot):
                 deserialized = sample  # dirty fix
             else:
-                deserialized = component.flag(sample, save_dir)
+                deserialized = utils.simplify_file_data_in_str(
+                    component.flag(sample, save_dir)
+                )                
 
             # Add deserialized object to row
             features[label] = {"dtype": "string", "_type": "Value"}
             try:
-                assert Path(deserialized).exists()
-                row.append(str(Path(deserialized).relative_to(self.dataset_dir)))
+                deserialized_path = Path(deserialized)
+                if not deserialized_path.exists():
+                    raise FileNotFoundError(f"File {deserialized} not found")
+                row.append(str(deserialized_path.relative_to(self.dataset_dir)))
             except (AssertionError, TypeError, ValueError, OSError):
                 deserialized = "" if deserialized is None else str(deserialized)
                 row.append(deserialized)