Update src/streamlit_app.py

src/streamlit_app.py

@@ -2,6 +2,41 @@ import altair as alt
 import numpy as np
 import pandas as pd
 import streamlit as st
+#==
+import uuid
+from pathlib import Path
+from huggingface_hub import CommitScheduler
+import json
+#==
+
+
+#===
+# Define the file where to save the data. Use UUID to make sure not to overwrite existing data from a previous run.
+feedback_file = Path("data_pref/") / f"data_{uuid.uuid4()}.json"
+feedback_folder = feedback_file.parent
+
+# Schedule regular uploads. Remote repo and local folder are created if they don't already exist.
+scheduler = CommitScheduler(
+    repo_id="data_pref",
+    repo_type="dataset",
+    folder_path=feedback_folder,
+    path_in_repo="data",
+    every=10,
+)
+
+# Define the function that will be called when the user submits its feedback (to be called in Gradio)
+#def save_feedback(input_text:str, output_1: str, output_2:str, user_choice: int) -> None:
+#    """
+#    Append input/outputs and user feedback to a JSON Lines file using a thread lock to avoid concurrent writes from different users.
+#    """
+#    with scheduler.lock:
+#        with feedback_file.open("a") as f:
+#            f.write(json.dumps({"input": input_text, "output_1": output_1, "output_2": output_2, "user_choice": user_choice}))
+#            f.write("\n")
+#====
+
+
+
 
 # ---- CONFIG ----
 st.set_page_config(page_title="Image Preference Study", layout="centered")
@@ -52,6 +87,15 @@ if st.button("Submit"):
     
     # st.json(results)
 
+    #====
+    with scheduler.lock:
+        with feedback_file.open("a") as f:
+            f.write(json.dumps(results))
+            f.write("\n")
+    #====
+
+    
     # Save to file (you could also save to a database)
-    with open("resp/responses.txt", "a") as f:
-        f.write(str(results) + "\n")
+    #with open("resp/responses.txt", "a") as f:
+    #    f.write(str(results) + "\n")
+