Update app.py

app.py

@@ -15,21 +15,48 @@ def clear_chat(state):
     return state, None,None,gr.Button.update(interactive=False),gr.Button.update(interactive=False)
 
 
-# Initialize or get user-specific ELO ratings
-def get_user_elo_ratings(state):
-    return state['elo_ratings']
-
-# Read and write ELO ratings to file (thread-safe)
-def read_elo_ratings():
-    try:
-        with open('elo_ratings.json', 'r') as file:
-            return json.load(file)
-    except FileNotFoundError:
-        return {model: 1200 for model in chatbots.keys()}
-
-def write_elo_ratings(elo_ratings):
-    with open('elo_ratings.json', 'w') as file:
-        json.dump(elo_ratings, file, indent=4)
+
+
+
+from datasets import load_dataset
+import requests
+
+HUGGINGFACE_DATASET_URL = "https://huggingface.co/datasets/your_username/your_dataset_name"
+API_TOKEN = "your_huggingface_api_token"  # Keep this secure
+
+def get_user_elo_ratings():
+    dataset = load_dataset("rwitz/mistral-elo-ratings", streaming=True)
+    elo_ratings = dataset['train']  # or the relevant split
+
+    # Check and assign default ELO rating
+    default_elo = 1200
+    for bot in list(chatbots.keys()):
+        if bot not in elo_ratings:
+            elo_ratings[bot] = default_elo
+
+    return elo_ratings
+
+def update_elo_rating(player_id, new_rating):
+    # Fetch the current dataset
+    elo_ratings = get_user_elo_ratings()
+
+    # Update the rating
+    elo_ratings = elo_ratings.map(lambda x: {'rating': new_rating})
+
+    # Push the updated dataset back to Hugging Face
+    # This part is more complex as it involves using Hugging Face's dataset push API.
+    # You'll need to use the `requests` library to make a POST request to Hugging Face's API.
+    # This is a simplified example:
+    updated_data = elo_ratings.to_dict()
+    response = requests.post(
+        "https://huggingface.co/datasets/rwitz/mistral-elo-ratings",
+        headers={"Authorization": f"Bearer {os.environ.get("huggingface_token")}"},
+        json=updated_data
+    )
+    if response.status_code == 200:
+        print("Successfully updated the dataset")
+    else:
+        print("Failed to update the dataset")
 
 # Function to get bot response
 def format_alpaca_prompt(state):
@@ -84,13 +111,13 @@ def chat_with_bots(user_input, state):
     return bot1_response, bot2_response
 
 def update_ratings(state, winner_index):
-    elo_ratings = get_user_elo_ratings(state)
+    elo_ratings = get_user_elo_ratings()
     bot_names = list(chatbots.keys())
     winner = state['last_bots'][winner_index]
     loser = state['last_bots'][1 - winner_index]
     
     elo_ratings = update_elo_ratings(elo_ratings, winner, loser)
-    write_elo_ratings(elo_ratings)
+    update_elo_rating(elo_ratings)
     return [('Winner: ',state['last_bots'][winner_index]),('Loser: ',state['last_bots'][1 - winner_index])]
 
 def vote_up_model(state, chatbot,chatbot2):
@@ -111,7 +138,7 @@ def user_ask(state, chatbot1, chatbot2, textbox):
         user_input = user_input[:200]  # Limit user input to 200 characters
 
     # Updating state with the current ELO ratings
-    state["elo_ratings"] = read_elo_ratings()
+    state["elo_ratings"] = get_user_elo_ratings()
     if "history" not in state:
         state.update({'history': [[],[]]})
     state["history"][0].extend([
@@ -140,7 +167,7 @@ import pandas as pd
 
 # Function to generate leaderboard data
 def generate_leaderboard():
-    elo_ratings = read_elo_ratings()  # Assuming this function returns a dict of {bot_name: elo_score}
+    elo_ratings = get_user_elo_ratings()  # Assuming this function returns a dict of {bot_name: elo_score}
     leaderboard_data = pd.DataFrame(list(elo_ratings.items()), columns=['Chatbot', 'ELO Score'])
     leaderboard_data = leaderboard_data.sort_values('ELO Score', ascending=False)
     return leaderboard_data