Update app.py

app.py

@@ -3,6 +3,7 @@ import os
 import openai
 import gradio as gr
 import json
+import csv
 
 # Set the OpenAI API key
 openai.api_key = os.getenv("OPENAI_API_KEY")
@@ -48,6 +49,21 @@ def CustomChatGPT(category, user_input):
     # Return the model's message
     return ChatGPT_reply
 
+# Function to save flagged data to a Hugging Face Dataset using a token
+class HuggingFaceDatasetSaver:
+    def __init__(self, dataset_name, huggingface_token):
+        self.dataset_name = dataset_name
+        self.huggingface_token = huggingface_token
+
+    def save_flagged_data(self, data):
+        from datasets import Dataset, FileSystemDataset
+
+        dataset = Dataset.from_dict(data)
+
+        # Write the dataset to Hugging Face using the provided token
+        fs_dataset = FileSystemDataset(self.dataset_name, data_files={self.dataset_name: dataset}, token=self.huggingface_token)
+        fs_dataset.save_to_disk()
+
 # Define the Gradio interface
 iface = gr.Interface(
     fn=CustomChatGPT, 
@@ -133,9 +149,9 @@ iface = gr.Interface(
             on growing your business!""",  # Article text
     thumbnail="https://assets.bigcartel.com/theme_images/101321509/IMG_6002.png",  # Thumbnail image URL
     favicon_path="https://assets.bigcartel.com/theme_images/101321509/IMG_6002.png",  # Favicon image URL
-    allow_flagging="manual",
-    flagging_callback=hf_writer
-   
+    allow_flagging="manual",  # Enable flagging with manual control
+    flagging_options=["Incorrect", "Ambiguous"],  # Specify flagging options
+    flagging_callback=HuggingFaceDatasetSaver("CannaTech/Flagged", os.getenv("HF_TOKEN")).save_flagged_data  # Save flagged data to Hugging Face Dataset using token
 )
 
 # Launch the interface with authentication