Update app.py

app.py

@@ -69,61 +69,77 @@ demo = gr.ChatInterface(
 
 demo.launch()
 """
+
 import gradio as gr
-import csv, time
+import json, time, os
+from huggingface_hub import HfApi
+
+DATASET_ID = "dn/flags"        # your dataset
+SECRET_TOKEN = os.getenv("HF_TOKEN")   # token stored in your Space secrets
+LOCAL_JSONL = "flags.jsonl"   # local buffer file before upload
 
 
 # -------------------------------------------------
-# Your chatbot model
+# Example Chat Model
 # -------------------------------------------------
 def chat_model(message, history):
-    # Example model: reverse text
-    return message[::-1]
+    return message[::-1]  # dummy: reverse text
 
 
 # -------------------------------------------------
-# Save flagged conversation
+# Save flags to Hugging Face Dataset
 # -------------------------------------------------
-def flag_conversation(history, reason):
-    # Save the entire chat + reason
-    with open("chat_flags.csv", "a", newline="") as f:
-        writer = csv.writer(f)
-        writer.writerow([
-            time.time(),
-            history,   # full conversation as a Python list
-            reason
-        ])
-    return "Flag recorded!"
+def save_flag_to_dataset(history, reason):
+    # Prepare flag record
+    record = {
+        "timestamp": time.time(),
+        "history": history,
+        "reason": reason
+    }
+
+    # Append locally to JSONL
+    with open(LOCAL_JSONL, "a") as f:
+        f.write(json.dumps(record) + "\n")
+
+    # Upload to the dataset (overwrite the JSONL with the new one)
+    api = HfApi()
+    api.upload_file(
+        path_or_fileobj=LOCAL_JSONL,
+        path_in_repo=LOCAL_JSONL,   # same name in the dataset
+        repo_id=DATASET_ID,
+        repo_type="dataset",
+        token=SECRET_TOKEN
+    )
+
+    return "Flag successfully saved to dataset!"
 
 
 # -------------------------------------------------
 # UI
 # -------------------------------------------------
 with gr.Blocks() as app:
+    gr.Markdown("### Chatbot with Multi-Reason Flagging → HF Dataset")
 
-    gr.Markdown("### Chatbot with Multi-Reason Flagging")
-
-    # Create chat interface
+    # Chat UI
     chat = gr.ChatInterface(
         fn=chat_model,
         chatbot=gr.Chatbot(height=400),
         textbox=gr.Textbox(placeholder="Say something..."),
-        title="Demo Chatbot",
-        description="Supports multi-reason flagging."
+        title="Chatbot",
+        description="Flags saved to the dn/flags dataset."
     )
 
-    # Flag button
+    # Flagging UI components
     flag_btn = gr.Button("Flag Conversation", variant="stop")
 
-    # Modal for selecting flag reason
     with gr.Group(visible=False) as flag_modal:
         gr.Markdown("### Why are you flagging this conversation?")
         reason_dd = gr.Dropdown(
             choices=[
                 "Offensive / Toxic",
-                "Incorrect",
+                "Incorrect Output",
                 "Hallucination",
-                "Safety Issue",
+                "Safety Concern",
                 "Biased Output",
                 "Other"
             ],
@@ -132,21 +148,22 @@ with gr.Blocks() as app:
         submit_flag = gr.Button("Submit Flag", variant="primary")
         cancel_flag = gr.Button("Cancel")
 
+    flag_status = gr.Textbox(label="Flag Status", visible=True)
 
     # Show modal
     flag_btn.click(lambda: gr.update(visible=True), None, flag_modal)
 
-    # Cancel hides modal
+    # Hide modal
     cancel_flag.click(lambda: gr.update(visible=False), None, flag_modal)
 
-    # Submit flag → save CSV → hide modal → show status
-    flag_status = gr.Textbox(label="Flagging Status", visible=True)
-
+    # Submit → save to dataset → hide modal
     submit_flag.click(
-        lambda chat_history, reason: flag_conversation(chat_history, reason),
+        lambda history, reason: save_flag_to_dataset(history, reason),
         inputs=[chat.chatbot, reason_dd],
         outputs=flag_status
-    ).then(lambda: gr.update(visible=False), None, flag_modal)
+    ).then(
+        lambda: gr.update(visible=False), None, flag_modal
+    )
 
 
 app.launch()